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800 | 800 | ['Kamil Rocki'] | 1603.08262v1 | There exists a theory of a single general-purpose learning algorithm which
could explain the principles of its operation. This theory assumes that the
brain has some initial rough architecture, a small library of simple innate
circuits which are prewired at birth and proposes that all significant mental
algorithms can be learned. Given current understanding and observations, this
paper reviews and lists the ingredients of such an algorithm from both
architectural and functional perspectives. | Towards Machine Intelligence | 2,016 | http://arxiv.org/pdf/1603.08262v1 | Title Towards Machine Intelligence Summary exists theory single generalpurpose learning algorithm could explain principle operation theory assumes brain initial rough architecture small library simple innate circuit prewired birth proposes significant mental algorithm learned Given current understanding observation paper review list ingredient algorithm architectural functional perspective Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.017351990565657616, 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801 | 801 | ['Aravind S. Lakshminarayanan', 'Sahil Sharma', 'Balaraman Ravindran'] | 1605.05365v2 | Deep Reinforcement Learning methods have achieved state of the art
performance in learning control policies for the games in the Atari 2600
domain. One of the important parameters in the Arcade Learning Environment
(ALE) is the frame skip rate. It decides the granularity at which agents can
control game play. A frame skip value of $k$ allows the agent to repeat a
selected action $k$ number of times. The current state of the art architectures
like Deep Q-Network (DQN) and Dueling Network Architectures (DuDQN) consist of
a framework with a static frame skip rate, where the action output from the
network is repeated for a fixed number of frames regardless of the current
state. In this paper, we propose a new architecture, Dynamic Frame skip Deep
Q-Network (DFDQN) which makes the frame skip rate a dynamic learnable
parameter. This allows us to choose the number of times an action is to be
repeated based on the current state. We show empirically that such a setting
improves the performance on relatively harder games like Seaquest. | Dynamic Frame skip Deep Q Network | 2,016 | http://arxiv.org/pdf/1605.05365v2 | Title Dynamic Frame skip Deep Q Network Summary Deep Reinforcement Learning method achieved state art performance learning control policy game Atari 2600 domain One important parameter Arcade Learning Environment ALE frame skip rate decides granularity agent control game play frame skip value k allows agent repeat selected action k number time current state art architecture like Deep QNetwork DQN Dueling Network Architectures DuDQN consist framework static frame skip rate action output network repeated fixed number frame regardless current state paper propose new architecture Dynamic Frame skip Deep QNetwork DFDQN make frame skip rate dynamic learnable parameter allows u choose number time action repeated based current state show empirically setting improves performance relatively harder game like Seaquest Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.020967982709407806, 0.025493472814559937, -0.03489236533641815, -0.02994390018284321, 0.0019058444304391742, 0.0023948538582772017, 0.02895328775048256, -0.0014554777881130576, -0.05222182348370552, 0.022573666647076607, -0.02657044120132923, 0.008481152355670929, -0.027061982080340385, 0.10703840106725693, 0.011274815537035465, -0.0683189183473587, 0.04366001859307289, 0.023985907435417175, 0.021945344284176826, 0.004986822139471769, -0.010456452146172523, -0.07560952752828598, -0.018783751875162125, 0.016804439947009087, 0.02581578865647316, 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802 | 802 | ['Matko Bošnjak', 'Tim Rocktäschel', 'Jason Naradowsky', 'Sebastian Riedel'] | 1605.06640v3 | Given that in practice training data is scarce for all but a small set of
problems, a core question is how to incorporate prior knowledge into a model.
In this paper, we consider the case of prior procedural knowledge for neural
networks, such as knowing how a program should traverse a sequence, but not
what local actions should be performed at each step. To this end, we present an
end-to-end differentiable interpreter for the programming language Forth which
enables programmers to write program sketches with slots that can be filled
with behaviour trained from program input-output data. We can optimise this
behaviour directly through gradient descent techniques on user-specified
objectives, and also integrate the program into any larger neural computation
graph. We show empirically that our interpreter is able to effectively leverage
different levels of prior program structure and learn complex behaviours such
as sequence sorting and addition. When connected to outputs of an LSTM and
trained jointly, our interpreter achieves state-of-the-art accuracy for
end-to-end reasoning about quantities expressed in natural language stories. | Programming with a Differentiable Forth Interpreter | 2,016 | http://arxiv.org/pdf/1605.06640v3 | Title Programming Differentiable Forth Interpreter Summary Given practice training data scarce small set problem core question incorporate prior knowledge model paper consider case prior procedural knowledge neural network knowing program traverse sequence local action performed step end present endtoend differentiable interpreter programming language Forth enables programmer write program sketch slot filled behaviour trained program inputoutput data optimise behaviour directly gradient descent technique userspecified objective also integrate program larger neural computation graph show empirically interpreter able effectively leverage different level prior program structure learn complex behaviour sequence sorting addition connected output LSTM trained jointly interpreter achieves stateoftheart accuracy endtoend reasoning quantity expressed natural language story Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.015784751623868942, 0.02968372032046318, -0.02548852376639843, 0.03009185940027237, -0.0074228448793292046, 0.014865677803754807, -0.004632242955267429, -0.026738736778497696, -0.010036690160632133, -0.0015159444883465767, -0.00042609203956089914, 0.030986011028289795, 0.02082044444978237, 0.09317515045404434, 0.0025680747348815203, -0.049094974994659424, 0.013097230345010757, -0.031733956187963486, 0.01755557581782341, 0.02819732390344143, 0.0006235711043700576, 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803 | 803 | ['Luka Crnkovic-Friis', 'Louise Crnkovic-Friis'] | 1605.06921v1 | Recent advances in deep learning have enabled the extraction of high-level
features from raw sensor data which has opened up new possibilities in many
different fields, including computer generated choreography. In this paper we
present a system chor-rnn for generating novel choreographic material in the
nuanced choreographic language and style of an individual choreographer. It
also shows promising results in producing a higher level compositional
cohesion, rather than just generating sequences of movement. At the core of
chor-rnn is a deep recurrent neural network trained on raw motion capture data
and that can generate new dance sequences for a solo dancer. Chor-rnn can be
used for collaborative human-machine choreography or as a creative catalyst,
serving as inspiration for a choreographer. | Generative Choreography using Deep Learning | 2,016 | http://arxiv.org/pdf/1605.06921v1 | Title Generative Choreography using Deep Learning Summary Recent advance deep learning enabled extraction highlevel feature raw sensor data opened new possibility many different field including computer generated choreography paper present system chorrnn generating novel choreographic material nuanced choreographic language style individual choreographer also show promising result producing higher level compositional cohesion rather generating sequence movement core chorrnn deep recurrent neural network trained raw motion capture data generate new dance sequence solo dancer Chorrnn used collaborative humanmachine choreography creative catalyst serving inspiration choreographer Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.014845410361886024, 0.01873498037457466, -0.0018858957337215543, 0.01969604566693306, -0.0060719698667526245, -0.0038841378409415483, 0.010183590464293957, 0.007069035898894072, -0.02876858413219452, 0.01212883461266756, -0.04341796785593033, -0.028224367648363113, 0.0031514095608145, 0.08033016324043274, 0.03648742660880089, -0.044576775282621384, -0.03642628714442253, -0.00034768760087899864, -0.028185127303004265, 0.008522789925336838, -0.029461605474352837, -0.023108884692192078, 0.019656652584671974, 0.00807659700512886, -0.031129140406847, 0.013933942653238773, -0.0436280332505703, 0.010299374349415302, 0.014455551281571388, -0.028545599430799484, -0.023858899250626564, -0.009008358232676983, 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804 | 804 | ['Luciano Serafini', "Artur d'Avila Garcez"] | 1606.04422v2 | We propose Logic Tensor Networks: a uniform framework for integrating
automatic learning and reasoning. A logic formalism called Real Logic is
defined on a first-order language whereby formulas have truth-value in the
interval [0,1] and semantics defined concretely on the domain of real numbers.
Logical constants are interpreted as feature vectors of real numbers. Real
Logic promotes a well-founded integration of deductive reasoning on a
knowledge-base and efficient data-driven relational machine learning. We show
how Real Logic can be implemented in deep Tensor Neural Networks with the use
of Google's tensorflow primitives. The paper concludes with experiments
applying Logic Tensor Networks on a simple but representative example of
knowledge completion. | Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and
Knowledge | 2,016 | http://arxiv.org/pdf/1606.04422v2 | Title Logic Tensor Networks Deep Learning Logical Reasoning Data Knowledge Summary propose Logic Tensor Networks uniform framework integrating automatic learning reasoning logic formalism called Real Logic defined firstorder language whereby formula truthvalue interval 01 semantics defined concretely domain real number Logical constant interpreted feature vector real number Real Logic promotes wellfounded integration deductive reasoning knowledgebase efficient datadriven relational machine learning show Real Logic implemented deep Tensor Neural Networks use Googles tensorflow primitive paper concludes experiment applying Logic Tensor Networks simple representative example knowledge completion Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.005493875592947006, 0.061042167246341705, -0.020459052175283432, 0.0005539292469620705, -0.03672215715050697, 0.006175587419420481, 0.04792485013604164, 0.003593750298023224, 0.014896784909069538, -0.02431536838412285, 0.050967440009117126, 0.044971566647291183, -0.0448899008333683, 0.0772092193365097, -0.027581647038459778, -0.06606351584196091, 0.0072644902393221855, -0.002102195518091321, 0.05776359513401985, -0.017017925158143044, -0.013089471496641636, -0.0003876450064126402, -0.02901468239724636, -0.015690017491579056, -0.05508873984217644, -0.0048557184636592865, 0.005844851490110159, -0.03650479018688202, 0.019966011866927147, 0.03643620014190674, 0.03352586552500725, 0.02036137320101261, 0.023876992985606194, 0.0579930804669857, 2.2364099550031824e-06, 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805 | 805 | ['Randal S. Olson', 'Jason H. Moore'] | 1607.08878v1 | As data science continues to grow in popularity, there will be an increasing
need to make data science tools more scalable, flexible, and accessible. In
particular, automated machine learning (AutoML) systems seek to automate the
process of designing and optimizing machine learning pipelines. In this
chapter, we present a genetic programming-based AutoML system called TPOT that
optimizes a series of feature preprocessors and machine learning models with
the goal of maximizing classification accuracy on a supervised classification
problem. Further, we analyze a large database of pipelines that were previously
used to solve various supervised classification problems and identify 100 short
series of machine learning operations that appear the most frequently, which we
call the building blocks of machine learning pipelines. We harness these
building blocks to initialize TPOT with promising solutions, and find that this
sensible initialization method significantly improves TPOT's performance on one
benchmark at no cost of significantly degrading performance on the others.
Thus, sensible initialization with machine learning pipeline building blocks
shows promise for GP-based AutoML systems, and should be further refined in
future work. | Identifying and Harnessing the Building Blocks of Machine Learning
Pipelines for Sensible Initialization of a Data Science Automation Tool | 2,016 | http://arxiv.org/pdf/1607.08878v1 | Title Identifying Harnessing Building Blocks Machine Learning Pipelines Sensible Initialization Data Science Automation Tool Summary data science continues grow popularity increasing need make data science tool scalable flexible accessible particular automated machine learning AutoML system seek automate process designing optimizing machine learning pipeline chapter present genetic programmingbased AutoML system called TPOT optimizes series feature preprocessors machine learning model goal maximizing classification accuracy supervised classification problem analyze large database pipeline previously used solve various supervised classification problem identify 100 short series machine learning operation appear frequently call building block machine learning pipeline harness building block initialize TPOT promising solution find sensible initialization method significantly improves TPOTs performance one benchmark cost significantly degrading performance others Thus sensible initialization machine learning pipeline building block show promise GPbased AutoML system refined future work Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.006545540411025286, 0.025100000202655792, -0.06344747543334961, -0.031780652701854706, -0.002485371194779873, 0.033455368131399155, 0.04328327625989914, -0.003430407028645277, 0.03076452575623989, -0.03438777104020119, 0.051863860338926315, 0.027494585141539574, -0.0013480602065101266, 0.124763622879982, 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806 | 806 | ['Karan K. Budhraja', 'Tim Oates'] | 1608.02971v1 | The problem of Learning from Demonstration is targeted at learning to perform
tasks based on observed examples. One approach to Learning from Demonstration
is Inverse Reinforcement Learning, in which actions are observed to infer
rewards. This work combines a feature based state evaluation approach to
Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying
neural networks based on their performance on a given task. Neural networks are
used to learn from a demonstrated expert policy and are evolved to generate a
policy similar to the demonstration. The algorithm is discussed and evaluated
against competitive feature-based Inverse Reinforcement Learning approaches. At
the cost of execution time, neural networks allow for non-linear combinations
of features in state evaluations. These valuations may correspond to state
value or state reward. This results in better correspondence to observed
examples as opposed to using linear combinations. This work also extends
existing work on Bayesian Non-Parametric Feature Construction for Inverse
Reinforcement Learning by using non-linear combinations of intermediate data to
improve performance. The algorithm is observed to be specifically suitable for
a linearly solvable non-deterministic Markov Decision Processes in which
multiple rewards are sparsely scattered in state space. A conclusive
performance hierarchy between evaluated algorithms is presented. | Neuroevolution-Based Inverse Reinforcement Learning | 2,016 | http://arxiv.org/pdf/1608.02971v1 | Title NeuroevolutionBased Inverse Reinforcement Learning Summary problem Learning Demonstration targeted learning perform task based observed example One approach Learning Demonstration Inverse Reinforcement Learning action observed infer reward work combine feature based state evaluation approach Inverse Reinforcement Learning neuroevolution paradigm modifying neural network based performance given task Neural network used learn demonstrated expert policy evolved generate policy similar demonstration algorithm discussed evaluated competitive featurebased Inverse Reinforcement Learning approach cost execution time neural network allow nonlinear combination feature state evaluation valuation may correspond state value state reward result better correspondence observed example opposed using linear combination work also extends existing work Bayesian NonParametric Feature Construction Inverse Reinforcement Learning using nonlinear combination intermediate data improve performance algorithm observed specifically suitable linearly solvable nondeterministic Markov Decision Processes multiple reward sparsely scattered state space conclusive performance hierarchy evaluated algorithm presented Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.015042547136545181, 0.004591308068484068, -0.005603528581559658, -0.045446816831827164, -0.016664888709783554, -0.04285820201039314, -0.03574316203594208, 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807 | 807 | ['Alexander L. Gaunt', 'Marc Brockschmidt', 'Rishabh Singh', 'Nate Kushman', 'Pushmeet Kohli', 'Jonathan Taylor', 'Daniel Tarlow'] | 1608.04428v1 | We study machine learning formulations of inductive program synthesis; given
input-output examples, we try to synthesize source code that maps inputs to
corresponding outputs. Our aims are to develop new machine learning approaches
based on neural networks and graphical models, and to understand the
capabilities of machine learning techniques relative to traditional
alternatives, such as those based on constraint solving from the programming
languages community.
Our key contribution is the proposal of TerpreT, a domain-specific language
for expressing program synthesis problems. TerpreT is similar to a
probabilistic programming language: a model is composed of a specification of a
program representation (declarations of random variables) and an interpreter
describing how programs map inputs to outputs (a model connecting unknowns to
observations). The inference task is to observe a set of input-output examples
and infer the underlying program. TerpreT has two main benefits. First, it
enables rapid exploration of a range of domains, program representations, and
interpreter models. Second, it separates the model specification from the
inference algorithm, allowing like-to-like comparisons between different
approaches to inference. From a single TerpreT specification we automatically
perform inference using four different back-ends. These are based on gradient
descent, linear program (LP) relaxations for graphical models, discrete
satisfiability solving, and the Sketch program synthesis system.
We illustrate the value of TerpreT by developing several interpreter models
and performing an empirical comparison between alternative inference
algorithms. Our key empirical finding is that constraint solvers dominate the
gradient descent and LP-based formulations. We conclude with suggestions for
the machine learning community to make progress on program synthesis. | TerpreT: A Probabilistic Programming Language for Program Induction | 2,016 | http://arxiv.org/pdf/1608.04428v1 | Title TerpreT Probabilistic Programming Language Program Induction Summary study machine learning formulation inductive program synthesis given inputoutput example try synthesize source code map input corresponding output aim develop new machine learning approach based neural network graphical model understand capability machine learning technique relative traditional alternative based constraint solving programming language community key contribution proposal TerpreT domainspecific language expressing program synthesis problem TerpreT similar probabilistic programming language model composed specification program representation declaration random variable interpreter describing program map input output model connecting unknown observation inference task observe set inputoutput example infer underlying program TerpreT two main benefit First enables rapid exploration range domain program representation interpreter model Second separate model specification inference algorithm allowing liketolike comparison different approach inference single TerpreT specification automatically perform inference using four different backends based gradient descent linear program LP relaxation graphical model discrete satisfiability solving Sketch program synthesis system illustrate value TerpreT developing several interpreter model performing empirical comparison alternative inference algorithm key empirical finding constraint solver dominate gradient descent LPbased formulation conclude suggestion machine learning community make progress program synthesis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.01722017489373684, 0.017943255603313446, -0.027007954195141792, -0.010089694522321224, -0.024836236611008644, -0.023966439068317413, -0.04823090881109238, -0.02402658388018608, 0.0008894599159248173, -0.03042583353817463, 0.08167152106761932, 0.08090608566999435, 0.025328921154141426, 0.10649725049734116, 0.015834921970963478, -0.06300108134746552, 0.025274690240621567, -0.034562159329652786, 0.000121623495942913, 0.029340585693717003, -0.005207919515669346, 0.00894305482506752, 0.002306498121470213, 0.03862259164452553, 0.0038658150006085634, 0.004608887247741222, -0.01374767441302538, -0.004048720467835665, -0.014231550507247448, -0.037549108266830444, -0.00770825007930398, 0.03566958010196686, -0.020441345870494843, -0.007343175821006298, 2.280341732330271e-06, 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808 | 808 | ['Rajasekar Venkatesan', 'Meng Joo Er'] | 1608.08435v1 | In this paper, an Extreme Learning Machine (ELM) based technique for
Multi-label classification problems is proposed and discussed. In multi-label
classification, each of the input data samples belongs to one or more than one
class labels. The traditional binary and multi-class classification problems
are the subset of the multi-label problem with the number of labels
corresponding to each sample limited to one. The proposed ELM based multi-label
classification technique is evaluated with six different benchmark multi-label
datasets from different domains such as multimedia, text and biology. A
detailed comparison of the results is made by comparing the proposed method
with the results from nine state of the arts techniques for five different
evaluation metrics. The nine methods are chosen from different categories of
multi-label methods. The comparative results shows that the proposed Extreme
Learning Machine based multi-label classification technique is a better
alternative than the existing state of the art methods for multi-label
problems. | Multi-Label Classification Method Based on Extreme Learning Machines | 2,016 | http://arxiv.org/pdf/1608.08435v1 | Title MultiLabel Classification Method Based Extreme Learning Machines Summary paper Extreme Learning Machine ELM based technique Multilabel classification problem proposed discussed multilabel classification input data sample belongs one one class label traditional binary multiclass classification problem subset multilabel problem number label corresponding sample limited one proposed ELM based multilabel classification technique evaluated six different benchmark multilabel datasets different domain multimedia text biology detailed comparison result made comparing proposed method result nine state art technique five different evaluation metric nine method chosen different category multilabel method comparative result show proposed Extreme Learning Machine based multilabel classification technique better alternative existing state art method multilabel problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03651493787765503, -0.03894156590104103, -0.02149415761232376, -0.016799520701169968, 0.014798006042838097, 0.04487765580415726, 0.0054421694949269295, 0.023157594725489616, 0.021656595170497894, -0.04483086243271828, -0.013578305952250957, 0.056093357503414154, -0.013931783847510815, 0.01588790863752365, 0.010210406966507435, -0.018250063061714172, 0.010305104777216911, 0.024491479620337486, 0.053672511130571365, -0.013156113214790821, 0.035018447786569595, -0.018597690388560295, 0.019318755716085434, 0.040805596858263016, 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809 | 809 | ['Rajasekar Venkatesan', 'Meng Joo Er', 'Shiqian Wu', 'Mahardhika Pratama'] | 1608.08905v1 | In this paper, a novel extreme learning machine based online multi-label
classifier for real-time data streams is proposed. Multi-label classification
is one of the actively researched machine learning paradigm that has gained
much attention in the recent years due to its rapidly increasing real world
applications. In contrast to traditional binary and multi-class classification,
multi-label classification involves association of each of the input samples
with a set of target labels simultaneously. There are no real-time online
neural network based multi-label classifier available in the literature. In
this paper, we exploit the inherent nature of high speed exhibited by the
extreme learning machines to develop a novel online real-time classifier for
multi-label data streams. The developed classifier is experimented with
datasets from different application domains for consistency, performance and
speed. The experimental studies show that the proposed method outperforms the
existing state-of-the-art techniques in terms of speed and accuracy and can
classify multi-label data streams in real-time. | A Novel Online Real-time Classifier for Multi-label Data Streams | 2,016 | http://arxiv.org/pdf/1608.08905v1 | Title Novel Online Realtime Classifier Multilabel Data Streams Summary paper novel extreme learning machine based online multilabel classifier realtime data stream proposed Multilabel classification one actively researched machine learning paradigm gained much attention recent year due rapidly increasing real world application contrast traditional binary multiclass classification multilabel classification involves association input sample set target label simultaneously realtime online neural network based multilabel classifier available literature paper exploit inherent nature high speed exhibited extreme learning machine develop novel online realtime classifier multilabel data stream developed classifier experimented datasets different application domain consistency performance speed experimental study show proposed method outperforms existing stateoftheart technique term speed accuracy classify multilabel data stream realtime Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.006298888940364122, 0.0011950135231018066, -0.015283727087080479, -0.010427319444715977, 0.01822822168469429, 0.033135946840047836, 0.03005293756723404, -0.00841550249606371, 0.024135859683156013, -0.03136824443936348, 0.005537543911486864, 0.0399785116314888, -0.029923059046268463, 0.07971092313528061, 0.021839916706085205, -0.030413052067160606, -0.013278304599225521, 0.013830187730491161, 0.03800642490386963, 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810 | 810 | ['Rajasekar Venkatesan', 'Meng Joo Er'] | 1609.00085v2 | In this paper, a progressive learning technique for multi-class
classification is proposed. This newly developed learning technique is
independent of the number of class constraints and it can learn new classes
while still retaining the knowledge of previous classes. Whenever a new class
(non-native to the knowledge learnt thus far) is encountered, the neural
network structure gets remodeled automatically by facilitating new neurons and
interconnections, and the parameters are calculated in such a way that it
retains the knowledge learnt thus far. This technique is suitable for
real-world applications where the number of classes is often unknown and online
learning from real-time data is required. The consistency and the complexity of
the progressive learning technique are analyzed. Several standard datasets are
used to evaluate the performance of the developed technique. A comparative
study shows that the developed technique is superior. | A Novel Progressive Learning Technique for Multi-class Classification | 2,016 | http://arxiv.org/pdf/1609.00085v2 | Title Novel Progressive Learning Technique Multiclass Classification Summary paper progressive learning technique multiclass classification proposed newly developed learning technique independent number class constraint learn new class still retaining knowledge previous class Whenever new class nonnative knowledge learnt thus far encountered neural network structure get remodeled automatically facilitating new neuron interconnection parameter calculated way retains knowledge learnt thus far technique suitable realworld application number class often unknown online learning realtime data required consistency complexity progressive learning technique analyzed Several standard datasets used evaluate performance developed technique comparative study show developed technique superior Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.009050623513758183, 0.07899542897939682, -0.013789976015686989, -0.0008588870405219495, -0.010595673695206642, -0.0043508633971214294, 0.03322456777095795, -0.007411185186356306, -0.022002218291163445, -0.026269644498825073, 0.01577657274901867, 0.0244437325745821, 0.007352571468800306, -0.005320791155099869, 0.01636802963912487, -0.028692949563264847, 0.017033852636814117, 0.025699133053421974, -0.0043922243639826775, 0.002991496818140149, 0.00997949205338955, -0.03958693519234657, -0.016112789511680603, 0.018069036304950714, -0.04893243685364723, 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811 | 811 | ['Rajasekar Venkatesan', 'Meng Joo Er', 'Mihika Dave', 'Mahardhika Pratama', 'Shiqian Wu'] | 1609.00086v1 | In this paper, a high-speed online neural network classifier based on extreme
learning machines for multi-label classification is proposed. In multi-label
classification, each of the input data sample belongs to one or more than one
of the target labels. The traditional binary and multi-class classification
where each sample belongs to only one target class forms the subset of
multi-label classification. Multi-label classification problems are far more
complex than binary and multi-class classification problems, as both the number
of target labels and each of the target labels corresponding to each of the
input samples are to be identified. The proposed work exploits the high-speed
nature of the extreme learning machines to achieve real-time multi-label
classification of streaming data. A new threshold-based online sequential
learning algorithm is proposed for high speed and streaming data classification
of multi-label problems. The proposed method is experimented with six different
datasets from different application domains such as multimedia, text, and
biology. The hamming loss, accuracy, training time and testing time of the
proposed technique is compared with nine different state-of-the-art methods.
Experimental studies shows that the proposed technique outperforms the existing
multi-label classifiers in terms of performance and speed. | A novel online multi-label classifier for high-speed streaming data
applications | 2,016 | http://arxiv.org/pdf/1609.00086v1 | Title novel online multilabel classifier highspeed streaming data application Summary paper highspeed online neural network classifier based extreme learning machine multilabel classification proposed multilabel classification input data sample belongs one one target label traditional binary multiclass classification sample belongs one target class form subset multilabel classification Multilabel classification problem far complex binary multiclass classification problem number target label target label corresponding input sample identified proposed work exploit highspeed nature extreme learning machine achieve realtime multilabel classification streaming data new thresholdbased online sequential learning algorithm proposed high speed streaming data classification multilabel problem proposed method experimented six different datasets different application domain multimedia text biology hamming loss accuracy training time testing time proposed technique compared nine different stateoftheart method Experimental study show proposed technique outperforms existing multilabel classifier term performance speed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0017909012967720628, 0.011116793379187584, -0.012937987223267555, -0.017560698091983795, 0.01685900054872036, 0.02884375862777233, 0.020700978115200996, 0.016809318214654922, 0.024953285232186317, -0.033660922199487686, -0.008609489537775517, 0.03520015627145767, -0.02639882266521454, 0.08068560063838959, 0.004453576635569334, 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812 | 812 | ['Hande Alemdar', 'Vincent Leroy', 'Adrien Prost-Boucle', 'Frédéric Pétrot'] | 1609.00222v2 | The computation and storage requirements for Deep Neural Networks (DNNs) are
usually high. This issue limits their deployability on ubiquitous computing
devices such as smart phones, wearables and autonomous drones. In this paper,
we propose ternary neural networks (TNNs) in order to make deep learning more
resource-efficient. We train these TNNs using a teacher-student approach based
on a novel, layer-wise greedy methodology. Thanks to our two-stage training
procedure, the teacher network is still able to use state-of-the-art methods
such as dropout and batch normalization to increase accuracy and reduce
training time. Using only ternary weights and activations, the student ternary
network learns to mimic the behavior of its teacher network without using any
multiplication. Unlike its -1,1 binary counterparts, a ternary neural network
inherently prunes the smaller weights by setting them to zero during training.
This makes them sparser and thus more energy-efficient. We design a
purpose-built hardware architecture for TNNs and implement it on FPGA and ASIC.
We evaluate TNNs on several benchmark datasets and demonstrate up to 3.1x
better energy efficiency with respect to the state of the art while also
improving accuracy. | Ternary Neural Networks for Resource-Efficient AI Applications | 2,016 | http://arxiv.org/pdf/1609.00222v2 | Title Ternary Neural Networks ResourceEfficient AI Applications Summary computation storage requirement Deep Neural Networks DNNs usually high issue limit deployability ubiquitous computing device smart phone wearable autonomous drone paper propose ternary neural network TNNs order make deep learning resourceefficient train TNNs using teacherstudent approach based novel layerwise greedy methodology Thanks twostage training procedure teacher network still able use stateoftheart method dropout batch normalization increase accuracy reduce training time Using ternary weight activation student ternary network learns mimic behavior teacher network without using multiplication Unlike 11 binary counterpart ternary neural network inherently prune smaller weight setting zero training make sparser thus energyefficient design purposebuilt hardware architecture TNNs implement FPGA ASIC evaluate TNNs several benchmark datasets demonstrate 31x better energy efficiency respect state art also improving accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.007484246511012316, 0.03200938552618027, -0.02053963765501976, 0.029234547168016434, -0.0013657851377502084, -0.05449562519788742, 0.06825247406959534, -0.017961448058485985, -0.06530588120222092, -0.04260271415114403, 0.01697932556271553, -0.004746080841869116, -0.02053097076714039, 0.04266133904457092, 0.05788387730717659, -0.016835305839776993, 0.043124355375766754, 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813 | 813 | ['Navid Kardan', 'Kenneth O. Stanley'] | 1609.02226v3 | Though deep learning has pushed the boundaries of classification forward, in
recent years hints of the limits of standard classification have begun to
emerge. Problems such as fooling, adding new classes over time, and the need to
retrain learning models only for small changes to the original problem all
point to a potential shortcoming in the classic classification regime, where a
comprehensive a priori knowledge of the possible classes or concepts is
critical. Without such knowledge, classifiers misjudge the limits of their
knowledge and overgeneralization therefore becomes a serious obstacle to
consistent performance. In response to these challenges, this paper extends the
classic regime by reframing classification instead with the assumption that
concepts present in the training set are only a sample of the hypothetical
final set of concepts. To bring learning models into this new paradigm, a novel
elaboration of standard architectures called the competitive overcomplete
output layer (COOL) neural network is introduced. Experiments demonstrate the
effectiveness of COOL by applying it to fooling, separable concept learning,
one-class neural networks, and standard classification benchmarks. The results
suggest that, unlike conventional classifiers, the amount of generalization in
COOL networks can be tuned to match the problem. | Fitted Learning: Models with Awareness of their Limits | 2,016 | http://arxiv.org/pdf/1609.02226v3 | Title Fitted Learning Models Awareness Limits Summary Though deep learning pushed boundary classification forward recent year hint limit standard classification begun emerge Problems fooling adding new class time need retrain learning model small change original problem point potential shortcoming classic classification regime comprehensive priori knowledge possible class concept critical Without knowledge classifier misjudge limit knowledge overgeneralization therefore becomes serious obstacle consistent performance response challenge paper extends classic regime reframing classification instead assumption concept present training set sample hypothetical final set concept bring learning model new paradigm novel elaboration standard architecture called competitive overcomplete output layer COOL neural network introduced Experiments demonstrate effectiveness COOL applying fooling separable concept learning oneclass neural network standard classification benchmark result suggest unlike conventional classifier amount generalization COOL network tuned match problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.004553238861262798, 0.023862645030021667, -0.02515711449086666, 0.015982070937752724, 0.004415976349264383, 3.2951364119071513e-05, 0.02864004857838154, 0.004825477488338947, -0.037041936069726944, -0.014320683665573597, 0.0034049013629555702, 0.026574747636914253, -0.010187797248363495, 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814 | 814 | ['Thomas Miconi'] | 1609.02228v2 | Hebbian plasticity is a powerful principle that allows biological brains to
learn from their lifetime experience. By contrast, artificial neural networks
trained with backpropagation generally have fixed connection weights that do
not change once training is complete. While recent methods can endow neural
networks with long-term memories, Hebbian plasticity is currently not amenable
to gradient descent. Here we derive analytical expressions for activity
gradients in neural networks with Hebbian plastic connections. Using these
expressions, we can use backpropagation to train not just the baseline weights
of the connections, but also their plasticity. As a result, the networks "learn
how to learn" in order to solve the problem at hand: the trained networks
automatically perform fast learning of unpredictable environmental features
during their lifetime, expanding the range of solvable problems. We test the
algorithm on various on-line learning tasks, including pattern completion,
one-shot learning, and reversal learning. The algorithm successfully learns how
to learn the relevant associations from one-shot instruction, and fine-tunes
the temporal dynamics of plasticity to allow for continual learning in response
to changing environmental parameters. We conclude that backpropagation of
Hebbian plasticity offers a powerful model for lifelong learning. | Learning to learn with backpropagation of Hebbian plasticity | 2,016 | http://arxiv.org/pdf/1609.02228v2 | Title Learning learn backpropagation Hebbian plasticity Summary Hebbian plasticity powerful principle allows biological brain learn lifetime experience contrast artificial neural network trained backpropagation generally fixed connection weight change training complete recent method endow neural network longterm memory Hebbian plasticity currently amenable gradient descent derive analytical expression activity gradient neural network Hebbian plastic connection Using expression use backpropagation train baseline weight connection also plasticity result network learn learn order solve problem hand trained network automatically perform fast learning unpredictable environmental feature lifetime expanding range solvable problem test algorithm various online learning task including pattern completion oneshot learning reversal learning algorithm successfully learns learn relevant association oneshot instruction finetunes temporal dynamic plasticity allow continual learning response changing environmental parameter conclude backpropagation Hebbian plasticity offer powerful model lifelong learning Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.005512428469955921, 0.006936597637832165, -0.031196948140859604, -0.028671780601143837, 0.01135297678411007, -0.021965527907013893, 0.037528522312641144, 0.0007433979772031307, -0.019423458725214005, 0.037506431341171265, -0.016754431650042534, 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815 | 815 | ['Lana Sinapayen', 'Atsushi Masumori', 'Takashi Ikegami'] | 1609.07706v1 | Learning based on networks of real neurons, and by extension biologically
inspired models of neural networks, has yet to find general learning rules
leading to widespread applications. In this paper, we argue for the existence
of a principle allowing to steer the dynamics of a biologically inspired neural
network. Using carefully timed external stimulation, the network can be driven
towards a desired dynamical state. We term this principle "Learning by
Stimulation Avoidance" (LSA). We demonstrate through simulation that the
minimal sufficient conditions leading to LSA in artificial networks are also
sufficient to reproduce learning results similar to those obtained in
biological neurons by Shahaf and Marom [1]. We examine the mechanism's basic
dynamics in a reduced network, and demonstrate how it scales up to a network of
100 neurons. We show that LSA has a higher explanatory power than existing
hypotheses about the response of biological neural networks to external
simulation, and can be used as a learning rule for an embodied application:
learning of wall avoidance by a simulated robot. The surge in popularity of
artificial neural networks is mostly directed to disembodied models of neurons
with biologically irrelevant dynamics: to the authors' knowledge, this is the
first work demonstrating sensory-motor learning with random spiking networks
through pure Hebbian learning. | Learning by Stimulation Avoidance: A Principle to Control Spiking Neural
Networks Dynamics | 2,016 | http://arxiv.org/pdf/1609.07706v1 | Title Learning Stimulation Avoidance Principle Control Spiking Neural Networks Dynamics Summary Learning based network real neuron extension biologically inspired model neural network yet find general learning rule leading widespread application paper argue existence principle allowing steer dynamic biologically inspired neural network Using carefully timed external stimulation network driven towards desired dynamical state term principle Learning Stimulation Avoidance LSA demonstrate simulation minimal sufficient condition leading LSA artificial network also sufficient reproduce learning result similar obtained biological neuron Shahaf Marom 1 examine mechanism basic dynamic reduced network demonstrate scale network 100 neuron show LSA higher explanatory power existing hypothesis response biological neural network external simulation used learning rule embodied application learning wall avoidance simulated robot surge popularity artificial neural network mostly directed disembodied model neuron biologically irrelevant dynamic author knowledge first work demonstrating sensorymotor learning random spiking network pure Hebbian learning Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.018463438376784325, -0.03528013452887535, -0.028859732672572136, -0.003135600360110402, 0.010782533325254917, -0.007587278727442026, -0.0055375006049871445, -0.003981065936386585, 0.01698615401983261, 0.027135254815220833, -0.036468978971242905, 0.027992412447929382, -0.030093610286712646, 0.05802058428525925, 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816 | 816 | ['Kamil Rocki', 'Tomasz Kornuta', 'Tegan Maharaj'] | 1610.07675v6 | We propose a novel method of regularization for recurrent neural networks
called suprisal-driven zoneout. In this method, states zoneout (maintain their
previous value rather than updating), when the suprisal (discrepancy between
the last state's prediction and target) is small. Thus regularization is
adaptive and input-driven on a per-neuron basis. We demonstrate the
effectiveness of this idea by achieving state-of-the-art bits per character of
1.31 on the Hutter Prize Wikipedia dataset, significantly reducing the gap to
the best known highly-engineered compression methods. | Surprisal-Driven Zoneout | 2,016 | http://arxiv.org/pdf/1610.07675v6 | Title SurprisalDriven Zoneout Summary propose novel method regularization recurrent neural network called suprisaldriven zoneout method state zoneout maintain previous value rather updating suprisal discrepancy last state prediction target small Thus regularization adaptive inputdriven perneuron basis demonstrate effectiveness idea achieving stateoftheart bit per character 131 Hutter Prize Wikipedia dataset significantly reducing gap best known highlyengineered compression method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 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817 | 817 | ['Barret Zoph', 'Quoc V. Le'] | 1611.01578v2 | Neural networks are powerful and flexible models that work well for many
difficult learning tasks in image, speech and natural language understanding.
Despite their success, neural networks are still hard to design. In this paper,
we use a recurrent network to generate the model descriptions of neural
networks and train this RNN with reinforcement learning to maximize the
expected accuracy of the generated architectures on a validation set. On the
CIFAR-10 dataset, our method, starting from scratch, can design a novel network
architecture that rivals the best human-invented architecture in terms of test
set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is
0.09 percent better and 1.05x faster than the previous state-of-the-art model
that used a similar architectural scheme. On the Penn Treebank dataset, our
model can compose a novel recurrent cell that outperforms the widely-used LSTM
cell, and other state-of-the-art baselines. Our cell achieves a test set
perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than
the previous state-of-the-art model. The cell can also be transferred to the
character language modeling task on PTB and achieves a state-of-the-art
perplexity of 1.214. | Neural Architecture Search with Reinforcement Learning | 2,016 | http://arxiv.org/pdf/1611.01578v2 | Title Neural Architecture Search Reinforcement Learning Summary Neural network powerful flexible model work well many difficult learning task image speech natural language understanding Despite success neural network still hard design paper use recurrent network generate model description neural network train RNN reinforcement learning maximize expected accuracy generated architecture validation set CIFAR10 dataset method starting scratch design novel network architecture rival best humaninvented architecture term test set accuracy CIFAR10 model achieves test error rate 365 009 percent better 105x faster previous stateoftheart model used similar architectural scheme Penn Treebank dataset model compose novel recurrent cell outperforms widelyused LSTM cell stateoftheart baseline cell achieves test set perplexity 624 Penn Treebank 36 perplexity better previous stateoftheart model cell also transferred character language modeling task PTB achieves stateoftheart perplexity 1214 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04315446689724922, 0.041258394718170166, -0.010374544188380241, 0.03191472217440605, -0.0324622243642807, -0.016813019290566444, 0.0007508726557716727, 0.0015609796391800046, -0.047142814844846725, 0.00805315189063549, -0.015890780836343765, -0.03392862156033516, 0.012950747273862362, 0.07452162355184555, 0.014718878082931042, -0.06787525117397308, 0.03888126090168953, -0.011145396158099174, 0.005551391281187534, 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818 | 818 | ['Brian Cheung', 'Eric Weiss', 'Bruno Olshausen'] | 1611.09430v2 | We describe a neural attention model with a learnable retinal sampling
lattice. The model is trained on a visual search task requiring the
classification of an object embedded in a visual scene amidst background
distractors using the smallest number of fixations. We explore the tiling
properties that emerge in the model's retinal sampling lattice after training.
Specifically, we show that this lattice resembles the eccentricity dependent
sampling lattice of the primate retina, with a high resolution region in the
fovea surrounded by a low resolution periphery. Furthermore, we find conditions
where these emergent properties are amplified or eliminated providing clues to
their function. | Emergence of foveal image sampling from learning to attend in visual
scenes | 2,016 | http://arxiv.org/pdf/1611.09430v2 | Title Emergence foveal image sampling learning attend visual scene Summary describe neural attention model learnable retinal sampling lattice model trained visual search task requiring classification object embedded visual scene amidst background distractors using smallest number fixation explore tiling property emerge model retinal sampling lattice training Specifically show lattice resembles eccentricity dependent sampling lattice primate retina high resolution region fovea surrounded low resolution periphery Furthermore find condition emergent property amplified eliminated providing clue function Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.012428577058017254, -0.03871902450919151, -0.03312397003173828, 0.011463208124041557, 0.024118706583976746, -0.01312296837568283, 0.012522065080702305, -0.016382869333028793, -0.024642430245876312, 0.0025040465407073498, 0.016565145924687386, 0.01324431411921978, 0.0005026690778322518, 0.02636227384209633, 0.01626433990895748, -0.08360543847084045, -0.04189461097121239, 0.00995832309126854, -0.0692012682557106, -0.04687858372926712, 0.013502194546163082, -0.006289735436439514, 0.05466052517294884, -0.0020655086264014244, 0.0010879684705287218, -0.015948709100484848, -0.0056297434493899345, 0.014818510971963406, 0.004265063442289829, -0.0059899827465415, 0.033020418137311935, -0.003658798523247242, 0.005342163611203432, -0.00225790380500257, 2.276669192724512e-06, -0.03387782350182533, 0.016373855993151665, -0.014519480057060719, 0.015688136219978333, 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819 | 819 | ['Steven Stenberg Hansen'] | 1701.03866v1 | Credit assignment in traditional recurrent neural networks usually involves
back-propagating through a long chain of tied weight matrices. The length of
this chain scales linearly with the number of time-steps as the same network is
run at each time-step. This creates many problems, such as vanishing gradients,
that have been well studied. In contrast, a NNEM's architecture recurrent
activity doesn't involve a long chain of activity (though some architectures
such as the NTM do utilize a traditional recurrent architecture as a
controller). Rather, the externally stored embedding vectors are used at each
time-step, but no messages are passed from previous time-steps. This means that
vanishing gradients aren't a problem, as all of the necessary gradient paths
are short. However, these paths are extremely numerous (one per embedding
vector in memory) and reused for a very long time (until it leaves the memory).
Thus, the forward-pass information of each memory must be stored for the entire
duration of the memory. This is problematic as this additional storage far
surpasses that of the actual memories, to the extent that large memories on
infeasible to back-propagate through in high dimensional settings. One way to
get around the need to hold onto forward-pass information is to recalculate the
forward-pass whenever gradient information is available. However, if the
observations are too large to store in the domain of interest, direct
reinstatement of a forward pass cannot occur. Instead, we rely on a learned
autoencoder to reinstate the observation, and then use the embedding network to
recalculate the forward-pass. Since the recalculated embedding vector is
unlikely to perfectly match the one stored in memory, we try out 2
approximations to utilize error gradient w.r.t. the vector in memory. | Long Timescale Credit Assignment in NeuralNetworks with External Memory | 2,017 | http://arxiv.org/pdf/1701.03866v1 | Title Long Timescale Credit Assignment NeuralNetworks External Memory Summary Credit assignment traditional recurrent neural network usually involves backpropagating long chain tied weight matrix length chain scale linearly number timesteps network run timestep creates many problem vanishing gradient well studied contrast NNEMs architecture recurrent activity doesnt involve long chain activity though architecture NTM utilize traditional recurrent architecture controller Rather externally stored embedding vector used timestep message passed previous timesteps mean vanishing gradient arent problem necessary gradient path short However path extremely numerous one per embedding vector memory reused long time leaf memory Thus forwardpass information memory must stored entire duration memory problematic additional storage far surpasses actual memory extent large memory infeasible backpropagate high dimensional setting One way get around need hold onto forwardpass information recalculate forwardpass whenever gradient information available However observation large store domain interest direct reinstatement forward pas cannot occur Instead rely learned autoencoder reinstate observation use embedding network recalculate forwardpass Since recalculated embedding vector unlikely perfectly match one stored memory try 2 approximation utilize error gradient wrt vector memory Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name 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820 | 820 | ['Arman Afrasiyabi', 'Ozan Yildiz', 'Baris Nasir', 'Fatos T. Yarman Vural', 'A. Enis Cetin'] | 1702.02676v1 | In recent years, machine learning techniques based on neural networks for
mobile computing become increasingly popular. Classical multi-layer neural
networks require matrix multiplications at each stage. Multiplication operation
is not an energy efficient operation and consequently it drains the battery of
the mobile device. In this paper, we propose a new energy efficient neural
network with the universal approximation property over space of Lebesgue
integrable functions. This network, called, additive neural network, is very
suitable for mobile computing. The neural structure is based on a novel vector
product definition, called ef-operator, that permits a multiplier-free
implementation. In ef-operation, the "product" of two real numbers is defined
as the sum of their absolute values, with the sign determined by the sign of
the product of the numbers. This "product" is used to construct a vector
product in $R^N$. The vector product induces the $l_1$ norm. The proposed
additive neural network successfully solves the XOR problem. The experiments on
MNIST dataset show that the classification performances of the proposed
additive neural networks are very similar to the corresponding multi-layer
perceptron and convolutional neural networks (LeNet). | Energy Saving Additive Neural Network | 2,017 | http://arxiv.org/pdf/1702.02676v1 | Title Energy Saving Additive Neural Network Summary recent year machine learning technique based neural network mobile computing become increasingly popular Classical multilayer neural network require matrix multiplication stage Multiplication operation energy efficient operation consequently drain battery mobile device paper propose new energy efficient neural network universal approximation property space Lebesgue integrable function network called additive neural network suitable mobile computing neural structure based novel vector product definition called efoperator permit multiplierfree implementation efoperation product two real number defined sum absolute value sign determined sign product number product used construct vector product RN vector product induces l1 norm proposed additive neural network successfully solves XOR problem experiment MNIST dataset show classification performance proposed additive neural network similar corresponding multilayer perceptron convolutional neural network LeNet Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.041559599339962006, 0.0196552537381649, -0.013178504072129726, 0.01698065921664238, 0.020249901339411736, -0.004165116231888533, 0.043071042746305466, -0.03175780177116394, -0.05672718212008476, -0.0013886174419894814, 0.003121745539829135, 0.01433127373456955, 0.022105585783720016, 0.007520826067775488, 0.048379458487033844, 0.02131614461541176, 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821 | 821 | ['Sahil Sharma', 'Aravind S. Lakshminarayanan', 'Balaraman Ravindran'] | 1702.06054v1 | Reinforcement Learning algorithms can learn complex behavioral patterns for
sequential decision making tasks wherein an agent interacts with an environment
and acquires feedback in the form of rewards sampled from it. Traditionally,
such algorithms make decisions, i.e., select actions to execute, at every
single time step of the agent-environment interactions. In this paper, we
propose a novel framework, Fine Grained Action Repetition (FiGAR), which
enables the agent to decide the action as well as the time scale of repeating
it. FiGAR can be used for improving any Deep Reinforcement Learning algorithm
which maintains an explicit policy estimate by enabling temporal abstractions
in the action space. We empirically demonstrate the efficacy of our framework
by showing performance improvements on top of three policy search algorithms in
different domains: Asynchronous Advantage Actor Critic in the Atari 2600
domain, Trust Region Policy Optimization in Mujoco domain and Deep
Deterministic Policy Gradients in the TORCS car racing domain. | Learning to Repeat: Fine Grained Action Repetition for Deep
Reinforcement Learning | 2,017 | http://arxiv.org/pdf/1702.06054v1 | Title Learning Repeat Fine Grained Action Repetition Deep Reinforcement Learning Summary Reinforcement Learning algorithm learn complex behavioral pattern sequential decision making task wherein agent interacts environment acquires feedback form reward sampled Traditionally algorithm make decision ie select action execute every single time step agentenvironment interaction paper propose novel framework Fine Grained Action Repetition FiGAR enables agent decide action well time scale repeating FiGAR used improving Deep Reinforcement Learning algorithm maintains explicit policy estimate enabling temporal abstraction action space empirically demonstrate efficacy framework showing performance improvement top three policy search algorithm different domain Asynchronous Advantage Actor Critic Atari 2600 domain Trust Region Policy Optimization Mujoco domain Deep Deterministic Policy Gradients TORCS car racing domain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.007449817378073931, -0.017083393409848213, -0.020155394449830055, -0.014041852205991745, -0.0049669453874230385, 0.01620970480144024, -0.00025756648392416537, -0.018405510112643242, -0.04354967921972275, -0.008511803112924099, -0.0058097923174500465, 0.04968591779470444, -0.00846255011856556, 0.09525875747203827, 0.027240052819252014, -0.07232317328453064, 0.02365017496049404, -0.009466370567679405, 0.033082783222198486, -0.011718282476067543, 0.04639793932437897, -0.0634300634264946, -0.017108440399169922, 0.05570390820503235, 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822 | 822 | ['Amit Sahu'] | 1702.06186v2 | Reason and inference require process as well as memory skills by humans.
Neural networks are able to process tasks like image recognition (better than
humans) but in memory aspects are still limited (by attention mechanism, size).
Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve
small memory contexts, but as context becomes larger than a threshold, it is
difficult to use them. The Solution is to use large external memory. Still, it
poses many challenges like, how to train neural networks for discrete memory
representation, how to describe long term dependencies in sequential data etc.
Most prominent neural architectures for such tasks are Memory networks:
inference components combined with long term memory and Neural Turing Machines:
neural networks using external memory resources. Also, additional techniques
like attention mechanism, end to end gradient descent on discrete memory
representation are needed to support these solutions. Preliminary results of
above neural architectures on simple algorithms (sorting, copying) and Question
Answering (based on story, dialogs) application are comparable with the state
of the art. In this paper, I explain these architectures (in general), the
additional techniques used and the results of their application. | Survey of reasoning using Neural networks | 2,017 | http://arxiv.org/pdf/1702.06186v2 | Title Survey reasoning using Neural network Summary Reason inference require process well memory skill human Neural network able process task like image recognition better human memory aspect still limited attention mechanism size Recurrent Neural Network RNN modified version LSTM able solve small memory context context becomes larger threshold difficult use Solution use large external memory Still pose many challenge like train neural network discrete memory representation describe long term dependency sequential data etc prominent neural architecture task Memory network inference component combined long term memory Neural Turing Machines neural network using external memory resource Also additional technique like attention mechanism end end gradient descent discrete memory representation needed support solution Preliminary result neural architecture simple algorithm sorting copying Question Answering based story dialog application comparable state art paper explain architecture general additional technique used result application Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05290656164288521, 0.045261893421411514, -0.00019537043408490717, 0.06497050076723099, -0.039999376982450485, 0.0035967694129794836, 0.022891681641340256, 0.00871207844465971, 0.014085907489061356, -0.042083173990249634, 0.01758415438234806, 0.008582827635109425, 0.004018485080450773, 0.018058646470308304, 0.018950611352920532, 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823 | 823 | ['Yan Duan', 'Marcin Andrychowicz', 'Bradly C. Stadie', 'Jonathan Ho', 'Jonas Schneider', 'Ilya Sutskever', 'Pieter Abbeel', 'Wojciech Zaremba'] | 1703.07326v3 | Imitation learning has been commonly applied to solve different tasks in
isolation. This usually requires either careful feature engineering, or a
significant number of samples. This is far from what we desire: ideally, robots
should be able to learn from very few demonstrations of any given task, and
instantly generalize to new situations of the same task, without requiring
task-specific engineering. In this paper, we propose a meta-learning framework
for achieving such capability, which we call one-shot imitation learning.
Specifically, we consider the setting where there is a very large set of
tasks, and each task has many instantiations. For example, a task could be to
stack all blocks on a table into a single tower, another task could be to place
all blocks on a table into two-block towers, etc. In each case, different
instances of the task would consist of different sets of blocks with different
initial states. At training time, our algorithm is presented with pairs of
demonstrations for a subset of all tasks. A neural net is trained that takes as
input one demonstration and the current state (which initially is the initial
state of the other demonstration of the pair), and outputs an action with the
goal that the resulting sequence of states and actions matches as closely as
possible with the second demonstration. At test time, a demonstration of a
single instance of a new task is presented, and the neural net is expected to
perform well on new instances of this new task. The use of soft attention
allows the model to generalize to conditions and tasks unseen in the training
data. We anticipate that by training this model on a much greater variety of
tasks and settings, we will obtain a general system that can turn any
demonstrations into robust policies that can accomplish an overwhelming variety
of tasks.
Videos available at https://bit.ly/nips2017-oneshot . | One-Shot Imitation Learning | 2,017 | http://arxiv.org/pdf/1703.07326v3 | Title OneShot Imitation Learning Summary Imitation learning commonly applied solve different task isolation usually requires either careful feature engineering significant number sample far desire ideally robot able learn demonstration given task instantly generalize new situation task without requiring taskspecific engineering paper propose metalearning framework achieving capability call oneshot imitation learning Specifically consider setting large set task task many instantiation example task could stack block table single tower another task could place block table twoblock tower etc case different instance task would consist different set block different initial state training time algorithm presented pair demonstration subset task neural net trained take input one demonstration current state initially initial state demonstration pair output action goal resulting sequence state action match closely possible second demonstration test time demonstration single instance new task presented neural net expected perform well new instance new task use soft attention allows model generalize condition task unseen training data anticipate training model much greater variety task setting obtain general system turn demonstration robust policy accomplish overwhelming variety task Videos available httpsbitlynips2017oneshot Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02619013376533985, 0.003925428260117769, -0.004163840785622597, 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824 | 824 | ['Shumeet Baluja'] | 1703.07394v1 | In all but the most trivial optimization problems, the structure of the
solutions exhibit complex interdependencies between the input parameters.
Decades of research with stochastic search techniques has shown the benefit of
explicitly modeling the interactions between sets of parameters and the overall
quality of the solutions discovered. We demonstrate a novel method, based on
learning deep networks, to model the global landscapes of optimization
problems. To represent the search space concisely and accurately, the deep
networks must encode information about the underlying parameter interactions
and their contributions to the quality of the solution. Once the networks are
trained, the networks are probed to reveal parameter combinations with high
expected performance with respect to the optimization task. These estimates are
used to initialize fast, randomized, local search algorithms, which in turn
expose more information about the search space that is subsequently used to
refine the models. We demonstrate the technique on multiple optimization
problems that have arisen in a variety of real-world domains, including:
packing, graphics, job scheduling, layout and compression. The problems include
combinatoric search spaces, discontinuous and highly non-linear spaces, and
span binary, higher-cardinality discrete, as well as continuous parameters.
Strengths, limitations, and extensions of the approach are extensively
discussed and demonstrated. | Deep Learning for Explicitly Modeling Optimization Landscapes | 2,017 | http://arxiv.org/pdf/1703.07394v1 | Title Deep Learning Explicitly Modeling Optimization Landscapes Summary trivial optimization problem structure solution exhibit complex interdependency input parameter Decades research stochastic search technique shown benefit explicitly modeling interaction set parameter overall quality solution discovered demonstrate novel method based learning deep network model global landscape optimization problem represent search space concisely accurately deep network must encode information underlying parameter interaction contribution quality solution network trained network probed reveal parameter combination high expected performance respect optimization task estimate used initialize fast randomized local search algorithm turn expose information search space subsequently used refine model demonstrate technique multiple optimization problem arisen variety realworld domain including packing graphic job scheduling layout compression problem include combinatoric search space discontinuous highly nonlinear space span binary highercardinality discrete well continuous parameter Strengths limitation extension approach extensively discussed demonstrated Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.010769948363304138, 0.0350692979991436, -0.03418678790330887, 0.027825459837913513, -0.011421464383602142, -0.0665297731757164, 0.013387186452746391, 0.00261049740947783, -0.08670773357152939, 0.004274922888725996, 0.008350112475454807, 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825 | 825 | ['Carlos Florensa', 'Yan Duan', 'Pieter Abbeel'] | 1704.03012v1 | Deep reinforcement learning has achieved many impressive results in recent
years. However, tasks with sparse rewards or long horizons continue to pose
significant challenges. To tackle these important problems, we propose a
general framework that first learns useful skills in a pre-training
environment, and then leverages the acquired skills for learning faster in
downstream tasks. Our approach brings together some of the strengths of
intrinsic motivation and hierarchical methods: the learning of useful skill is
guided by a single proxy reward, the design of which requires very minimal
domain knowledge about the downstream tasks. Then a high-level policy is
trained on top of these skills, providing a significant improvement of the
exploration and allowing to tackle sparse rewards in the downstream tasks. To
efficiently pre-train a large span of skills, we use Stochastic Neural Networks
combined with an information-theoretic regularizer. Our experiments show that
this combination is effective in learning a wide span of interpretable skills
in a sample-efficient way, and can significantly boost the learning performance
uniformly across a wide range of downstream tasks. | Stochastic Neural Networks for Hierarchical Reinforcement Learning | 2,017 | http://arxiv.org/pdf/1704.03012v1 | Title Stochastic Neural Networks Hierarchical Reinforcement Learning Summary Deep reinforcement learning achieved many impressive result recent year However task sparse reward long horizon continue pose significant challenge tackle important problem propose general framework first learns useful skill pretraining environment leverage acquired skill learning faster downstream task approach brings together strength intrinsic motivation hierarchical method learning useful skill guided single proxy reward design requires minimal domain knowledge downstream task highlevel policy trained top skill providing significant improvement exploration allowing tackle sparse reward downstream task efficiently pretrain large span skill use Stochastic Neural Networks combined informationtheoretic regularizer experiment show combination effective learning wide span interpretable skill sampleefficient way significantly boost learning performance uniformly across wide range downstream task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0013203145936131477, 0.04116049036383629, -0.004599382169544697, -0.008501452393829823, 0.022824062034487724, -0.02133704349398613, 0.023822534829378128, -0.008480294607579708, -0.051138367503881454, 0.02041446790099144, -0.06494960933923721, 0.0006839332054369152, 0.0030479920096695423, 0.07526356726884842, 0.020694291219115257, -0.10797673463821411, 0.029820766299962997, 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826 | 826 | ['Daniel Hein', 'Steffen Udluft', 'Michel Tokic', 'Alexander Hentschel', 'Thomas A. Runkler', 'Volkmar Sterzing'] | 1705.07262v2 | The Particle Swarm Optimization Policy (PSO-P) has been recently introduced
and proven to produce remarkable results on interacting with academic
reinforcement learning benchmarks in an off-policy, batch-based setting. To
further investigate the properties and feasibility on real-world applications,
this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a
novel reinforcement learning (RL) benchmark that aims at being realistic by
including a variety of aspects found in industrial applications, like
continuous state and action spaces, a high dimensional, partially observable
state space, delayed effects, and complex stochasticity. The experimental
results of PSO-P on IB are compared to results of closed-form control policies
derived from the model-based Recurrent Control Neural Network (RCNN) and the
model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not
only of interest for academic benchmarks, but also for real-world industrial
applications, since it also yielded the best performing policy in our IB
setting. Compared to other well established RL techniques, PSO-P produced
outstanding results in performance and robustness, requiring only a relatively
low amount of effort in finding adequate parameters or making complex design
decisions. | Batch Reinforcement Learning on the Industrial Benchmark: First
Experiences | 2,017 | http://arxiv.org/pdf/1705.07262v2 | Title Batch Reinforcement Learning Industrial Benchmark First Experiences Summary Particle Swarm Optimization Policy PSOP recently introduced proven produce remarkable result interacting academic reinforcement learning benchmark offpolicy batchbased setting investigate property feasibility realworld application paper investigates PSOP socalled Industrial Benchmark IB novel reinforcement learning RL benchmark aim realistic including variety aspect found industrial application like continuous state action space high dimensional partially observable state space delayed effect complex stochasticity experimental result PSOP IB compared result closedform control policy derived modelbased Recurrent Control Neural Network RCNN modelfree Neural Fitted QIteration NFQ Experiments show PSOP interest academic benchmark also realworld industrial application since also yielded best performing policy IB setting Compared well established RL technique PSOP produced outstanding result performance robustness requiring relatively low amount effort finding adequate parameter making complex design decision Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04859529808163643, 0.009126704186201096, 0.004413751419633627, -0.036701805889606476, -0.009103127755224705, -0.053609319031238556, 0.0009935915004462004, 0.02723786048591137, -0.0376310721039772, 0.0017250364180654287, 0.024324486032128334, 0.02293894998729229, 0.003281214740127325, 0.029910871759057045, 0.014424520544707775, -0.023479795083403587, 0.0518496111035347, 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827 | 827 | ['Tim Rocktäschel', 'Sebastian Riedel'] | 1705.11040v2 | We introduce neural networks for end-to-end differentiable proving of queries
to knowledge bases by operating on dense vector representations of symbols.
These neural networks are constructed recursively by taking inspiration from
the backward chaining algorithm as used in Prolog. Specifically, we replace
symbolic unification with a differentiable computation on vector
representations of symbols using a radial basis function kernel, thereby
combining symbolic reasoning with learning subsymbolic vector representations.
By using gradient descent, the resulting neural network can be trained to infer
facts from a given incomplete knowledge base. It learns to (i) place
representations of similar symbols in close proximity in a vector space, (ii)
make use of such similarities to prove queries, (iii) induce logical rules, and
(iv) use provided and induced logical rules for multi-hop reasoning. We
demonstrate that this architecture outperforms ComplEx, a state-of-the-art
neural link prediction model, on three out of four benchmark knowledge bases
while at the same time inducing interpretable function-free first-order logic
rules. | End-to-End Differentiable Proving | 2,017 | http://arxiv.org/pdf/1705.11040v2 | Title EndtoEnd Differentiable Proving Summary introduce neural network endtoend differentiable proving query knowledge base operating dense vector representation symbol neural network constructed recursively taking inspiration backward chaining algorithm used Prolog Specifically replace symbolic unification differentiable computation vector representation symbol using radial basis function kernel thereby combining symbolic reasoning learning subsymbolic vector representation using gradient descent resulting neural network trained infer fact given incomplete knowledge base learns place representation similar symbol close proximity vector space ii make use similarity prove query iii induce logical rule iv use provided induced logical rule multihop reasoning demonstrate architecture outperforms ComplEx stateoftheart neural link prediction model three four benchmark knowledge base time inducing interpretable functionfree firstorder logic rule Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.00876219104975462, 0.0424051396548748, -0.019259411841630936, 0.040070559829473495, -0.024717167019844055, -0.019320471212267876, 0.026828352361917496, 0.020608365535736084, -0.009044548496603966, 0.003754157805815339, 0.0062873768620193005, 0.061944689601659775, -0.0130495959892869, 0.041643187403678894, 0.0214371420443058, -0.04178289324045181, 0.03528797999024391, 0.019480587914586067, 0.006906518712639809, -0.01037841010838747, -0.01077441219240427, -0.025250203907489777, 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828 | 828 | ['Ryan Lowe', 'Yi Wu', 'Aviv Tamar', 'Jean Harb', 'Pieter Abbeel', 'Igor Mordatch'] | 1706.02275v3 | We explore deep reinforcement learning methods for multi-agent domains. We
begin by analyzing the difficulty of traditional algorithms in the multi-agent
case: Q-learning is challenged by an inherent non-stationarity of the
environment, while policy gradient suffers from a variance that increases as
the number of agents grows. We then present an adaptation of actor-critic
methods that considers action policies of other agents and is able to
successfully learn policies that require complex multi-agent coordination.
Additionally, we introduce a training regimen utilizing an ensemble of policies
for each agent that leads to more robust multi-agent policies. We show the
strength of our approach compared to existing methods in cooperative as well as
competitive scenarios, where agent populations are able to discover various
physical and informational coordination strategies. | Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments | 2,017 | http://arxiv.org/pdf/1706.02275v3 | Title MultiAgent ActorCritic Mixed CooperativeCompetitive Environments Summary explore deep reinforcement learning method multiagent domain begin analyzing difficulty traditional algorithm multiagent case Qlearning challenged inherent nonstationarity environment policy gradient suffers variance increase number agent grows present adaptation actorcritic method considers action policy agent able successfully learn policy require complex multiagent coordination Additionally introduce training regimen utilizing ensemble policy agent lead robust multiagent policy show strength approach compared existing method cooperative well competitive scenario agent population able discover various physical informational coordination strategy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.014611853286623955, 0.02163694240152836, -0.022250503301620483, -0.018286388367414474, -0.019145065918564796, -0.006084064487367868, 0.04140058159828186, -0.05955472216010094, -0.03481706976890564, 0.007267179433256388, -0.03561222925782204, -0.021177997812628746, -0.03134753555059433, 0.023660384118556976, -0.0054882001131772995, -0.02644454315304756, 0.0003178852784913033, -0.002969603519886732, 0.013359476812183857, -0.03613879159092903, 0.000655788229778409, -0.09506961703300476, -0.0022706729359924793, 0.036847785115242004, -0.03348102048039436, -0.00021486963669303805, -0.0590948686003685, -0.02564220316708088, 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829 | 829 | ['Joan Serrà', 'Alexandros Karatzoglou'] | 1706.03993v1 | Recommendation algorithms that incorporate techniques from deep learning are
becoming increasingly popular. Due to the structure of the data coming from
recommendation domains (i.e., one-hot-encoded vectors of item preferences),
these algorithms tend to have large input and output dimensionalities that
dominate their overall size. This makes them difficult to train, due to the
limited memory of graphical processing units, and difficult to deploy on mobile
devices with limited hardware. To address these difficulties, we propose Bloom
embeddings, a compression technique that can be applied to the input and output
of neural network models dealing with sparse high-dimensional binary-coded
instances. Bloom embeddings are computationally efficient, and do not seriously
compromise the accuracy of the model up to 1/5 compression ratios. In some
cases, they even improve over the original accuracy, with relative increases up
to 12%. We evaluate Bloom embeddings on 7 data sets and compare it against 4
alternative methods, obtaining favorable results. We also discuss a number of
further advantages of Bloom embeddings, such as 'on-the-fly' constant-time
operation, zero or marginal space requirements, training time speedups, or the
fact that they do not require any change to the core model architecture or
training configuration. | Getting deep recommenders fit: Bloom embeddings for sparse binary
input/output networks | 2,017 | http://arxiv.org/pdf/1706.03993v1 | Title Getting deep recommenders fit Bloom embeddings sparse binary inputoutput network Summary Recommendation algorithm incorporate technique deep learning becoming increasingly popular Due structure data coming recommendation domain ie onehotencoded vector item preference algorithm tend large input output dimensionality dominate overall size make difficult train due limited memory graphical processing unit difficult deploy mobile device limited hardware address difficulty propose Bloom embeddings compression technique applied input output neural network model dealing sparse highdimensional binarycoded instance Bloom embeddings computationally efficient seriously compromise accuracy model 15 compression ratio case even improve original accuracy relative increase 12 evaluate Bloom embeddings 7 data set compare 4 alternative method obtaining favorable result also discus number advantage Bloom embeddings onthefly constanttime operation zero marginal space requirement training time speedup fact require change core model architecture training configuration Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.012883339077234268, -0.009338920004665852, -0.01198730431497097, 0.009027131833136082, -0.002386106178164482, -0.04708776995539665, 0.05819787085056305, 0.016367528587579727, 0.010520237497985363, -0.018471479415893555, 0.008583184331655502, -0.014873209409415722, -0.009784399531781673, 0.12243649363517761, 0.010296443477272987, -0.004346174653619528, 0.02815733104944229, 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830 | 830 | ['Jinzhuo Wang', 'Wenmin Wang', 'Ronggang Wang', 'Wen Gao'] | 1706.04052v1 | Monte Carlo tree search (MCTS) is extremely popular in computer Go which
determines each action by enormous simulations in a broad and deep search tree.
However, human experts select most actions by pattern analysis and careful
evaluation rather than brute search of millions of future nteractions. In this
paper, we propose a computer Go system that follows experts way of thinking and
playing. Our system consists of two parts. The first part is a novel deep
alternative neural network (DANN) used to generate candidates of next move.
Compared with existing deep convolutional neural network (DCNN), DANN inserts
recurrent layer after each convolutional layer and stacks them in an
alternative manner. We show such setting can preserve more contexts of local
features and its evolutions which are beneficial for move prediction. The
second part is a long-term evaluation (LTE) module used to provide a reliable
evaluation of candidates rather than a single probability from move predictor.
This is consistent with human experts nature of playing since they can foresee
tens of steps to give an accurate estimation of candidates. In our system, for
each candidate, LTE calculates a cumulative reward after several future
interactions when local variations are settled. Combining criteria from the two
parts, our system determines the optimal choice of next move. For more
comprehensive experiments, we introduce a new professional Go dataset (PGD),
consisting of 253233 professional records. Experiments on GoGoD and PGD
datasets show the DANN can substantially improve performance of move prediction
over pure DCNN. When combining LTE, our system outperforms most relevant
approaches and open engines based on MCTS. | Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural
Network and Long-Term Evaluation | 2,017 | http://arxiv.org/pdf/1706.04052v1 | Title Beyond Monte Carlo Tree Search Playing Go Deep Alternative Neural Network LongTerm Evaluation Summary Monte Carlo tree search MCTS extremely popular computer Go determines action enormous simulation broad deep search tree However human expert select action pattern analysis careful evaluation rather brute search million future nteractions paper propose computer Go system follows expert way thinking playing system consists two part first part novel deep alternative neural network DANN used generate candidate next move Compared existing deep convolutional neural network DCNN DANN insert recurrent layer convolutional layer stack alternative manner show setting preserve context local feature evolution beneficial move prediction second part longterm evaluation LTE module used provide reliable evaluation candidate rather single probability move predictor consistent human expert nature playing since foresee ten step give accurate estimation candidate system candidate LTE calculates cumulative reward several future interaction local variation settled Combining criterion two part system determines optimal choice next move comprehensive experiment introduce new professional Go dataset PGD consisting 253233 professional record Experiments GoGoD PGD datasets show DANN substantially improve performance move prediction pure DCNN combining LTE system outperforms relevant approach open engine based MCTS Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.018440835177898407, 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831 | 831 | ['Marcin Andrychowicz', 'Filip Wolski', 'Alex Ray', 'Jonas Schneider', 'Rachel Fong', 'Peter Welinder', 'Bob McGrew', 'Josh Tobin', 'Pieter Abbeel', 'Wojciech Zaremba'] | 1707.01495v3 | Dealing with sparse rewards is one of the biggest challenges in Reinforcement
Learning (RL). We present a novel technique called Hindsight Experience Replay
which allows sample-efficient learning from rewards which are sparse and binary
and therefore avoid the need for complicated reward engineering. It can be
combined with an arbitrary off-policy RL algorithm and may be seen as a form of
implicit curriculum.
We demonstrate our approach on the task of manipulating objects with a
robotic arm. In particular, we run experiments on three different tasks:
pushing, sliding, and pick-and-place, in each case using only binary rewards
indicating whether or not the task is completed. Our ablation studies show that
Hindsight Experience Replay is a crucial ingredient which makes training
possible in these challenging environments. We show that our policies trained
on a physics simulation can be deployed on a physical robot and successfully
complete the task. | Hindsight Experience Replay | 2,017 | http://arxiv.org/pdf/1707.01495v3 | Title Hindsight Experience Replay Summary Dealing sparse reward one biggest challenge Reinforcement Learning RL present novel technique called Hindsight Experience Replay allows sampleefficient learning reward sparse binary therefore avoid need complicated reward engineering combined arbitrary offpolicy RL algorithm may seen form implicit curriculum demonstrate approach task manipulating object robotic arm particular run experiment three different task pushing sliding pickandplace case using binary reward indicating whether task completed ablation study show Hindsight Experience Replay crucial ingredient make training possible challenging environment show policy trained physic simulation deployed physical robot successfully complete task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.01251895260065794, -0.006217009387910366, 0.008013303391635418, -0.048185672610998154, -0.024119410663843155, -0.022304944694042206, 0.00254358584061265, 0.012106338515877724, -0.0323614664375782, -0.0015307181747630239, -0.002653340343385935, 0.03844265267252922, -0.033091168850660324, 0.04591533914208412, -0.023032983765006065, -0.06660851836204529, 0.028972769156098366, 0.010286383330821991, -0.014311402104794979, -0.004205194301903248, 0.0010529370047152042, -0.06340071558952332, -0.038558974862098694, 0.025354430079460144, -0.029800163581967354, 0.013725983910262585, -0.07669448107481003, 0.005176165606826544, 0.02631739154458046, -0.0820075273513794, 0.033139072358608246, 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832 | 832 | ['William Saunders', 'Girish Sastry', 'Andreas Stuhlmueller', 'Owain Evans'] | 1707.05173v1 | AI systems are increasingly applied to complex tasks that involve interaction
with humans. During training, such systems are potentially dangerous, as they
haven't yet learned to avoid actions that could cause serious harm. How can an
AI system explore and learn without making a single mistake that harms humans
or otherwise causes serious damage? For model-free reinforcement learning,
having a human "in the loop" and ready to intervene is currently the only way
to prevent all catastrophes. We formalize human intervention for RL and show
how to reduce the human labor required by training a supervised learner to
imitate the human's intervention decisions. We evaluate this scheme on Atari
games, with a Deep RL agent being overseen by a human for four hours. When the
class of catastrophes is simple, we are able to prevent all catastrophes
without affecting the agent's learning (whereas an RL baseline fails due to
catastrophic forgetting). However, this scheme is less successful when
catastrophes are more complex: it reduces but does not eliminate catastrophes
and the supervised learner fails on adversarial examples found by the agent.
Extrapolating to more challenging environments, we show that our implementation
would not scale (due to the infeasible amount of human labor required). We
outline extensions of the scheme that are necessary if we are to train
model-free agents without a single catastrophe. | Trial without Error: Towards Safe Reinforcement Learning via Human
Intervention | 2,017 | http://arxiv.org/pdf/1707.05173v1 | Title Trial without Error Towards Safe Reinforcement Learning via Human Intervention Summary AI system increasingly applied complex task involve interaction human training system potentially dangerous havent yet learned avoid action could cause serious harm AI system explore learn without making single mistake harm human otherwise cause serious damage modelfree reinforcement learning human loop ready intervene currently way prevent catastrophe formalize human intervention RL show reduce human labor required training supervised learner imitate human intervention decision evaluate scheme Atari game Deep RL agent overseen human four hour class catastrophe simple able prevent catastrophe without affecting agent learning whereas RL baseline fails due catastrophic forgetting However scheme le successful catastrophe complex reduces eliminate catastrophe supervised learner fails adversarial example found agent Extrapolating challenging environment show implementation would scale due infeasible amount human labor required outline extension scheme necessary train modelfree agent without single catastrophe Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.016132855787873268, 0.04305306077003479, -0.005523810163140297, -0.06343850493431091, 0.003946242388337851, 0.03117883950471878, -1.495579181209905e-05, -0.007241299841552973, -1.276238435821142e-06, 0.02175363525748253, 0.02454415149986744, 0.05398100987076759, -0.042051032185554504, 0.0768401250243187, 0.029091153293848038, -0.04368329420685768, 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833 | 833 | ['Carlos Florensa', 'David Held', 'Markus Wulfmeier', 'Michael Zhang', 'Pieter Abbeel'] | 1707.05300v2 | Many relevant tasks require an agent to reach a certain state, or to
manipulate objects into a desired configuration. For example, we might want a
robot to align and assemble a gear onto an axle or insert and turn a key in a
lock. These goal-oriented tasks present a considerable challenge for
reinforcement learning, since their natural reward function is sparse and
prohibitive amounts of exploration are required to reach the goal and receive
some learning signal. Past approaches tackle these problems by exploiting
expert demonstrations or by manually designing a task-specific reward shaping
function to guide the learning agent. Instead, we propose a method to learn
these tasks without requiring any prior knowledge other than obtaining a single
state in which the task is achieved. The robot is trained in reverse, gradually
learning to reach the goal from a set of start states increasingly far from the
goal. Our method automatically generates a curriculum of start states that
adapts to the agent's performance, leading to efficient training on
goal-oriented tasks. We demonstrate our approach on difficult simulated
navigation and fine-grained manipulation problems, not solvable by
state-of-the-art reinforcement learning methods. | Reverse Curriculum Generation for Reinforcement Learning | 2,017 | http://arxiv.org/pdf/1707.05300v2 | Title Reverse Curriculum Generation Reinforcement Learning Summary Many relevant task require agent reach certain state manipulate object desired configuration example might want robot align assemble gear onto axle insert turn key lock goaloriented task present considerable challenge reinforcement learning since natural reward function sparse prohibitive amount exploration required reach goal receive learning signal Past approach tackle problem exploiting expert demonstration manually designing taskspecific reward shaping function guide learning agent Instead propose method learn task without requiring prior knowledge obtaining single state task achieved robot trained reverse gradually learning reach goal set start state increasingly far goal method automatically generates curriculum start state adapts agent performance leading efficient training goaloriented task demonstrate approach difficult simulated navigation finegrained manipulation problem solvable stateoftheart reinforcement learning method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.02395312860608101, -0.03560492396354675, -0.022230951115489006, -0.027542339637875557, -0.009401685558259487, -0.01566183567047119, -0.00923572201281786, -0.021622776985168457, -0.03852638974785805, -0.0009287800639867783, -0.02003348246216774, 0.06214943900704384, -0.010654082521796227, 0.04130471497774124, -0.007070759776979685, -0.08501944690942764, 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834 | 834 | ['S. Hossein Hosseini', 'Afshin Ebrahimi'] | 1707.06992v2 | A population-based optimization algorithm was designed, inspired by two main
thinking modes in philosophy, both based on dialectic concept and
thesis-antithesis paradigm. They impose two different kinds of dialectics.
Idealistic and materialistic antitheses are formulated as optimization models.
Based on the models, the population is coordinated for dialectical
interactions. At the population-based context, the formulated optimization
models are reduced to a simple detection problem for each thinker (particle).
According to the assigned thinking mode to each thinker and her/his
measurements of corresponding dialectic with other candidate particles, they
deterministically decide to interact with a thinker in maximum dialectic with
their theses. The position of a thinker at maximum dialectic is known as an
available antithesis among the existing solutions. The dialectical interactions
at each ideological community are distinguished by meaningful distributions of
step-sizes for each thinking mode. In fact, the thinking modes are regarded as
exploration and exploitation elements of the proposed algorithm. The result is
a delicate balance without any requirement for adjustment of step-size
coefficients. Main parameter of the proposed algorithm is the number of
particles appointed to each thinking modes, or equivalently for each kind of
motions. An additional integer parameter is defined to boost the stability of
the final algorithm in some particular problems. The proposed algorithm is
evaluated by a testbed of 12 single-objective continuous benchmark functions.
Moreover, its performance and speed were highlighted in sparse reconstruction
and antenna selection problems, at the context of compressed sensing and
massive MIMO, respectively. The results indicate fast and efficient performance
in comparison with well-known evolutionary algorithms and dedicated
state-of-the-art algorithms. | Ideological Sublations: Resolution of Dialectic in Population-based
Optimization | 2,017 | http://arxiv.org/pdf/1707.06992v2 | Title Ideological Sublations Resolution Dialectic Populationbased Optimization Summary populationbased optimization algorithm designed inspired two main thinking mode philosophy based dialectic concept thesisantithesis paradigm impose two different kind dialectic Idealistic materialistic antithesis formulated optimization model Based model population coordinated dialectical interaction populationbased context formulated optimization model reduced simple detection problem thinker particle According assigned thinking mode thinker herhis measurement corresponding dialectic candidate particle deterministically decide interact thinker maximum dialectic thesis position thinker maximum dialectic known available antithesis among existing solution dialectical interaction ideological community distinguished meaningful distribution stepsizes thinking mode fact thinking mode regarded exploration exploitation element proposed algorithm result delicate balance without requirement adjustment stepsize coefficient Main parameter proposed algorithm number particle appointed thinking mode equivalently kind motion additional integer parameter defined boost stability final algorithm particular problem proposed algorithm evaluated testbed 12 singleobjective continuous benchmark function Moreover performance speed highlighted sparse reconstruction antenna selection problem context compressed sensing massive MIMO respectively result indicate fast efficient performance comparison wellknown evolutionary algorithm dedicated stateoftheart algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 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835 | 835 | ['Sujith Ravi'] | 1708.00630v2 | Deep neural networks have become ubiquitous for applications related to
visual recognition and language understanding tasks. However, it is often
prohibitive to use typical neural networks on devices like mobile phones or
smart watches since the model sizes are huge and cannot fit in the limited
memory available on such devices. While these devices could make use of machine
learning models running on high-performance data centers with CPUs or GPUs,
this is not feasible for many applications because data can be privacy
sensitive and inference needs to be performed directly "on" device.
We introduce a new architecture for training compact neural networks using a
joint optimization framework. At its core lies a novel objective that jointly
trains using two different types of networks--a full trainer neural network
(using existing architectures like Feed-forward NNs or LSTM RNNs) combined with
a simpler "projection" network that leverages random projections to transform
inputs or intermediate representations into bits. The simpler network encodes
lightweight and efficient-to-compute operations in bit space with a low memory
footprint. The two networks are trained jointly using backpropagation, where
the projection network learns from the full network similar to apprenticeship
learning. Once trained, the smaller network can be used directly for inference
at low memory and computation cost. We demonstrate the effectiveness of the new
approach at significantly shrinking the memory requirements of different types
of neural networks while preserving good accuracy on visual recognition and
text classification tasks. We also study the question "how many neural bits are
required to solve a given task?" using the new framework and show empirical
results contrasting model predictive capacity (in bits) versus accuracy on
several datasets. | ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural
Projections | 2,017 | http://arxiv.org/pdf/1708.00630v2 | Title ProjectionNet Learning Efficient OnDevice Deep Networks Using Neural Projections Summary Deep neural network become ubiquitous application related visual recognition language understanding task However often prohibitive use typical neural network device like mobile phone smart watch since model size huge cannot fit limited memory available device device could make use machine learning model running highperformance data center CPUs GPUs feasible many application data privacy sensitive inference need performed directly device introduce new architecture training compact neural network using joint optimization framework core lie novel objective jointly train using two different type networksa full trainer neural network using existing architecture like Feedforward NNs LSTM RNNs combined simpler projection network leverage random projection transform input intermediate representation bit simpler network encodes lightweight efficienttocompute operation bit space low memory footprint two network trained jointly using backpropagation projection network learns full network similar apprenticeship learning trained smaller network used directly inference low memory computation cost demonstrate effectiveness new approach significantly shrinking memory requirement different type neural network preserving good accuracy visual recognition text classification task also study question many neural bit required solve given task using new framework show empirical result contrasting model predictive capacity bit versus accuracy several datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David 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836 | 836 | ['Zhen Li', 'Zuoqiang Shi'] | 1708.06257v2 | Based on a natural connection between ResNet and transport equation or its
characteristic equation, we propose a continuous flow model for both ResNet and
plain net. Through this continuous model, a ResNet can be explicitly
constructed as a refinement of a plain net. The flow model provides an
alternative perspective to understand phenomena in deep neural networks, such
as why it is necessary and sufficient to use 2-layer blocks in ResNets, why
deeper is better, and why ResNets are even deeper, and so on. It also opens a
gate to bring in more tools from the huge area of differential equations. | A Flow Model of Neural Networks | 2,017 | http://arxiv.org/pdf/1708.06257v2 | Title Flow Model Neural Networks Summary Based natural connection ResNet transport equation characteristic equation propose continuous flow model ResNet plain net continuous model ResNet explicitly constructed refinement plain net flow model provides alternative perspective understand phenomenon deep neural network necessary sufficient use 2layer block ResNets deeper better ResNets even deeper also open gate bring tool huge area differential equation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 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837 | 837 | ['Nikhita Vedula', 'Wei Sun', 'Hyunhwan Lee', 'Harsh Gupta', 'Mitsunori Ogihara', 'Joseph Johnson', 'Gang Ren', 'Srinivasan Parthasarathy'] | 1709.03946v1 | The rapid advances in e-commerce and Web 2.0 technologies have greatly
increased the impact of commercial advertisements on the general public. As a
key enabling technology, a multitude of recommender systems exists which
analyzes user features and browsing patterns to recommend appealing
advertisements to users. In this work, we seek to study the characteristics or
attributes that characterize an effective advertisement and recommend a useful
set of features to aid the designing and production processes of commercial
advertisements. We analyze the temporal patterns from multimedia content of
advertisement videos including auditory, visual and textual components, and
study their individual roles and synergies in the success of an advertisement.
The objective of this work is then to measure the effectiveness of an
advertisement, and to recommend a useful set of features to advertisement
designers to make it more successful and approachable to users. Our proposed
framework employs the signal processing technique of cross modality feature
learning where data streams from different components are employed to train
separate neural network models and are then fused together to learn a shared
representation. Subsequently, a neural network model trained on this joint
feature embedding representation is utilized as a classifier to predict
advertisement effectiveness. We validate our approach using subjective ratings
from a dedicated user study, the sentiment strength of online viewer comments,
and a viewer opinion metric of the ratio of the Likes and Views received by
each advertisement from an online platform. | Multimodal Content Analysis for Effective Advertisements on YouTube | 2,017 | http://arxiv.org/pdf/1709.03946v1 | Title Multimodal Content Analysis Effective Advertisements YouTube Summary rapid advance ecommerce Web 20 technology greatly increased impact commercial advertisement general public key enabling technology multitude recommender system exists analyzes user feature browsing pattern recommend appealing advertisement user work seek study characteristic attribute characterize effective advertisement recommend useful set feature aid designing production process commercial advertisement analyze temporal pattern multimedia content advertisement video including auditory visual textual component study individual role synergy success advertisement objective work measure effectiveness advertisement recommend useful set feature advertisement designer make successful approachable user proposed framework employ signal processing technique cross modality feature learning data stream different component employed train separate neural network model fused together learn shared representation Subsequently neural network model trained joint feature embedding representation utilized classifier predict advertisement effectiveness validate approach using subjective rating dedicated user study sentiment strength online viewer comment viewer opinion metric ratio Likes Views received advertisement online platform Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05325677990913391, 0.01905878633260727, -0.04145479202270508, 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838 | 838 | ['Ashvin Nair', 'Bob McGrew', 'Marcin Andrychowicz', 'Wojciech Zaremba', 'Pieter Abbeel'] | 1709.10089v2 | Exploration in environments with sparse rewards has been a persistent problem
in reinforcement learning (RL). Many tasks are natural to specify with a sparse
reward, and manually shaping a reward function can result in suboptimal
performance. However, finding a non-zero reward is exponentially more difficult
with increasing task horizon or action dimensionality. This puts many
real-world tasks out of practical reach of RL methods. In this work, we use
demonstrations to overcome the exploration problem and successfully learn to
perform long-horizon, multi-step robotics tasks with continuous control such as
stacking blocks with a robot arm. Our method, which builds on top of Deep
Deterministic Policy Gradients and Hindsight Experience Replay, provides an
order of magnitude of speedup over RL on simulated robotics tasks. It is simple
to implement and makes only the additional assumption that we can collect a
small set of demonstrations. Furthermore, our method is able to solve tasks not
solvable by either RL or behavior cloning alone, and often ends up
outperforming the demonstrator policy. | Overcoming Exploration in Reinforcement Learning with Demonstrations | 2,017 | http://arxiv.org/pdf/1709.10089v2 | Title Overcoming Exploration Reinforcement Learning Demonstrations Summary Exploration environment sparse reward persistent problem reinforcement learning RL Many task natural specify sparse reward manually shaping reward function result suboptimal performance However finding nonzero reward exponentially difficult increasing task horizon action dimensionality put many realworld task practical reach RL method work use demonstration overcome exploration problem successfully learn perform longhorizon multistep robotics task continuous control stacking block robot arm method build top Deep Deterministic Policy Gradients Hindsight Experience Replay provides order magnitude speedup RL simulated robotics task simple implement make additional assumption collect small set demonstration Furthermore method able solve task solvable either RL behavior cloning alone often end outperforming demonstrator policy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.000670247187372297, 0.007406280376017094, 0.008679671213030815, -0.04938372224569321, -0.01916898600757122, -0.008341330103576183, -0.0046310280449688435, -0.032446566969156265, -0.055457938462495804, 0.03828710317611694, -0.02534225583076477, 0.06884787976741791, -0.0331156887114048, 0.07698538899421692, -0.000784434552770108, -0.05323542281985283, 0.04298918694257736, -0.027173612266778946, -0.02702045440673828, -0.0029392472933977842, -0.012632811442017555, 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839 | 839 | ['Chaitanya Ahuja', 'Louis-Philippe Morency'] | 1710.02254v2 | Recurrent neural networks have shown remarkable success in modeling
sequences. However low resource situations still adversely affect the
generalizability of these models. We introduce a new family of models, called
Lattice Recurrent Units (LRU), to address the challenge of learning deep
multi-layer recurrent models with limited resources. LRU models achieve this
goal by creating distinct (but coupled) flow of information inside the units: a
first flow along time dimension and a second flow along depth dimension. It
also offers a symmetry in how information can flow horizontally and vertically.
We analyze the effects of decoupling three different components of our LRU
model: Reset Gate, Update Gate and Projected State. We evaluate this family on
new LRU models on computational convergence rates and statistical efficiency.
Our experiments are performed on four publicly-available datasets, comparing
with Grid-LSTM and Recurrent Highway networks. Our results show that LRU has
better empirical computational convergence rates and statistical efficiency
values, along with learning more accurate language models. | Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency
for Sequence Modeling | 2,017 | http://arxiv.org/pdf/1710.02254v2 | Title Lattice Recurrent Unit Improving Convergence Statistical Efficiency Sequence Modeling Summary Recurrent neural network shown remarkable success modeling sequence However low resource situation still adversely affect generalizability model introduce new family model called Lattice Recurrent Units LRU address challenge learning deep multilayer recurrent model limited resource LRU model achieve goal creating distinct coupled flow information inside unit first flow along time dimension second flow along depth dimension also offer symmetry information flow horizontally vertically analyze effect decoupling three different component LRU model Reset Gate Update Gate Projected State evaluate family new LRU model computational convergence rate statistical efficiency experiment performed four publiclyavailable datasets comparing GridLSTM Recurrent Highway network result show LRU better empirical computational convergence rate statistical efficiency value along learning accurate language model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02703038603067398, 0.056324027478694916, -0.019291691482067108, 0.06274662911891937, -0.03949630632996559, -0.0014617762062698603, 0.028200378641486168, -0.00469781132414937, -0.07082455605268478, -0.008138565346598625, 0.022258136421442032, -0.07556074857711792, 0.033635951578617096, 0.027700180187821388, 0.006997247226536274, -0.06000665947794914, -0.0078048561699688435, -0.010349584743380547, -0.021804116666316986, 0.01004141103476286, 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840 | 840 | ['Matthew Riemer', 'Tim Klinger', 'Michele Franceschini', 'Djallel Bouneffouf'] | 1711.06761v3 | Given the recent success of Deep Learning applied to a variety of single
tasks, it is natural to consider more human-realistic settings. Perhaps the
most difficult of these settings is that of continual lifelong learning, where
the model must learn online over a continuous stream of non-stationary data. A
continual lifelong learning system must have three primary capabilities to
succeed: it must learn and adapt over time, it must not forget what it has
learned, and it must be efficient in both training time and memory. Recent
techniques have focused their efforts largely on the first two capabilities
while the third capability remains largely unexplored. In this paper, we
consider the problem of efficient and effective storage of experiences over
very large time-frames. In particular we consider the case where typical
experiences are n bits and memories are limited to k bits for k << n. We
present a novel scalable architecture and training algorithm in this
challenging domain and provide an extensive evaluation of its performance. Our
results show that we can achieve considerable gains on top of state-of-the-art
methods such as GEM. | Scalable Recollections for Continual Lifelong Learning | 2,017 | http://arxiv.org/pdf/1711.06761v3 | Title Scalable Recollections Continual Lifelong Learning Summary Given recent success Deep Learning applied variety single task natural consider humanrealistic setting Perhaps difficult setting continual lifelong learning model must learn online continuous stream nonstationary data continual lifelong learning system must three primary capability succeed must learn adapt time must forget learned must efficient training time memory Recent technique focused effort largely first two capability third capability remains largely unexplored paper consider problem efficient effective storage experience large timeframes particular consider case typical experience n bit memory limited k bit k n present novel scalable architecture training algorithm challenging domain provide extensive evaluation performance result show achieve considerable gain top stateoftheart method GEM Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.02174822986125946, 0.05593891069293022, -0.015595111064612865, 0.010836074128746986, -0.020778335630893707, 0.005336886737495661, 0.04968583956360817, -0.042867522686719894, -0.022607678547501564, -0.007372090592980385, -0.0037958917673677206, -0.03462539613246918, -0.06478039175271988, 0.09908340126276016, -0.02315066009759903, -0.031163660809397697, 0.037444666028022766, 0.0096282958984375, -0.040815889835357666, 0.004170771688222885, 0.03402071073651314, -0.042404115200042725, -0.009299127385020256, -0.01385445799678564, 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841 | 841 | ['Davide Bacciu'] | 1711.07784v1 | The paper introduces the Hidden Tree Markov Network (HTN), a
neuro-probabilistic hybrid fusing the representation power of generative models
for trees with the incremental and discriminative learning capabilities of
neural networks. We put forward a modular architecture in which multiple
generative models of limited complexity are trained to learn structural feature
detectors whose outputs are then combined and integrated by neural layers at a
later stage. In this respect, the model is both deep, thanks to the unfolding
of the generative models on the input structures, as well as wide, given the
potentially large number of generative modules that can be trained in parallel.
Experimental results show that the proposed approach can outperform
state-of-the-art syntactic kernels as well as generative kernels built on the
same probabilistic model as the HTN. | Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data | 2,017 | http://arxiv.org/pdf/1711.07784v1 | Title Hidden Tree Markov Networks Deep Wide Learning Structured Data Summary paper introduces Hidden Tree Markov Network HTN neuroprobabilistic hybrid fusing representation power generative model tree incremental discriminative learning capability neural network put forward modular architecture multiple generative model limited complexity trained learn structural feature detector whose output combined integrated neural layer later stage respect model deep thanks unfolding generative model input structure well wide given potentially large number generative module trained parallel Experimental result show proposed approach outperform stateoftheart syntactic kernel well generative kernel built probabilistic model HTN Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.011593655683100224, 0.006323219742625952, 0.025108326226472855, 0.05469093099236488, -0.0006732799229212105, 0.027713587507605553, -0.053613532334566116, 0.0014808099949732423, -0.013125276193022728, -0.07022825628519058, -0.0016904890071600676, -0.006906906142830849, 0.01242598332464695, 0.08071021735668182, -0.015292052179574966, -0.06889525800943375, 0.031211823225021362, -0.010519251227378845, 0.028653284534811974, 0.020659685134887695, 0.016255857422947884, 0.014790249057114124, -0.035495512187480927, 0.03480986878275871, -0.08504197001457214, -0.03725786879658699, -0.01748443953692913, -0.0037924766074866056, -0.025352001190185547, 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842 | 842 | ['Andrew Levy', 'Robert Platt', 'Kate Saenko'] | 1712.00948v3 | The ability to learn at different resolutions in time may help overcome one
of the main challenges in deep reinforcement learning -- sample efficiency.
Hierarchical agents that operate at different levels of temporal abstraction
can learn tasks more quickly because they can divide the work of learning
behaviors among multiple policies and can also explore the environment at a
higher level. In this paper, we present a novel approach to hierarchical
reinforcement learning called Hierarchical Actor-Critic (HAC) that enables
agents to learn to break down problems involving continuous action spaces into
simpler subproblems belonging to different time scales. HAC has two key
advantages over most existing hierarchical learning methods: (i) the potential
for faster learning as agents learn short policies at each level of the
hierarchy and (ii) an end-to-end approach. We demonstrate that HAC
significantly accelerates learning in a series of tasks that require behavior
over a relatively long time horizon and involve sparse rewards. | Hierarchical Actor-Critic | 2,017 | http://arxiv.org/pdf/1712.00948v3 | Title Hierarchical ActorCritic Summary ability learn different resolution time may help overcome one main challenge deep reinforcement learning sample efficiency Hierarchical agent operate different level temporal abstraction learn task quickly divide work learning behavior among multiple policy also explore environment higher level paper present novel approach hierarchical reinforcement learning called Hierarchical ActorCritic HAC enables agent learn break problem involving continuous action space simpler subproblems belonging different time scale HAC two key advantage existing hierarchical learning method potential faster learning agent learn short policy level hierarchy ii endtoend approach demonstrate HAC significantly accelerates learning series task require behavior relatively long time horizon involve sparse reward Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.010711533948779106, 0.057223305106163025, -0.029990557581186295, -0.02625054307281971, 0.0012431297218427062, 0.007482779212296009, 0.04047413542866707, 0.01626020297408104, -0.007949854247272015, -0.00869183149188757, -0.0275528896600008, -0.01351903285831213, -0.04517769441008568, 0.08610741794109344, -0.002952864393591881, -0.12468427419662476, 0.027178190648555756, -0.026561113074421883, 0.0650302842259407, -0.017389215528964996, -0.013583429157733917, -0.05580978840589523, 0.0013872613199055195, 0.046566929668188095, -0.02207825891673565, 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843 | 843 | ['Freek Stulp', 'Pierre-Yves Oudeyer'] | 1712.05249v1 | To harness the complexity of their high-dimensional bodies during
sensorimotor development, infants are guided by patterns of freezing and
freeing of degrees of freedom. For instance, when learning to reach, infants
free the degrees of freedom in their arm proximodistally, i.e. from joints that
are closer to the body to those that are more distant. Here, we formulate and
study computationally the hypothesis that such patterns can emerge
spontaneously as the result of a family of stochastic optimization processes
(evolution strategies with covariance-matrix adaptation), without an innate
encoding of a maturational schedule. In particular, we present simulated
experiments with an arm where a computational learner progressively acquires
reaching skills through adaptive exploration, and we show that a proximodistal
organization appears spontaneously, which we denote PDFF (ProximoDistal
Freezing and Freeing of degrees of freedom). We also compare this emergent
organization between different arm morphologies -- from human-like to quite
unnatural ones -- to study the effect of different kinematic structures on the
emergence of PDFF. Keywords: human motor learning; proximo-distal exploration;
stochastic optimization; modelling; evolution strategies; cross-entropy
methods; policy search; morphology.} | Proximodistal Exploration in Motor Learning as an Emergent Property of
Optimization | 2,017 | http://arxiv.org/pdf/1712.05249v1 | Title Proximodistal Exploration Motor Learning Emergent Property Optimization Summary harness complexity highdimensional body sensorimotor development infant guided pattern freezing freeing degree freedom instance learning reach infant free degree freedom arm proximodistally ie joint closer body distant formulate study computationally hypothesis pattern emerge spontaneously result family stochastic optimization process evolution strategy covariancematrix adaptation without innate encoding maturational schedule particular present simulated experiment arm computational learner progressively acquires reaching skill adaptive exploration show proximodistal organization appears spontaneously denote PDFF ProximoDistal Freezing Freeing degree freedom also compare emergent organization different arm morphology humanlike quite unnatural one study effect different kinematic structure emergence PDFF Keywords human motor learning proximodistal exploration stochastic optimization modelling evolution strategy crossentropy method policy search morphology Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.007591096218675375, -0.030919570475816727, -0.04300932586193085, -0.013939157128334045, -0.01064420398324728, 0.022560911253094673, 0.019957570359110832, -0.005076027940958738, -0.029832862317562103, 0.026859911158680916, -0.024408021941781044, -0.03122008964419365, 0.02185315266251564, 0.028434840962290764, 0.01783500611782074, -0.032775141298770905, 0.028607111424207687, -0.018338453024625778, 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844 | 844 | ['M. J. Gagen'] | 1712.09014v1 | The hard problem in artificial intelligence asks how the shuffling of
syntactical symbols in a program can lead to systems which experience semantics
and qualia. We address this question in three stages. First, we introduce a new
class of human semantic symbols which appears when unexpected and drastic
environmental change causes humans to become surprised, confused, uncertain,
and in extreme cases, unresponsive, passive and dysfunctional. For this class
of symbols, pre-learned programs become inoperative so these syntactical
programs cannot be the source of experienced qualia. Second, we model the
dysfunctional human response to a radically changed environment as being the
natural response of any learning machine facing novel inputs from well outside
its previous training set. In this situation, learning machines are unable to
extract information from their input and will typically enter a dynamical state
characterized by null outputs and a lack of response. This state immediately
predicts and explains the characteristics of the semantic experiences of humans
in similar circumstances. In the third stage, we consider learning machines
trained to implement multiple functions in simple sequential programs using
environmental data to specify subroutine names, control flow instructions,
memory calls, and so on. Drastic change in any of these environmental inputs
can again lead to inoperative programs. By examining changes specific to people
or locations we can model human cognitive symbols featuring these dependencies,
such as attachment and grief. Our approach links known dynamical machines
states with human qualia and thus offers new insight into the hard problem of
artificial intelligence. | Null Dynamical State Models of Human Cognitive Dysfunction | 2,017 | http://arxiv.org/pdf/1712.09014v1 | Title Null Dynamical State Models Human Cognitive Dysfunction Summary hard problem artificial intelligence asks shuffling syntactical symbol program lead system experience semantics qualia address question three stage First introduce new class human semantic symbol appears unexpected drastic environmental change cause human become surprised confused uncertain extreme case unresponsive passive dysfunctional class symbol prelearned program become inoperative syntactical program cannot source experienced qualia Second model dysfunctional human response radically changed environment natural response learning machine facing novel input well outside previous training set situation learning machine unable extract information input typically enter dynamical state characterized null output lack response state immediately predicts explains characteristic semantic experience human similar circumstance third stage consider learning machine trained implement multiple function simple sequential program using environmental data specify subroutine name control flow instruction memory call Drastic change environmental input lead inoperative program examining change specific people location model human cognitive symbol featuring dependency attachment grief approach link known dynamical machine state human qualia thus offer new insight hard problem artificial intelligence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.02669551596045494, 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845 | 845 | ['Haik Manukian', 'Fabio L. Traversa', 'Massimiliano Di Ventra'] | 1801.00512v2 | Restricted Boltzmann machines (RBMs) and their extensions, called
'deep-belief networks', are powerful neural networks that have found
applications in the fields of machine learning and big data. The standard way
to training these models resorts to an iterative unsupervised procedure based
on Gibbs sampling, called 'contrastive divergence' (CD), and additional
supervised tuning via back-propagation. However, this procedure has been shown
not to follow any gradient and can lead to suboptimal solutions. In this paper,
we show an efficient alternative to CD by means of simulations of digital
memcomputing machines (DMMs). We test our approach on pattern recognition using
a modified version of the MNIST data set. DMMs sample effectively the vast
phase space given by the model distribution of the RBM, and provide a very good
approximation close to the optimum. This efficient search significantly reduces
the number of pretraining iterations necessary to achieve a given level of
accuracy, as well as a total performance gain over CD. In fact, the
acceleration of pretraining achieved by simulating DMMs is comparable to, in
number of iterations, the recently reported hardware application of the quantum
annealing method on the same network and data set. Notably, however, DMMs
perform far better than the reported quantum annealing results in terms of
quality of the training. We also compare our method to advances in supervised
training, like batch-normalization and rectifiers, that work to reduce the
advantage of pretraining. We find that the memcomputing method still maintains
a quality advantage ($>1\%$ in accuracy, and a $20\%$ reduction in error rate)
over these approaches. Furthermore, our method is agnostic about the
connectivity of the network. Therefore, it can be extended to train full
Boltzmann machines, and even deep networks at once. | Accelerating Deep Learning with Memcomputing | 2,018 | http://arxiv.org/pdf/1801.00512v2 | Title Accelerating Deep Learning Memcomputing Summary Restricted Boltzmann machine RBMs extension called deepbelief network powerful neural network found application field machine learning big data standard way training model resort iterative unsupervised procedure based Gibbs sampling called contrastive divergence CD additional supervised tuning via backpropagation However procedure shown follow gradient lead suboptimal solution paper show efficient alternative CD mean simulation digital memcomputing machine DMMs test approach pattern recognition using modified version MNIST data set DMMs sample effectively vast phase space given model distribution RBM provide good approximation close optimum efficient search significantly reduces number pretraining iteration necessary achieve given level accuracy well total performance gain CD fact acceleration pretraining achieved simulating DMMs comparable number iteration recently reported hardware application quantum annealing method network data set Notably however DMMs perform far better reported quantum annealing result term quality training also compare method advance supervised training like batchnormalization rectifier work reduce advantage pretraining find memcomputing method still maintains quality advantage 1 accuracy 20 reduction error rate approach Furthermore method agnostic connectivity network Therefore extended train full Boltzmann machine even deep network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra 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846 | 846 | ['Yu Shi', 'Fangqiu Han', 'Xinran He', 'Carl Yang', 'Jie Luo', 'Jiawei Han'] | 1801.06597v1 | Multi-view networks are ubiquitous in real-world applications. In order to
extract knowledge or business value, it is of interest to transform such
networks into representations that are easily machine-actionable. Meanwhile,
network embedding has emerged as an effective approach to generate distributed
network representations. Therefore, we are motivated to study the problem of
multi-view network embedding, with a focus on the characteristics that are
specific and important in embedding this type of networks. In our practice of
embedding real-world multi-view networks, we identify two such characteristics,
which we refer to as preservation and collaboration. We then explore the
feasibility of achieving better embedding quality by simultaneously modeling
preservation and collaboration, and propose the mvn2vec algorithms. With
experiments on a series of synthetic datasets, an internal Snapchat dataset,
and two public datasets, we further confirm the presence and importance of
preservation and collaboration. These experiments also demonstrate that better
embedding can be obtained by simultaneously modeling the two characteristics,
while not over-complicating the model or requiring additional supervision. | mvn2vec: Preservation and Collaboration in Multi-View Network Embedding | 2,018 | http://arxiv.org/pdf/1801.06597v1 | Title mvn2vec Preservation Collaboration MultiView Network Embedding Summary Multiview network ubiquitous realworld application order extract knowledge business value interest transform network representation easily machineactionable Meanwhile network embedding emerged effective approach generate distributed network representation Therefore motivated study problem multiview network embedding focus characteristic specific important embedding type network practice embedding realworld multiview network identify two characteristic refer preservation collaboration explore feasibility achieving better embedding quality simultaneously modeling preservation collaboration propose mvn2vec algorithm experiment series synthetic datasets internal Snapchat dataset two public datasets confirm presence importance preservation collaboration experiment also demonstrate better embedding obtained simultaneously modeling two characteristic overcomplicating model requiring additional supervision Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0012199279153719544, 0.03439829498529434, -0.014865078963339329, 0.005650732666254044, 0.013211688958108425, -0.023278461769223213, -0.00024796868092380464, -0.02037089131772518, 0.020105179399251938, -0.020993001759052277, 0.036786191165447235, 0.02955283224582672, -0.0013284682063385844, 0.06180468946695328, 0.03193666785955429, 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847 | 847 | ['Patrick Schwab', 'Walter Karlen'] | 1802.02195v1 | Several methods have recently been proposed to detect salient input features
for outputs of neural networks. Those methods offer a qualitative glimpse at
feature importance, but they fall short of providing quantifiable attributions
that can be compared across decisions and measures of the expected quality of
their explanations. To address these shortcomings, we present an attentive
mixture of experts (AME) that couples attentive gating with a Granger-causal
objective to jointly produce accurate predictions as well as measures of
feature importance. We demonstrate the utility of AMEs by determining factors
driving demand for medical prescriptions, comparing predictive features for
Parkinson's disease and pinpointing discriminatory genes across cancer types. | Granger-causal Attentive Mixtures of Experts | 2,018 | http://arxiv.org/pdf/1802.02195v1 | Title Grangercausal Attentive Mixtures Experts Summary Several method recently proposed detect salient input feature output neural network method offer qualitative glimpse feature importance fall short providing quantifiable attribution compared across decision measure expected quality explanation address shortcoming present attentive mixture expert AME couple attentive gating Grangercausal objective jointly produce accurate prediction well measure feature importance demonstrate utility AMEs determining factor driving demand medical prescription comparing predictive feature Parkinsons disease pinpointing discriminatory gene across cancer type Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.00200763251632452, 0.010885958559811115, -0.006908542010933161, 0.019717030227184296, 0.01309158094227314, 0.019467514008283615, 0.0177998598664999, -0.010452842339873314, -0.03912140801548958, 0.004165454301983118, -0.014568503014743328, 0.013057363219559193, 0.04707609862089157, 0.036716509610414505, 0.012830219231545925, -0.04457923397421837, -0.005336433183401823, 0.05886997655034065, 0.007866090163588524, 0.02538682334125042, 0.034790582954883575, -0.019559655338525772, 0.04750325158238411, 0.056756868958473206, -0.06858206540346146, -0.011956610716879368, 0.021233873441815376, -0.036419276148080826, 0.08493532240390778, 0.02285420335829258, 0.038096148520708084, 0.019790077582001686, 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848 | 848 | ['Adam Liška', 'Germán Kruszewski', 'Marco Baroni'] | 1802.06467v1 | Neural networks are very powerful learning systems, but they do not readily
generalize from one task to the other. This is partly due to the fact that they
do not learn in a compositional way, that is, by discovering skills that are
shared by different tasks, and recombining them to solve new problems. In this
paper, we explore the compositional generalization capabilities of recurrent
neural networks (RNNs). We first propose the lookup table composition domain as
a simple setup to test compositional behaviour and show that it is
theoretically possible for a standard RNN to learn to behave compositionally in
this domain when trained with standard gradient descent and provided with
additional supervision. We then remove this additional supervision and perform
a search over a large number of model initializations to investigate the
proportion of RNNs that can still converge to a compositional solution. We
discover that a small but non-negligible proportion of RNNs do reach partial
compositional solutions even without special architectural constraints. This
suggests that a combination of gradient descent and evolutionary strategies
directly favouring the minority models that developed more compositional
approaches might suffice to lead standard RNNs towards compositional solutions. | Memorize or generalize? Searching for a compositional RNN in a haystack | 2,018 | http://arxiv.org/pdf/1802.06467v1 | Title Memorize generalize Searching compositional RNN haystack Summary Neural network powerful learning system readily generalize one task partly due fact learn compositional way discovering skill shared different task recombining solve new problem paper explore compositional generalization capability recurrent neural network RNNs first propose lookup table composition domain simple setup test compositional behaviour show theoretically possible standard RNN learn behave compositionally domain trained standard gradient descent provided additional supervision remove additional supervision perform search large number model initialization investigate proportion RNNs still converge compositional solution discover small nonnegligible proportion RNNs reach partial compositional solution even without special architectural constraint suggests combination gradient descent evolutionary strategy directly favouring minority model developed compositional approach might suffice lead standard RNNs towards compositional solution Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.029544081538915634, 0.060220688581466675, -0.0016016733134165406, -0.006507180165499449, -0.018431834876537323, -0.021829428151249886, 0.0024944611359387636, -0.01524284016340971, -0.07089319080114365, 0.004815997555851936, -0.011547374539077282, -0.035060349851846695, 0.006641760002821684, 0.00652617821469903, 0.026925865560770035, -0.0344146303832531, 0.028402194380760193, 0.015744807198643684, 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849 | 849 | ['Christos Kaplanis', 'Murray Shanahan', 'Claudia Clopath'] | 1802.07239v1 | Unlike humans, who are capable of continual learning over their lifetimes,
artificial neural networks have long been known to suffer from a phenomenon
known as catastrophic forgetting, whereby new learning can lead to abrupt
erasure of previously acquired knowledge. Whereas in a neural network the
parameters are typically modelled as scalar values, an individual synapse in
the brain comprises a complex network of interacting biochemical components
that evolve at different timescales. In this paper, we show that by equipping
tabular and deep reinforcement learning agents with a synaptic model that
incorporates this biological complexity (Benna & Fusi, 2016), catastrophic
forgetting can be mitigated at multiple timescales. In particular, we find that
as well as enabling continual learning across sequential training of two simple
tasks, it can also be used to overcome within-task forgetting by reducing the
need for an experience replay database. | Continual Reinforcement Learning with Complex Synapses | 2,018 | http://arxiv.org/pdf/1802.07239v1 | Title Continual Reinforcement Learning Complex Synapses Summary Unlike human capable continual learning lifetime artificial neural network long known suffer phenomenon known catastrophic forgetting whereby new learning lead abrupt erasure previously acquired knowledge Whereas neural network parameter typically modelled scalar value individual synapse brain comprises complex network interacting biochemical component evolve different timescales paper show equipping tabular deep reinforcement learning agent synaptic model incorporates biological complexity Benna Fusi 2016 catastrophic forgetting mitigated multiple timescales particular find well enabling continual learning across sequential training two simple task also used overcome withintask forgetting reducing need experience replay database Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.01147836446762085, 0.009462928399443626, -0.021536637097597122, -0.05127372220158577, 0.006826843600720167, 0.022359762340784073, -0.010852308012545109, -0.03854924067854881, -0.016124475747346878, 0.0515826940536499, -0.006582262925803661, -0.002793267834931612, -0.019202113151550293, 0.04928802698850632, 0.0028752873186022043, -0.027034059166908264, 0.08122368156909943, 0.005301824305206537, -0.03640438988804817, -0.04017556458711624, 0.02619919739663601, -0.079682357609272, -0.02149491012096405, 0.016020826995372772, -0.010432924143970013, -0.060863278806209564, 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850 | 850 | ['Abhishek Gupta', 'Russell Mendonca', 'YuXuan Liu', 'Pieter Abbeel', 'Sergey Levine'] | 1802.07245v1 | Exploration is a fundamental challenge in reinforcement learning (RL). Many
of the current exploration methods for deep RL use task-agnostic objectives,
such as information gain or bonuses based on state visitation. However, many
practical applications of RL involve learning more than a single task, and
prior tasks can be used to inform how exploration should be performed in new
tasks. In this work, we explore how prior tasks can inform an agent about how
to explore effectively in new situations. We introduce a novel gradient-based
fast adaptation algorithm -- model agnostic exploration with structured noise
(MAESN) -- to learn exploration strategies from prior experience. The prior
experience is used both to initialize a policy and to acquire a latent
exploration space that can inject structured stochasticity into a policy,
producing exploration strategies that are informed by prior knowledge and are
more effective than random action-space noise. We show that MAESN is more
effective at learning exploration strategies when compared to prior meta-RL
methods, RL without learned exploration strategies, and task-agnostic
exploration methods. We evaluate our method on a variety of simulated tasks:
locomotion with a wheeled robot, locomotion with a quadrupedal walker, and
object manipulation. | Meta-Reinforcement Learning of Structured Exploration Strategies | 2,018 | http://arxiv.org/pdf/1802.07245v1 | Title MetaReinforcement Learning Structured Exploration Strategies Summary Exploration fundamental challenge reinforcement learning RL Many current exploration method deep RL use taskagnostic objective information gain bonus based state visitation However many practical application RL involve learning single task prior task used inform exploration performed new task work explore prior task inform agent explore effectively new situation introduce novel gradientbased fast adaptation algorithm model agnostic exploration structured noise MAESN learn exploration strategy prior experience prior experience used initialize policy acquire latent exploration space inject structured stochasticity policy producing exploration strategy informed prior knowledge effective random actionspace noise show MAESN effective learning exploration strategy compared prior metaRL method RL without learned exploration strategy taskagnostic exploration method evaluate method variety simulated task locomotion wheeled robot locomotion quadrupedal walker object manipulation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.022845996543765068, -0.010327394120395184, 0.00637950049713254, -0.044209763407707214, 0.012235644273459911, -0.014252508990466595, 0.008930147625505924, -0.013155504129827023, -0.06443705409765244, -0.008876400999724865, -0.02665579505264759, 0.05503110960125923, -0.04755299538373947, 0.0789950042963028, -0.00790691003203392, 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851 | 851 | ['Matthew Streeter'] | 1802.07697v1 | We present an approximation algorithm that takes a pool of pre-trained models
as input and produces from it a cascaded model with similar accuracy but lower
average-case cost. Applied to state-of-the-art ImageNet classification models,
this yields up to a 2x reduction in floating point multiplications, and up to a
6x reduction in average-case memory I/O. The auto-generated cascades exhibit
intuitive properties, such as using lower-resolution input for easier images
and requiring higher prediction confidence when using a computationally cheaper
model. | Approximation Algorithms for Cascading Prediction Models | 2,018 | http://arxiv.org/pdf/1802.07697v1 | Title Approximation Algorithms Cascading Prediction Models Summary present approximation algorithm take pool pretrained model input produce cascaded model similar accuracy lower averagecase cost Applied stateoftheart ImageNet classification model yield 2x reduction floating point multiplication 6x reduction averagecase memory IO autogenerated cascade exhibit intuitive property using lowerresolution input easier image requiring higher prediction confidence using computationally cheaper model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 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852 | 852 | ['Wlodzislaw Duch'] | 1802.08478v1 | Neural networks are commonly regarded as black boxes performing
incomprehensible functions. For classification problems networks provide maps
from high dimensional feature space to K-dimensional image space. Images of
training vector are projected on polygon vertices, providing visualization of
network function. Such visualization may show the dynamics of learning, allow
for comparison of different networks, display training vectors around which
potential problems may arise, show differences due to regularization and
optimization procedures, investigate stability of network classification under
perturbation of original vectors, and place new data sample in relation to
training data, allowing for estimation of confidence in classification of a
given sample. An illustrative example for the three-class Wine data and
five-class Satimage data is described. The visualization method proposed here
is applicable to any black box system that provides continuous outputs. | Coloring black boxes: visualization of neural network decisions | 2,018 | http://arxiv.org/pdf/1802.08478v1 | Title Coloring black box visualization neural network decision Summary Neural network commonly regarded black box performing incomprehensible function classification problem network provide map high dimensional feature space Kdimensional image space Images training vector projected polygon vertex providing visualization network function visualization may show dynamic learning allow comparison different network display training vector around potential problem may arise show difference due regularization optimization procedure investigate stability network classification perturbation original vector place new data sample relation training data allowing estimation confidence classification given sample illustrative example threeclass Wine data fiveclass Satimage data described visualization method proposed applicable black box system provides continuous output Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.027500305324792862, 0.00057623052271083, -0.024883462116122246, 0.01702534779906273, -0.04228878766298294, -0.013837902806699276, 0.06630858778953552, 0.03646942973136902, -0.016635505482554436, 0.013931164517998695, 0.020103393122553825, 0.035459134727716446, 0.01946960762143135, 0.08091599494218826, 0.03291395679116249, -0.07045935094356537, -0.03480079770088196, 0.06471765786409378, -0.01988224871456623, -0.015998389571905136, 0.011925943195819855, -0.03625031188130379, 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853 | 853 | ['Sjoerd van Steenkiste', 'Michael Chang', 'Klaus Greff', 'Jürgen Schmidhuber'] | 1802.10353v1 | Common-sense physical reasoning is an essential ingredient for any
intelligent agent operating in the real-world. For example, it can be used to
simulate the environment, or to infer the state of parts of the world that are
currently unobserved. In order to match real-world conditions this causal
knowledge must be learned without access to supervised data. To address this
problem we present a novel method that learns to discover objects and model
their physical interactions from raw visual images in a purely
\emph{unsupervised} fashion. It incorporates prior knowledge about the
compositional nature of human perception to factor interactions between
object-pairs and learn efficiently. On videos of bouncing balls we show the
superior modelling capabilities of our method compared to other unsupervised
neural approaches that do not incorporate such prior knowledge. We demonstrate
its ability to handle occlusion and show that it can extrapolate learned
knowledge to scenes with different numbers of objects. | Relational Neural Expectation Maximization: Unsupervised Discovery of
Objects and their Interactions | 2,018 | http://arxiv.org/pdf/1802.10353v1 | Title Relational Neural Expectation Maximization Unsupervised Discovery Objects Interactions Summary Commonsense physical reasoning essential ingredient intelligent agent operating realworld example used simulate environment infer state part world currently unobserved order match realworld condition causal knowledge must learned without access supervised data address problem present novel method learns discover object model physical interaction raw visual image purely emphunsupervised fashion incorporates prior knowledge compositional nature human perception factor interaction objectpairs learn efficiently video bouncing ball show superior modelling capability method compared unsupervised neural approach incorporate prior knowledge demonstrate ability handle occlusion show extrapolate learned knowledge scene different number object Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.012887672521173954, 0.04423285648226738, 0.008632563054561615, 0.07505037635564804, -0.009485420770943165, -0.02576008066534996, 0.017814286053180695, -0.03150689974427223, 0.03437905013561249, -0.003737053833901882, 0.011476302519440651, 0.010449493303894997, 0.0022367045748978853, 0.07655835151672363, 0.018068132922053337, -0.05108867585659027, -0.00818007905036211, 0.003137945896014571, -0.04237636178731918, -0.007544194348156452, -0.013450754806399345, -0.02478247508406639, -0.03592590615153313, 0.015172644518315792, -0.0718403235077858, 0.030413825064897537, -0.05675256624817848, -0.015265061520040035, 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854 | 854 | ['Henrique X. Goulart', 'Guilherme A. Wachs-Lopes'] | 1802.10393v1 | In events that are composed by many activities, there is a problem that
involves retrieve and management the information of visitors that are visiting
the activities. This management is crucial to find some activities that are
drawing attention of visitors; identify an ideal positioning for activities;
which path is more frequented by visitors. In this work, these features are
studied using Complex Network theory. For the beginning, an artificial database
was generated to study the mentioned features. Secondly, this work shows a
method to optimize the event structure that is better than a random method and
a recommendation system that achieves ~95% of accuracy. | A Bayesian Model for Activities Recommendation and Event Structure
Optimization Using Visitors Tracking | 2,018 | http://arxiv.org/pdf/1802.10393v1 | Title Bayesian Model Activities Recommendation Event Structure Optimization Using Visitors Tracking Summary event composed many activity problem involves retrieve management information visitor visiting activity management crucial find activity drawing attention visitor identify ideal positioning activity path frequented visitor work feature studied using Complex Network theory beginning artificial database generated study mentioned feature Secondly work show method optimize event structure better random method recommendation system achieves 95 accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang 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855 | 855 | ['Jonathan Frankle', 'Michael Carbin'] | 1803.03635v1 | Recent work on neural network pruning indicates that, at training time,
neural networks need to be significantly larger in size than is necessary to
represent the eventual functions that they learn. This paper articulates a new
hypothesis to explain this phenomenon. This conjecture, which we term the
"lottery ticket hypothesis," proposes that successful training depends on lucky
random initialization of a smaller subcomponent of the network. Larger networks
have more of these "lottery tickets," meaning they are more likely to luck out
with a subcomponent initialized in a configuration amenable to successful
optimization.
This paper conducts a series of experiments with XOR and MNIST that support
the lottery ticket hypothesis. In particular, we identify these
fortuitously-initialized subcomponents by pruning low-magnitude weights from
trained networks. We then demonstrate that these subcomponents can be
successfully retrained in isolation so long as the subnetworks are given the
same initializations as they had at the beginning of the training process.
Initialized as such, these small networks reliably converge successfully, often
faster than the original network at the same level of accuracy. However, when
these subcomponents are randomly reinitialized or rearranged, they perform
worse than the original network. In other words, large networks that train
successfully contain small subnetworks with initializations conducive to
optimization.
The lottery ticket hypothesis and its connection to pruning are a step toward
developing architectures, initializations, and training strategies that make it
possible to solve the same problems with much smaller networks. | The Lottery Ticket Hypothesis: Training Pruned Neural Networks | 2,018 | http://arxiv.org/pdf/1803.03635v1 | Title Lottery Ticket Hypothesis Training Pruned Neural Networks Summary Recent work neural network pruning indicates training time neural network need significantly larger size necessary represent eventual function learn paper articulates new hypothesis explain phenomenon conjecture term lottery ticket hypothesis proposes successful training depends lucky random initialization smaller subcomponent network Larger network lottery ticket meaning likely luck subcomponent initialized configuration amenable successful optimization paper conduct series experiment XOR MNIST support lottery ticket hypothesis particular identify fortuitouslyinitialized subcomponents pruning lowmagnitude weight trained network demonstrate subcomponents successfully retrained isolation long subnetworks given initialization beginning training process Initialized small network reliably converge successfully often faster original network level accuracy However subcomponents randomly reinitialized rearranged perform worse original network word large network train successfully contain small subnetworks initialization conducive optimization lottery ticket hypothesis connection pruning step toward developing architecture initialization training strategy make possible solve problem much smaller network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.004814235493540764, 0.05187557265162468, -0.036668747663497925, 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856 | 856 | ['Christopher Kim', 'Carson Chow'] | 1803.06622v1 | Spiking activity of neurons engaged in learning and performing a task show
complex spatiotemporal dynamics. While the output of recurrent network models
can learn to perform various tasks, the possible range of recurrent dynamics
that emerge after learning remains unknown. Here we show that modifying the
recurrent connectivity with a recursive least squares algorithm provides
sufficient flexibility for synaptic and spiking rate dynamics of spiking
networks to produce a wide range of spatiotemporal activity. We apply the
training method to learn arbitrary firing patterns, stabilize irregular spiking
activity of a balanced network, and reproduce the heterogeneous spiking rate
patterns of cortical neurons engaged in motor planning and movement. We
identify sufficient conditions for successful learning, characterize two types
of learning errors, and assess the network capacity. Our findings show that
synaptically-coupled recurrent spiking networks possess a vast computational
capability that can support the diverse activity patterns in the brain. | Learning recurrent dynamics in spiking networks | 2,018 | http://arxiv.org/pdf/1803.06622v1 | Title Learning recurrent dynamic spiking network Summary Spiking activity neuron engaged learning performing task show complex spatiotemporal dynamic output recurrent network model learn perform various task possible range recurrent dynamic emerge learning remains unknown show modifying recurrent connectivity recursive least square algorithm provides sufficient flexibility synaptic spiking rate dynamic spiking network produce wide range spatiotemporal activity apply training method learn arbitrary firing pattern stabilize irregular spiking activity balanced network reproduce heterogeneous spiking rate pattern cortical neuron engaged motor planning movement identify sufficient condition successful learning characterize two type learning error ass network capacity finding show synapticallycoupled recurrent spiking network posse vast computational capability support diverse activity pattern brain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03337254002690315, -0.05719134956598282, -0.017811812460422516, -0.0037006523925811052, 0.015769802033901215, 0.019087083637714386, -0.02505081333220005, -0.021392086520791054, -0.009612438268959522, 0.004935021046549082, -0.009756585583090782, 0.0027564247138798237, 0.02311229519546032, 0.09632956981658936, 0.025565195828676224, -0.025679131969809532, 0.07423681020736694, 0.03610284999012947, -0.018611494451761246, -0.025392120704054832, 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857 | 857 | ['A. N. Gorban', 'A. Y. Zinovyev'] | 0809.0490v2 | In many physical, statistical, biological and other investigations it is
desirable to approximate a system of points by objects of lower dimension
and/or complexity. For this purpose, Karl Pearson invented principal component
analysis in 1901 and found 'lines and planes of closest fit to system of
points'. The famous k-means algorithm solves the approximation problem too, but
by finite sets instead of lines and planes. This chapter gives a brief
practical introduction into the methods of construction of general principal
objects, i.e. objects embedded in the 'middle' of the multidimensional data
set. As a basis, the unifying framework of mean squared distance approximation
of finite datasets is selected. Principal graphs and manifolds are constructed
as generalisations of principal components and k-means principal points. For
this purpose, the family of expectation/maximisation algorithms with nearest
generalisations is presented. Construction of principal graphs with controlled
complexity is based on the graph grammar approach. | Principal Graphs and Manifolds | 2,008 | http://arxiv.org/pdf/0809.0490v2 | Title Principal Graphs Manifolds Summary many physical statistical biological investigation desirable approximate system point object lower dimension andor complexity purpose Karl Pearson invented principal component analysis 1901 found line plane closest fit system point famous kmeans algorithm solves approximation problem finite set instead line plane chapter give brief practical introduction method construction general principal object ie object embedded middle multidimensional data set basis unifying framework mean squared distance approximation finite datasets selected Principal graph manifold constructed generalisation principal component kmeans principal point purpose family expectationmaximisation algorithm nearest generalisation presented Construction principal graph controlled complexity based graph grammar approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.05076771602034569, -0.029868997633457184, -0.0129631869494915, 0.026080191135406494, -0.014954281970858574, -0.023005656898021698, 0.009431012906134129, -0.003656127257272601, 0.014218808151781559, 0.019609374925494194, 0.0367770753800869, 0.01792166940867901, 0.013533476740121841, 0.017544740810990334, 0.051993824541568756, -0.059767432510852814, -0.0227980837225914, -0.001410702825523913, -0.0027663896325975657, -0.041149578988552094, 0.021970251575112343, -0.02708389051258564, 0.011723414063453674, 0.022265950217843056, -0.050804976373910904, 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858 | 858 | ['Xanadu Halkias', 'Sebastien Paris', 'Herve Glotin'] | 1301.3533v2 | Deep Belief Networks (DBN) have been successfully applied on popular machine
learning tasks. Specifically, when applied on hand-written digit recognition,
DBNs have achieved approximate accuracy rates of 98.8%. In an effort to
optimize the data representation achieved by the DBN and maximize their
descriptive power, recent advances have focused on inducing sparse constraints
at each layer of the DBN. In this paper we present a theoretical approach for
sparse constraints in the DBN using the mixed norm for both non-overlapping and
overlapping groups. We explore how these constraints affect the classification
accuracy for digit recognition in three different datasets (MNIST, USPS, RIMES)
and provide initial estimations of their usefulness by altering different
parameters such as the group size and overlap percentage. | Sparse Penalty in Deep Belief Networks: Using the Mixed Norm Constraint | 2,013 | http://arxiv.org/pdf/1301.3533v2 | Title Sparse Penalty Deep Belief Networks Using Mixed Norm Constraint Summary Deep Belief Networks DBN successfully applied popular machine learning task Specifically applied handwritten digit recognition DBNs achieved approximate accuracy rate 988 effort optimize data representation achieved DBN maximize descriptive power recent advance focused inducing sparse constraint layer DBN paper present theoretical approach sparse constraint DBN using mixed norm nonoverlapping overlapping group explore constraint affect classification accuracy digit recognition three different datasets MNIST USPS RIMES provide initial estimation usefulness altering different parameter group size overlap percentage Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.05097030848264694, 0.11358065903186798, -0.018799735233187675, 0.04781665652990341, -0.004256457556039095, 0.031146284192800522, 0.09198739379644394, 0.025534050539135933, 0.028942802920937538, 0.012865393422544003, -0.00016754117677919567, -0.06164489686489105, 0.002961382968351245, 0.015032391063869, -0.00670802453532815, -0.014939660206437111, -0.0070642405189573765, -0.018599187955260277, -0.005589948035776615, -0.04257439821958542, 0.009029557928442955, -0.033438581973314285, -0.008018231019377708, 0.011398998089134693, -0.10480187833309174, 0.03221001848578453, 0.0010534337488934398, -0.009489381685853004, 0.013004948385059834, 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859 | 859 | ['Kyunghyun Cho'] | 1306.2801v4 | In this paper, a simple, general method of adding auxiliary stochastic
neurons to a multi-layer perceptron is proposed. It is shown that the proposed
method is a generalization of recently successful methods of dropout (Hinton et
al., 2012), explicit noise injection (Vincent et al., 2010; Bishop, 1995) and
semantic hashing (Salakhutdinov & Hinton, 2009). Under the proposed framework,
an extension of dropout which allows using separate dropping probabilities for
different hidden neurons, or layers, is found to be available. The use of
different dropping probabilities for hidden layers separately is empirically
investigated. | Understanding Dropout: Training Multi-Layer Perceptrons with Auxiliary
Independent Stochastic Neurons | 2,013 | http://arxiv.org/pdf/1306.2801v4 | Title Understanding Dropout Training MultiLayer Perceptrons Auxiliary Independent Stochastic Neurons Summary paper simple general method adding auxiliary stochastic neuron multilayer perceptron proposed shown proposed method generalization recently successful method dropout Hinton et al 2012 explicit noise injection Vincent et al 2010 Bishop 1995 semantic hashing Salakhutdinov Hinton 2009 proposed framework extension dropout allows using separate dropping probability different hidden neuron layer found available use different dropping probability hidden layer separately empirically investigated Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell 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860 | 860 | ['Linlin Cao', 'Ran He', 'Bao-Gang Hu'] | 1604.05198v1 | This work is a further study on the Generalized Constraint Neural Network
(GCNN) model [1], [2]. Two challenges are encountered in the study, that is, to
embed any type of prior information and to select its imposing schemes. The
work focuses on the second challenge and studies a new constraint imposing
scheme for equality constraints. A new method called locally imposing function
(LIF) is proposed to provide a local correction to the GCNN prediction
function, which therefore falls within Locally Imposing Scheme (LIS). In
comparison, the conventional Lagrange multiplier method is considered as
Globally Imposing Scheme (GIS) because its added constraint term exhibits a
global impact to its objective function. Two advantages are gained from LIS
over GIS. First, LIS enables constraints to fire locally and explicitly in the
domain only where they need on the prediction function. Second, constraints can
be implemented within a network setting directly. We attempt to interpret
several constraint methods graphically from a viewpoint of the locality
principle. Numerical examples confirm the advantages of the proposed method. In
solving boundary value problems with Dirichlet and Neumann constraints, the
GCNN model with LIF is possible to achieve an exact satisfaction of the
constraints. | Locally Imposing Function for Generalized Constraint Neural Networks - A
Study on Equality Constraints | 2,016 | http://arxiv.org/pdf/1604.05198v1 | Title Locally Imposing Function Generalized Constraint Neural Networks Study Equality Constraints Summary work study Generalized Constraint Neural Network GCNN model 1 2 Two challenge encountered study embed type prior information select imposing scheme work focus second challenge study new constraint imposing scheme equality constraint new method called locally imposing function LIF proposed provide local correction GCNN prediction function therefore fall within Locally Imposing Scheme LIS comparison conventional Lagrange multiplier method considered Globally Imposing Scheme GIS added constraint term exhibit global impact objective function Two advantage gained LIS GIS First LIS enables constraint fire locally explicitly domain need prediction function Second constraint implemented within network setting directly attempt interpret several constraint method graphically viewpoint locality principle Numerical example confirm advantage proposed method solving boundary value problem Dirichlet Neumann constraint GCNN model LIF possible achieve exact satisfaction constraint Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.012120881117880344, 0.06214816868305206, -0.016614563763141632, 0.014303338713943958, -0.018026331439614296, -0.07098133116960526, 0.04174258932471275, -0.0015680597862228751, -0.0076708621345460415, 0.01642751693725586, 0.008081131614744663, -0.021789826452732086, -0.00266508967615664, 0.03973434492945671, 0.03425168618559837, -0.0422992967069149, 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861 | 861 | ['Gabriel Kronberger', 'Michael Kommenda'] | 1305.3794v2 | In this contribution we describe an approach to evolve composite covariance
functions for Gaussian processes using genetic programming. A critical aspect
of Gaussian processes and similar kernel-based models such as SVM is, that the
covariance function should be adapted to the modeled data. Frequently, the
squared exponential covariance function is used as a default. However, this can
lead to a misspecified model, which does not fit the data well. In the proposed
approach we use a grammar for the composition of covariance functions and
genetic programming to search over the space of sentences that can be derived
from the grammar. We tested the proposed approach on synthetic data from
two-dimensional test functions, and on the Mauna Loa CO2 time series. The
results show, that our approach is feasible, finding covariance functions that
perform much better than a default covariance function. For the CO2 data set a
composite covariance function is found, that matches the performance of a
hand-tuned covariance function. | Evolution of Covariance Functions for Gaussian Process Regression using
Genetic Programming | 2,013 | http://arxiv.org/pdf/1305.3794v2 | Title Evolution Covariance Functions Gaussian Process Regression using Genetic Programming Summary contribution describe approach evolve composite covariance function Gaussian process using genetic programming critical aspect Gaussian process similar kernelbased model SVM covariance function adapted modeled data Frequently squared exponential covariance function used default However lead misspecified model fit data well proposed approach use grammar composition covariance function genetic programming search space sentence derived grammar tested proposed approach synthetic data twodimensional test function Mauna Loa CO2 time series result show approach feasible finding covariance function perform much better default covariance function CO2 data set composite covariance function found match performance handtuned covariance function Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03350526839494705, 0.07776971906423569, -0.022528696805238724, -0.0037425400223582983, 0.006588541436940432, 0.022453904151916504, 0.03150999918580055, -0.01943681761622429, -0.011949557811021805, 0.0022121621295809746, 0.04391717538237572, 0.034531835466623306, -0.000879958737641573, 0.1294478476047516, 0.0030570209491997957, -0.015123149380087852, 0.03966853395104408, -0.052622221410274506, 0.004503870382905006, -0.008032271638512611, -0.024378465488553047, -0.03088267520070076, 0.01350046880543232, 0.0337095707654953, -0.0364316962659359, 0.021182483062148094, 0.01271691732108593, -0.0075149741023778915, 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862 | 862 | ['Nan Wang', 'Jan Melchior', 'Laurenz Wiskott'] | 1401.5900v1 | We present a theoretical analysis of Gaussian-binary restricted Boltzmann
machines (GRBMs) from the perspective of density models. The key aspect of this
analysis is to show that GRBMs can be formulated as a constrained mixture of
Gaussians, which gives a much better insight into the model's capabilities and
limitations. We show that GRBMs are capable of learning meaningful features
both in a two-dimensional blind source separation task and in modeling natural
images. Further, we show that reported difficulties in training GRBMs are due
to the failure of the training algorithm rather than the model itself. Based on
our analysis we are able to propose several training recipes, which allowed
successful and fast training in our experiments. Finally, we discuss the
relationship of GRBMs to several modifications that have been proposed to
improve the model. | Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image
Statistics | 2,014 | http://arxiv.org/pdf/1401.5900v1 | Title Gaussianbinary Restricted Boltzmann Machines Modeling Natural Image Statistics Summary present theoretical analysis Gaussianbinary restricted Boltzmann machine GRBMs perspective density model key aspect analysis show GRBMs formulated constrained mixture Gaussians give much better insight model capability limitation show GRBMs capable learning meaningful feature twodimensional blind source separation task modeling natural image show reported difficulty training GRBMs due failure training algorithm rather model Based analysis able propose several training recipe allowed successful fast training experiment Finally discus relationship GRBMs several modification proposed improve model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan 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863 | 863 | ['Siamak Ravanbakhsh', 'Russell Greiner', 'Brendan Frey'] | 1405.1436v1 | A new approach to maximum likelihood learning of discrete graphical models
and RBM in particular is introduced. Our method, Perturb and Descend (PD) is
inspired by two ideas (I) perturb and MAP method for sampling (II) learning by
Contrastive Divergence minimization. In contrast to perturb and MAP, PD
leverages training data to learn the models that do not allow efficient MAP
estimation. During the learning, to produce a sample from the current model, we
start from a training data and descend in the energy landscape of the
"perturbed model", for a fixed number of steps, or until a local optima is
reached. For RBM, this involves linear calculations and thresholding which can
be very fast. Furthermore we show that the amount of perturbation is closely
related to the temperature parameter and it can regularize the model by
producing robust features resulting in sparse hidden layer activation. | Training Restricted Boltzmann Machine by Perturbation | 2,014 | http://arxiv.org/pdf/1405.1436v1 | Title Training Restricted Boltzmann Machine Perturbation Summary new approach maximum likelihood learning discrete graphical model RBM particular introduced method Perturb Descend PD inspired two idea perturb MAP method sampling II learning Contrastive Divergence minimization contrast perturb MAP PD leverage training data learn model allow efficient MAP estimation learning produce sample current model start training data descend energy landscape perturbed model fixed number step local optimum reached RBM involves linear calculation thresholding fast Furthermore show amount perturbation closely related temperature parameter regularize model producing robust feature resulting sparse hidden layer activation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.013479205779731274, -0.030411183834075928, -0.029634486883878708, 0.02206467278301716, -0.012852332554757595, -0.001959493849426508, 0.02294575795531273, 0.00039903982542455196, 0.005045519210398197, 0.001177957165054977, -0.030495693907141685, 0.04076455906033516, -0.002123118145391345, 0.020608248189091682, 0.038229383528232574, -0.07931271940469742, 0.006634132005274296, -0.00024227425456047058, -0.016760658472776413, -0.03146129101514816, 0.04003945738077164, -0.02624077908694744, 0.0482511930167675, -0.018726151436567307, -0.08393780887126923, 0.03754578158259392, -0.05205344408750534, 0.032329440116882324, 0.0907234251499176, 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864 | 864 | ['Xiao-Lei Zhang'] | 1408.0848v8 | Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear
network from bottom up for unsupervised nonlinear dimensionality reduction.
Each layer of the network is a nonparametric density estimator. It consists of
a group of k-centroids clusterings. Each clustering randomly selects data
points with randomly selected features as its centroids, and learns a one-hot
encoder by one-nearest-neighbor optimization. Geometrically, the nonparametric
density estimator at each layer projects the input data space to a
uniformly-distributed discrete feature space, where the similarity of two data
points in the discrete feature space is measured by the number of the nearest
centroids they share in common. The multilayer network gradually reduces the
nonlinear variations of data from bottom up by building a vast number of
hierarchical trees implicitly on the original data space. Theoretically, the
estimation error caused by the nonparametric density estimator is proportional
to the correlation between the clusterings, both of which are reduced by the
randomization steps. | Multilayer bootstrap networks | 2,014 | http://arxiv.org/pdf/1408.0848v8 | Title Multilayer bootstrap network Summary Multilayer bootstrap network build gradually narrowed multilayer nonlinear network bottom unsupervised nonlinear dimensionality reduction layer network nonparametric density estimator consists group kcentroids clustering clustering randomly selects data point randomly selected feature centroid learns onehot encoder onenearestneighbor optimization Geometrically nonparametric density estimator layer project input data space uniformlydistributed discrete feature space similarity two data point discrete feature space measured number nearest centroid share common multilayer network gradually reduces nonlinear variation data bottom building vast number hierarchical tree implicitly original data space Theoretically estimation error caused nonparametric density estimator proportional correlation clustering reduced randomization step Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04715077579021454, 0.016762183979153633, -0.039488036185503006, -0.005515534430742264, -0.037119973450899124, -0.019222823902964592, 0.04221014678478241, -0.003004320664331317, -0.06774306297302246, 0.051473766565322876, -0.024001939222216606, 0.024524623528122902, 0.015463277697563171, 0.025538334622979164, 0.04098612070083618, -0.0518987812101841, -0.03436070308089256, 0.03149145841598511, -0.031145183369517326, -0.029196610674262047, 0.031449079513549805, -0.0074898903258144855, 0.008214217610657215, 0.0029702766332775354, -0.037892162799835205, 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865 | 865 | ['Sergey Demyanov', 'James Bailey', 'Ramamohanarao Kotagiri', 'Christopher Leckie'] | 1502.04434v3 | In many classification problems a classifier should be robust to small
variations in the input vector. This is a desired property not only for
particular transformations, such as translation and rotation in image
classification problems, but also for all others for which the change is small
enough to retain the object perceptually indistinguishable. We propose two
extensions of the backpropagation algorithm that train a neural network to be
robust to variations in the feature vector. While the first of them enforces
robustness of the loss function to all variations, the second method trains the
predictions to be robust to a particular variation which changes the loss
function the most. The second methods demonstrates better results, but is
slightly slower. We analytically compare the proposed algorithm with two the
most similar approaches (Tangent BP and Adversarial Training), and propose
their fast versions. In the experimental part we perform comparison of all
algorithms in terms of classification accuracy and robustness to noise on MNIST
and CIFAR-10 datasets. Additionally we analyze how the performance of the
proposed algorithm depends on the dataset size and data augmentation. | Invariant backpropagation: how to train a transformation-invariant
neural network | 2,015 | http://arxiv.org/pdf/1502.04434v3 | Title Invariant backpropagation train transformationinvariant neural network Summary many classification problem classifier robust small variation input vector desired property particular transformation translation rotation image classification problem also others change small enough retain object perceptually indistinguishable propose two extension backpropagation algorithm train neural network robust variation feature vector first enforces robustness loss function variation second method train prediction robust particular variation change loss function second method demonstrates better result slightly slower analytically compare proposed algorithm two similar approach Tangent BP Adversarial Training propose fast version experimental part perform comparison algorithm term classification accuracy robustness noise MNIST CIFAR10 datasets Additionally analyze performance proposed algorithm depends dataset size data augmentation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.015735244378447533, 0.03271784633398056, -0.03202942758798599, 0.018016867339611053, 0.02261238917708397, -0.018340522423386574, 0.025017978623509407, 0.0011434972984716296, -0.05588329955935478, -0.028222892433404922, 0.0007722434820607305, 0.03622652217745781, 0.030474750325083733, 0.004983484745025635, 0.050902195274829865, -0.043725866824388504, 0.005890813656151295, 0.02764352597296238, -0.08231320977210999, -0.026533212512731552, -0.015437808819115162, -0.053196873515844345, -0.0070609175600111485, 0.009219509549438953, 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866 | 866 | ['Danila Doroshin', 'Alexander Yamshinin', 'Nikolay Lubimov', 'Marina Nastasenko', 'Mikhail Kotov', 'Maxim Tkachenko'] | 1503.05471v1 | This paper presents a novel approach to speaker subspace modelling based on
Gaussian-Binary Restricted Boltzmann Machines (GRBM). The proposed model is
based on the idea of shared factors as in the Probabilistic Linear Discriminant
Analysis (PLDA). GRBM hidden layer is divided into speaker and channel factors,
herein the speaker factor is shared over all vectors of the speaker. Then
Maximum Likelihood Parameter Estimation (MLE) for proposed model is introduced.
Various new scoring techniques for speaker verification using GRBM are
proposed. The results for NIST i-vector Challenge 2014 dataset are presented. | Shared latent subspace modelling within Gaussian-Binary Restricted
Boltzmann Machines for NIST i-Vector Challenge 2014 | 2,015 | http://arxiv.org/pdf/1503.05471v1 | Title Shared latent subspace modelling within GaussianBinary Restricted Boltzmann Machines NIST iVector Challenge 2014 Summary paper present novel approach speaker subspace modelling based GaussianBinary Restricted Boltzmann Machines GRBM proposed model based idea shared factor Probabilistic Linear Discriminant Analysis PLDA GRBM hidden layer divided speaker channel factor herein speaker factor shared vector speaker Maximum Likelihood Parameter Estimation MLE proposed model introduced Various new scoring technique speaker verification using GRBM proposed result NIST ivector Challenge 2014 dataset presented Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 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867 | 867 | ['Sebastian Urban', 'Patrick van der Smagt'] | 1503.05724v3 | Existing approaches to combine both additive and multiplicative neural units
either use a fixed assignment of operations or require discrete optimization to
determine what function a neuron should perform. This leads either to an
inefficient distribution of computational resources or an extensive increase in
the computational complexity of the training procedure.
We present a novel, parameterizable transfer function based on the
mathematical concept of non-integer functional iteration that allows the
operation each neuron performs to be smoothly and, most importantly,
differentiablely adjusted between addition and multiplication. This allows the
decision between addition and multiplication to be integrated into the standard
backpropagation training procedure. | A Neural Transfer Function for a Smooth and Differentiable Transition
Between Additive and Multiplicative Interactions | 2,015 | http://arxiv.org/pdf/1503.05724v3 | Title Neural Transfer Function Smooth Differentiable Transition Additive Multiplicative Interactions Summary Existing approach combine additive multiplicative neural unit either use fixed assignment operation require discrete optimization determine function neuron perform lead either inefficient distribution computational resource extensive increase computational complexity training procedure present novel parameterizable transfer function based mathematical concept noninteger functional iteration allows operation neuron performs smoothly importantly differentiablely adjusted addition multiplication allows decision addition multiplication integrated standard backpropagation training procedure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02768981270492077, 0.05566762387752533, 0.0052767000161111355, 0.03848479315638542, 0.04850592091679573, -0.008199125528335571, 0.028266819193959236, -0.0469738207757473, -0.0611237995326519, 0.005652204621583223, -0.024118052795529366, -0.042379822582006454, 0.04574961215257645, 0.06665749847888947, 0.034806590527296066, -0.00823962315917015, 0.033468082547187805, 0.019448451697826385, -0.05014979466795921, -0.0022070761770009995, 0.012668444775044918, -0.025104207918047905, -0.02872585505247116, 0.018138045445084572, -0.006965294945985079, 0.010345318354666233, 0.0009116464643739164, -0.03161615878343582, 0.030551528558135033, -0.007209466770291328, 0.020382042974233627, -0.002624594373628497, 0.017267471179366112, 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868 | 868 | ['Ankit B. Patel', 'Tan Nguyen', 'Richard G. Baraniuk'] | 1612.01936v1 | We develop a probabilistic framework for deep learning based on the Deep
Rendering Mixture Model (DRMM), a new generative probabilistic model that
explicitly capture variations in data due to latent task nuisance variables. We
demonstrate that max-sum inference in the DRMM yields an algorithm that exactly
reproduces the operations in deep convolutional neural networks (DCNs),
providing a first principles derivation. Our framework provides new insights
into the successes and shortcomings of DCNs as well as a principled route to
their improvement. DRMM training via the Expectation-Maximization (EM)
algorithm is a powerful alternative to DCN back-propagation, and initial
training results are promising. Classification based on the DRMM and other
variants outperforms DCNs in supervised digit classification, training 2-3x
faster while achieving similar accuracy. Moreover, the DRMM is applicable to
semi-supervised and unsupervised learning tasks, achieving results that are
state-of-the-art in several categories on the MNIST benchmark and comparable to
state of the art on the CIFAR10 benchmark. | A Probabilistic Framework for Deep Learning | 2,016 | http://arxiv.org/pdf/1612.01936v1 | Title Probabilistic Framework Deep Learning Summary develop probabilistic framework deep learning based Deep Rendering Mixture Model DRMM new generative probabilistic model explicitly capture variation data due latent task nuisance variable demonstrate maxsum inference DRMM yield algorithm exactly reproduces operation deep convolutional neural network DCNs providing first principle derivation framework provides new insight success shortcoming DCNs well principled route improvement DRMM training via ExpectationMaximization EM algorithm powerful alternative DCN backpropagation initial training result promising Classification based DRMM variant outperforms DCNs supervised digit classification training 23x faster achieving similar accuracy Moreover DRMM applicable semisupervised unsupervised learning task achieving result stateoftheart several category MNIST benchmark comparable state art CIFAR10 benchmark Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.023877430707216263, 0.028987117111682892, -0.0010197419906035066, 0.02622407302260399, 0.01665334403514862, -0.02270248532295227, 0.027868950739502907, -0.021976148709654808, -0.0785486102104187, 0.040055111050605774, 0.025343673303723335, 0.004170405212789774, 0.017193099483847618, 0.06400845944881439, 0.03136127442121506, -0.10701686143875122, -0.041751548647880554, -0.0058395895175635815, -0.008263819850981236, -0.011572235263884068, 0.00022954511223360896, 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869 | 869 | ['Timothy J. Draelos', 'Nadine E. Miner', 'Christopher C. Lamb', 'Jonathan A. Cox', 'Craig M. Vineyard', 'Kristofor D. Carlson', 'William M. Severa', 'Conrad D. James', 'James B. Aimone'] | 1612.03770v2 | Neural machine learning methods, such as deep neural networks (DNN), have
achieved remarkable success in a number of complex data processing tasks. These
methods have arguably had their strongest impact on tasks such as image and
audio processing - data processing domains in which humans have long held clear
advantages over conventional algorithms. In contrast to biological neural
systems, which are capable of learning continuously, deep artificial networks
have a limited ability for incorporating new information in an already trained
network. As a result, methods for continuous learning are potentially highly
impactful in enabling the application of deep networks to dynamic data sets.
Here, inspired by the process of adult neurogenesis in the hippocampus, we
explore the potential for adding new neurons to deep layers of artificial
neural networks in order to facilitate their acquisition of novel information
while preserving previously trained data representations. Our results on the
MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes
lower and upper case letters and digits, demonstrate that neurogenesis is well
suited for addressing the stability-plasticity dilemma that has long challenged
adaptive machine learning algorithms. | Neurogenesis Deep Learning | 2,016 | http://arxiv.org/pdf/1612.03770v2 | Title Neurogenesis Deep Learning Summary Neural machine learning method deep neural network DNN achieved remarkable success number complex data processing task method arguably strongest impact task image audio processing data processing domain human long held clear advantage conventional algorithm contrast biological neural system capable learning continuously deep artificial network limited ability incorporating new information already trained network result method continuous learning potentially highly impactful enabling application deep network dynamic data set inspired process adult neurogenesis hippocampus explore potential adding new neuron deep layer artificial neural network order facilitate acquisition novel information preserving previously trained data representation result MNIST handwritten digit dataset NIST SD 19 dataset includes lower upper case letter digit demonstrate neurogenesis well suited addressing stabilityplasticity dilemma long challenged adaptive machine learning algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.007181879132986069, 0.07644831389188766, -0.030216600745916367, -0.029585018754005432, 0.05933033302426338, 0.008174392394721508, 0.058180071413517, 0.01502329669892788, -0.03695451468229294, 0.041550710797309875, 0.0292380228638649, 0.0026918291114270687, 0.00036305346293374896, 0.044411759823560715, 0.032330386340618134, 0.033240966498851776, 0.03276265412569046, 0.018446652218699455, 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870 | 870 | ['Sergey M. Plis', 'Devon R. Hjelm', 'Ruslan Salakhutdinov', 'Vince D. Calhoun'] | 1312.5847v3 | Deep learning methods have recently made notable advances in the tasks of
classification and representation learning. These tasks are important for brain
imaging and neuroscience discovery, making the methods attractive for porting
to a neuroimager's toolbox. Success of these methods is, in part, explained by
the flexibility of deep learning models. However, this flexibility makes the
process of porting to new areas a difficult parameter optimization problem. In
this work we demonstrate our results (and feasible parameter ranges) in
application of deep learning methods to structural and functional brain imaging
data. We also describe a novel constraint-based approach to visualizing high
dimensional data. We use it to analyze the effect of parameter choices on data
transformations. Our results show that deep learning methods are able to learn
physiologically important representations and detect latent relations in
neuroimaging data. | Deep learning for neuroimaging: a validation study | 2,013 | http://arxiv.org/pdf/1312.5847v3 | Title Deep learning neuroimaging validation study Summary Deep learning method recently made notable advance task classification representation learning task important brain imaging neuroscience discovery making method attractive porting neuroimagers toolbox Success method part explained flexibility deep learning model However flexibility make process porting new area difficult parameter optimization problem work demonstrate result feasible parameter range application deep learning method structural functional brain imaging data also describe novel constraintbased approach visualizing high dimensional data use analyze effect parameter choice data transformation result show deep learning method able learn physiologically important representation detect latent relation neuroimaging data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.01837492361664772, 0.01100934948772192, -0.02576216496527195, -0.012718332931399345, 0.02512184903025627, 0.015584123320877552, 0.04696991294622421, 0.023571521043777466, -6.270770245464519e-05, 0.053926657885313034, -0.05788812041282654, -0.022314229980111122, 0.04795408248901367, 0.06083568558096886, 0.06029123067855835, -0.02876460924744606, -0.014906677417457104, 0.04407446086406708, 0.04599138721823692, -0.013587024062871933, 0.006532173603773117, -0.023418987169861794, -0.010546550154685974, 0.031215790659189224, -0.04988817498087883, -0.00774099538102746, 0.020746437832713127, -0.04244309291243553, 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871 | 871 | ['Jost Tobias Springenberg', 'Martin Riedmiller'] | 1312.6116v2 | We present a probabilistic variant of the recently introduced maxout unit.
The success of deep neural networks utilizing maxout can partly be attributed
to favorable performance under dropout, when compared to rectified linear
units. It however also depends on the fact that each maxout unit performs a
pooling operation over a group of linear transformations and is thus partially
invariant to changes in its input. Starting from this observation we ask the
question: Can the desirable properties of maxout units be preserved while
improving their invariance properties ? We argue that our probabilistic maxout
(probout) units successfully achieve this balance. We quantitatively verify
this claim and report classification performance matching or exceeding the
current state of the art on three challenging image classification benchmarks
(CIFAR-10, CIFAR-100 and SVHN). | Improving Deep Neural Networks with Probabilistic Maxout Units | 2,013 | http://arxiv.org/pdf/1312.6116v2 | Title Improving Deep Neural Networks Probabilistic Maxout Units Summary present probabilistic variant recently introduced maxout unit success deep neural network utilizing maxout partly attributed favorable performance dropout compared rectified linear unit however also depends fact maxout unit performs pooling operation group linear transformation thus partially invariant change input Starting observation ask question desirable property maxout unit preserved improving invariance property argue probabilistic maxout probout unit successfully achieve balance quantitatively verify claim report classification performance matching exceeding current state art three challenging image classification benchmark CIFAR10 CIFAR100 SVHN Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.005381505936384201, 0.019057119265198708, 0.016910037025809288, 0.03946641832590103, -0.018241185694932938, 0.012659365311264992, 0.048342134803533554, -0.019333038479089737, -0.04003531485795975, 0.022451849654316902, 0.011953772976994514, -0.03398923575878143, 0.002058961894363165, 0.07588708400726318, 0.02126079611480236, -0.02735043875873089, -0.02109302394092083, -0.003151986747980118, -0.02371799200773239, -0.019579622894525528, 0.003312188433483243, -0.0063884928822517395, -0.01637238822877407, 0.024413762614130974, -0.061145566403865814, 0.02509390003979206, 0.029738295823335648, 0.02263226918876171, 0.012598519213497639, 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872 | 872 | ['Fabrice Rossi'] | 1407.0611v1 | In numerous applicative contexts, data are too rich and too complex to be
represented by numerical vectors. A general approach to extend machine learning
and data mining techniques to such data is to really on a dissimilarity or on a
kernel that measures how different or similar two objects are. This approach
has been used to define several variants of the Self Organizing Map (SOM). This
paper reviews those variants in using a common set of notations in order to
outline differences and similarities between them. It discusses the advantages
and drawbacks of the variants, as well as the actual relevance of the
dissimilarity/kernel SOM for practical applications. | How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need? | 2,014 | http://arxiv.org/pdf/1407.0611v1 | Title Many DissimilarityKernel Self Organizing Map Variants Need Summary numerous applicative context data rich complex represented numerical vector general approach extend machine learning data mining technique data really dissimilarity kernel measure different similar two object approach used define several variant Self Organizing Map SOM paper review variant using common set notation order outline difference similarity discus advantage drawback variant well actual relevance dissimilaritykernel SOM practical application Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03707399219274521, -0.017206881195306778, -0.04620763659477234, 0.03050832822918892, -0.014078105799853802, -0.007796098478138447, 0.07212647050619125, -0.014766501262784004, 0.0195127222687006, -0.016467800363898277, -0.03621876239776611, 0.07111173868179321, -0.01064393576234579, 0.002003050409257412, -0.027564916759729385, -0.022585174068808556, 0.04173823446035385, 0.04271737113595009, -0.06549588590860367, -0.009115859866142273, -0.0004319644649513066, -0.04143460839986801, -0.008766905404627323, 0.02520567923784256, -0.0026927299331873655, -0.03466340899467468, -0.030699653550982475, -0.03286625072360039, 0.03131682053208351, -0.06431705504655838, 0.028991147875785828, 0.05917147919535637, -0.015785397961735725, 0.02826724760234356, 2.1237121927697444e-06, -0.07871606200933456, -0.04469794034957886, 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873 | 873 | ['John R. Hershey', 'Jonathan Le Roux', 'Felix Weninger'] | 1409.2574v4 | Model-based methods and deep neural networks have both been tremendously
successful paradigms in machine learning. In model-based methods, problem
domain knowledge can be built into the constraints of the model, typically at
the expense of difficulties during inference. In contrast, deterministic deep
neural networks are constructed in such a way that inference is
straightforward, but their architectures are generic and it is unclear how to
incorporate knowledge. This work aims to obtain the advantages of both
approaches. To do so, we start with a model-based approach and an associated
inference algorithm, and \emph{unfold} the inference iterations as layers in a
deep network. Rather than optimizing the original model, we \emph{untie} the
model parameters across layers, in order to create a more powerful network. The
resulting architecture can be trained discriminatively to perform accurate
inference within a fixed network size. We show how this framework allows us to
interpret conventional networks as mean-field inference in Markov random
fields, and to obtain new architectures by instead using belief propagation as
the inference algorithm. We then show its application to a non-negative matrix
factorization model that incorporates the problem-domain knowledge that sound
sources are additive. Deep unfolding of this model yields a new kind of
non-negative deep neural network, that can be trained using a multiplicative
backpropagation-style update algorithm. We present speech enhancement
experiments showing that our approach is competitive with conventional neural
networks despite using far fewer parameters. | Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures | 2,014 | http://arxiv.org/pdf/1409.2574v4 | Title Deep Unfolding ModelBased Inspiration Novel Deep Architectures Summary Modelbased method deep neural network tremendously successful paradigm machine learning modelbased method problem domain knowledge built constraint model typically expense difficulty inference contrast deterministic deep neural network constructed way inference straightforward architecture generic unclear incorporate knowledge work aim obtain advantage approach start modelbased approach associated inference algorithm emphunfold inference iteration layer deep network Rather optimizing original model emphuntie model parameter across layer order create powerful network resulting architecture trained discriminatively perform accurate inference within fixed network size show framework allows u interpret conventional network meanfield inference Markov random field obtain new architecture instead using belief propagation inference algorithm show application nonnegative matrix factorization model incorporates problemdomain knowledge sound source additive Deep unfolding model yield new kind nonnegative deep neural network trained using multiplicative backpropagationstyle update algorithm present speech enhancement experiment showing approach competitive conventional neural network despite using far fewer parameter Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04810643196105957, 0.036373239010572433, 0.010821335017681122, 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874 | 874 | ['Niklas Wahlström', 'Thomas B. Schön', 'Marc Peter Deisenroth'] | 1410.7550v1 | Modeling dynamical systems is important in many disciplines, e.g., control,
robotics, or neurotechnology. Commonly the state of these systems is not
directly observed, but only available through noisy and potentially
high-dimensional observations. In these cases, system identification, i.e.,
finding the measurement mapping and the transition mapping (system dynamics) in
latent space can be challenging. For linear system dynamics and measurement
mappings efficient solutions for system identification are available. However,
in practical applications, the linearity assumptions does not hold, requiring
non-linear system identification techniques. If additionally the observations
are high-dimensional (e.g., images), non-linear system identification is
inherently hard. To address the problem of non-linear system identification
from high-dimensional observations, we combine recent advances in deep learning
and system identification. In particular, we jointly learn a low-dimensional
embedding of the observation by means of deep auto-encoders and a predictive
transition model in this low-dimensional space. We demonstrate that our model
enables learning good predictive models of dynamical systems from pixel
information only. | Learning deep dynamical models from image pixels | 2,014 | http://arxiv.org/pdf/1410.7550v1 | Title Learning deep dynamical model image pixel Summary Modeling dynamical system important many discipline eg control robotics neurotechnology Commonly state system directly observed available noisy potentially highdimensional observation case system identification ie finding measurement mapping transition mapping system dynamic latent space challenging linear system dynamic measurement mapping efficient solution system identification available However practical application linearity assumption hold requiring nonlinear system identification technique additionally observation highdimensional eg image nonlinear system identification inherently hard address problem nonlinear system identification highdimensional observation combine recent advance deep learning system identification particular jointly learn lowdimensional embedding observation mean deep autoencoders predictive transition model lowdimensional space demonstrate model enables learning good predictive model dynamical system pixel information Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.044298529624938965, 0.030958494171500206, -0.03170099854469299, 0.05849612504243851, -0.0041681816801428795, -0.015608853660523891, 0.04241286963224411, -0.0038738218136131763, -0.06255733221769333, 0.014287787489593029, 0.044057298451662064, 0.01682785153388977, -0.003498351201415062, 0.08716800808906555, 0.041112229228019714, -0.06274716556072235, 0.03210601210594177, -0.0001254690287169069, 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875 | 875 | ['Harri Valpola'] | 1411.7783v2 | A network supporting deep unsupervised learning is presented. The network is
an autoencoder with lateral shortcut connections from the encoder to decoder at
each level of the hierarchy. The lateral shortcut connections allow the higher
levels of the hierarchy to focus on abstract invariant features. While standard
autoencoders are analogous to latent variable models with a single layer of
stochastic variables, the proposed network is analogous to hierarchical latent
variables models. Learning combines denoising autoencoder and denoising sources
separation frameworks. Each layer of the network contributes to the cost
function a term which measures the distance of the representations produced by
the encoder and the decoder. Since training signals originate from all levels
of the network, all layers can learn efficiently even in deep networks. The
speedup offered by cost terms from higher levels of the hierarchy and the
ability to learn invariant features are demonstrated in experiments. | From neural PCA to deep unsupervised learning | 2,014 | http://arxiv.org/pdf/1411.7783v2 | Title neural PCA deep unsupervised learning Summary network supporting deep unsupervised learning presented network autoencoder lateral shortcut connection encoder decoder level hierarchy lateral shortcut connection allow higher level hierarchy focus abstract invariant feature standard autoencoders analogous latent variable model single layer stochastic variable proposed network analogous hierarchical latent variable model Learning combine denoising autoencoder denoising source separation framework layer network contributes cost function term measure distance representation produced encoder decoder Since training signal originate level network layer learn efficiently even deep network speedup offered cost term higher level hierarchy ability learn invariant feature demonstrated experiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.020638780668377876, 0.08852510899305344, -0.011860786005854607, 0.04436541721224785, 0.026439804583787918, 0.018264351412653923, 0.06064463034272194, -0.012935417704284191, -0.04578427970409393, 0.012000597082078457, 0.023024294525384903, 0.0461929589509964, -0.006049367133527994, 0.11999958753585815, 0.02286304533481598, -0.08592654764652252, 0.002790688769891858, 0.016133535653352737, 0.019802561029791832, -0.02478020265698433, -0.018470512703061104, -0.04759526625275612, -0.010544169694185257, 0.024893373250961304, -0.050056830048561096, -0.010251608677208424, 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876 | 876 | ['Ian J. Goodfellow', 'Oriol Vinyals', 'Andrew M. Saxe'] | 1412.6544v6 | Training neural networks involves solving large-scale non-convex optimization
problems. This task has long been believed to be extremely difficult, with fear
of local minima and other obstacles motivating a variety of schemes to improve
optimization, such as unsupervised pretraining. However, modern neural networks
are able to achieve negligible training error on complex tasks, using only
direct training with stochastic gradient descent. We introduce a simple
analysis technique to look for evidence that such networks are overcoming local
optima. We find that, in fact, on a straight path from initialization to
solution, a variety of state of the art neural networks never encounter any
significant obstacles. | Qualitatively characterizing neural network optimization problems | 2,014 | http://arxiv.org/pdf/1412.6544v6 | Title Qualitatively characterizing neural network optimization problem Summary Training neural network involves solving largescale nonconvex optimization problem task long believed extremely difficult fear local minimum obstacle motivating variety scheme improve optimization unsupervised pretraining However modern neural network able achieve negligible training error complex task using direct training stochastic gradient descent introduce simple analysis technique look evidence network overcoming local optimum find fact straight path initialization solution variety state art neural network never encounter significant obstacle Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.026326825842261314, 0.024307403713464737, -0.014821956865489483, 0.03130592033267021, -0.016995619982481003, -0.03342808410525322, 0.008735844865441322, -0.001642549759708345, -0.034666623920202255, 0.01714233122766018, 0.011867056600749493, 0.031946271657943726, -0.0019691416528075933, 0.02467413991689682, 0.04791052266955376, 0.012124234810471535, -0.01015138253569603, 0.0029219863936305046, -0.03140856325626373, -0.011828891932964325, -0.0015395981026813388, -0.06360625475645065, -0.0150545509532094, 0.005080306902527809, -0.07718285173177719, -0.009626896120607853, -0.018368113785982132, 0.018918786197900772, 0.015959005802869797, -0.01708499900996685, 0.042838357388973236, -0.023209266364574432, 0.012102131731808186, 0.05490966513752937, 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877 | 877 | ['Arnab Paul', 'Suresh Venkatasubramanian'] | 1412.6621v3 | Why does Deep Learning work? What representations does it capture? How do
higher-order representations emerge? We study these questions from the
perspective of group theory, thereby opening a new approach towards a theory of
Deep learning.
One factor behind the recent resurgence of the subject is a key algorithmic
step called pre-training: first search for a good generative model for the
input samples, and repeat the process one layer at a time. We show deeper
implications of this simple principle, by establishing a connection with the
interplay of orbits and stabilizers of group actions. Although the neural
networks themselves may not form groups, we show the existence of {\em shadow}
groups whose elements serve as close approximations.
Over the shadow groups, the pre-training step, originally introduced as a
mechanism to better initialize a network, becomes equivalent to a search for
features with minimal orbits. Intuitively, these features are in a way the {\em
simplest}. Which explains why a deep learning network learns simple features
first. Next, we show how the same principle, when repeated in the deeper
layers, can capture higher order representations, and why representation
complexity increases as the layers get deeper. | Why does Deep Learning work? - A perspective from Group Theory | 2,014 | http://arxiv.org/pdf/1412.6621v3 | Title Deep Learning work perspective Group Theory Summary Deep Learning work representation capture higherorder representation emerge study question perspective group theory thereby opening new approach towards theory Deep learning One factor behind recent resurgence subject key algorithmic step called pretraining first search good generative model input sample repeat process one layer time show deeper implication simple principle establishing connection interplay orbit stabilizer group action Although neural network may form group show existence em shadow group whose element serve close approximation shadow group pretraining step originally introduced mechanism better initialize network becomes equivalent search feature minimal orbit Intuitively feature way em simplest explains deep learning network learns simple feature first Next show principle repeated deeper layer capture higher order representation representation complexity increase layer get deeper Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.017468933016061783, 0.08319175988435745, -0.036700539290905, -0.01308742817491293, -0.01370431762188673, 0.003490353235974908, 0.08844083547592163, 0.004378741607069969, -0.08824407309293747, 0.004143625032156706, 0.019065510481595993, -0.03476626053452492, -0.005053828004747629, 0.03994399309158325, 0.08365806937217712, -0.09125623852014542, 0.03807130083441734, 0.02241506800055504, 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878 | 878 | ['Caglar Gulcehre', 'Marcin Moczulski', 'Yoshua Bengio'] | 1412.7419v5 | Stochastic gradient algorithms have been the main focus of large-scale
learning problems and they led to important successes in machine learning. The
convergence of SGD depends on the careful choice of learning rate and the
amount of the noise in stochastic estimates of the gradients. In this paper, we
propose a new adaptive learning rate algorithm, which utilizes curvature
information for automatically tuning the learning rates. The information about
the element-wise curvature of the loss function is estimated from the local
statistics of the stochastic first order gradients. We further propose a new
variance reduction technique to speed up the convergence. In our preliminary
experiments with deep neural networks, we obtained better performance compared
to the popular stochastic gradient algorithms. | ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient | 2,014 | http://arxiv.org/pdf/1412.7419v5 | Title ADASECANT Robust Adaptive Secant Method Stochastic Gradient Summary Stochastic gradient algorithm main focus largescale learning problem led important success machine learning convergence SGD depends careful choice learning rate amount noise stochastic estimate gradient paper propose new adaptive learning rate algorithm utilizes curvature information automatically tuning learning rate information elementwise curvature loss function estimated local statistic stochastic first order gradient propose new variance reduction technique speed convergence preliminary experiment deep neural network obtained better performance compared popular stochastic gradient algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.009424963966012001, -0.013777581974864006, -0.00192730943672359, 0.027817241847515106, 0.014022969640791416, -0.02599353902041912, 0.032566990703344345, -0.04041124880313873, -0.0035957740619778633, 0.03253929316997528, -0.001778207952156663, -0.020874634385108948, 0.022946560755372047, 0.029129309579730034, 0.00011576952965697274, -0.026607176288962364, -0.008747849613428116, 0.01740710623562336, 0.018984297290444374, -0.029582936316728592, 0.033454447984695435, -0.025866135954856873, 0.019414285197854042, -0.003184820059686899, -0.045981280505657196, -0.02482236921787262, -0.006217358633875847, 0.01854652352631092, 0.06176825612783432, -0.05845781788229942, 0.07705990970134735, 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879 | 879 | ['Yongxin Yang', 'Timothy M. Hospedales'] | 1412.7489v3 | In this paper, we provide a new neural-network based perspective on
multi-task learning (MTL) and multi-domain learning (MDL). By introducing the
concept of a semantic descriptor, this framework unifies MDL and MTL as well as
encompassing various classic and recent MTL/MDL algorithms by interpreting them
as different ways of constructing semantic descriptors. Our interpretation
provides an alternative pipeline for zero-shot learning (ZSL), where a model
for a novel class can be constructed without training data. Moreover, it leads
to a new and practically relevant problem setting of zero-shot domain
adaptation (ZSDA), which is the analogous to ZSL but for novel domains: A model
for an unseen domain can be generated by its semantic descriptor. Experiments
across this range of problems demonstrate that our framework outperforms a
variety of alternatives. | A Unified Perspective on Multi-Domain and Multi-Task Learning | 2,014 | http://arxiv.org/pdf/1412.7489v3 | Title Unified Perspective MultiDomain MultiTask Learning Summary paper provide new neuralnetwork based perspective multitask learning MTL multidomain learning MDL introducing concept semantic descriptor framework unifies MDL MTL well encompassing various classic recent MTLMDL algorithm interpreting different way constructing semantic descriptor interpretation provides alternative pipeline zeroshot learning ZSL model novel class constructed without training data Moreover lead new practically relevant problem setting zeroshot domain adaptation ZSDA analogous ZSL novel domain model unseen domain generated semantic descriptor Experiments across range problem demonstrate framework outperforms variety alternative Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03835504502058029, -0.0007626500446349382, 0.006105609238147736, 0.002690607914701104, 0.00775135587900877, 0.02102486789226532, 0.08897290378808975, -0.029509609565138817, -0.028897147625684738, -0.0681108683347702, -0.0457310825586319, -0.033412013202905655, -0.03511973097920418, 0.029965292662382126, 0.03198010474443436, -0.03133164718747139, 0.02286718040704727, 0.0017106890445575118, -0.02515902929008007, 0.02276543341577053, -0.01073949970304966, 0.006740916520357132, -0.007026101928204298, 0.05810372903943062, -0.04588866978883743, 0.015794645994901657, -0.0320625826716423, -0.0013726170873269439, 0.043578535318374634, -0.016004322096705437, 0.006758567877113819, 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880 | 880 | ['Robert A. Murphy'] | 1501.07227v5 | The random cluster model is used to define an upper bound on a distance
measure as a function of the number of data points to be classified and the
expected value of the number of classes to form in a hybrid K-means and
regression classification methodology, with the intent of detecting anomalies.
Conditions are given for the identification of classes which contain anomalies
and individual anomalies within identified classes. A neural network model
describes the decision region-separating surface for offline storage and recall
in any new anomaly detection. | A Neural Network Anomaly Detector Using the Random Cluster Model | 2,015 | http://arxiv.org/pdf/1501.07227v5 | Title Neural Network Anomaly Detector Using Random Cluster Model Summary random cluster model used define upper bound distance measure function number data point classified expected value number class form hybrid Kmeans regression classification methodology intent detecting anomaly Conditions given identification class contain anomaly individual anomaly within identified class neural network model describes decision regionseparating surface offline storage recall new anomaly detection Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew 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881 | 881 | ['Arnab Paul', 'Suresh Venkatasubramanian'] | 1504.02462v3 | Why does Deep Learning work? What representations does it capture? How do
higher-order representations emerge? We study these questions from the
perspective of group theory, thereby opening a new approach towards a theory of
Deep learning.
One factor behind the recent resurgence of the subject is a key algorithmic
step called {\em pretraining}: first search for a good generative model for the
input samples, and repeat the process one layer at a time. We show deeper
implications of this simple principle, by establishing a connection with the
interplay of orbits and stabilizers of group actions. Although the neural
networks themselves may not form groups, we show the existence of {\em shadow}
groups whose elements serve as close approximations.
Over the shadow groups, the pre-training step, originally introduced as a
mechanism to better initialize a network, becomes equivalent to a search for
features with minimal orbits. Intuitively, these features are in a way the {\em
simplest}. Which explains why a deep learning network learns simple features
first. Next, we show how the same principle, when repeated in the deeper
layers, can capture higher order representations, and why representation
complexity increases as the layers get deeper. | A Group Theoretic Perspective on Unsupervised Deep Learning | 2,015 | http://arxiv.org/pdf/1504.02462v3 | Title Group Theoretic Perspective Unsupervised Deep Learning Summary Deep Learning work representation capture higherorder representation emerge study question perspective group theory thereby opening new approach towards theory Deep learning One factor behind recent resurgence subject key algorithmic step called em pretraining first search good generative model input sample repeat process one layer time show deeper implication simple principle establishing connection interplay orbit stabilizer group action Although neural network may form group show existence em shadow group whose element serve close approximation shadow group pretraining step originally introduced mechanism better initialize network becomes equivalent search feature minimal orbit Intuitively feature way em simplest explains deep learning network learns simple feature first Next show principle repeated deeper layer capture higher order representation representation complexity increase layer get deeper Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.009884160943329334, 0.07413185387849808, -0.028366418555378914, -0.0016227675369009376, -0.008837842382490635, 0.014756530523300171, 0.07110969722270966, -0.017646580934524536, -0.0825798287987709, 0.0019772634841501713, -0.0016793075483292341, -0.04635702818632126, -0.008833921514451504, 0.03528766334056854, 0.08181171119213104, -0.08234812319278717, 0.04560336843132973, 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882 | 882 | ['Yunchen Pu', 'Xin Yuan', 'Lawrence Carin'] | 1504.04054v1 | A generative model is developed for deep (multi-layered) convolutional
dictionary learning. A novel probabilistic pooling operation is integrated into
the deep model, yielding efficient bottom-up (pretraining) and top-down
(refinement) probabilistic learning. Experimental results demonstrate powerful
capabilities of the model to learn multi-layer features from images, and
excellent classification results are obtained on the MNIST and Caltech 101
datasets. | A Generative Model for Deep Convolutional Learning | 2,015 | http://arxiv.org/pdf/1504.04054v1 | Title Generative Model Deep Convolutional Learning Summary generative model developed deep multilayered convolutional dictionary learning novel probabilistic pooling operation integrated deep model yielding efficient bottomup pretraining topdown refinement probabilistic learning Experimental result demonstrate powerful capability model learn multilayer feature image excellent classification result obtained MNIST Caltech 101 datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype 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883 | 883 | ['Zhiyuan Tang', 'Dong Wang', 'Yiqiao Pan', 'Zhiyong Zhang'] | 1506.02256v1 | Pre-training is crucial for learning deep neural networks. Most of existing
pre-training methods train simple models (e.g., restricted Boltzmann machines)
and then stack them layer by layer to form the deep structure. This layer-wise
pre-training has found strong theoretical foundation and broad empirical
support. However, it is not easy to employ such method to pre-train models
without a clear multi-layer structure,e.g., recurrent neural networks (RNNs).
This paper presents a new pre-training approach based on knowledge transfer
learning. In contrast to the layer-wise approach which trains model components
incrementally, the new approach trains the entire model as a whole but with an
easier objective function. This is achieved by utilizing soft targets produced
by a prior trained model (teacher model). Compared to the conventional
layer-wise methods, this new method does not care about the model structure, so
can be used to pre-train very complex models. Experiments on a speech
recognition task demonstrated that with this approach, complex RNNs can be well
trained with a weaker deep neural network (DNN) model. Furthermore, the new
method can be combined with conventional layer-wise pre-training to deliver
additional gains. | Knowledge Transfer Pre-training | 2,015 | http://arxiv.org/pdf/1506.02256v1 | Title Knowledge Transfer Pretraining Summary Pretraining crucial learning deep neural network existing pretraining method train simple model eg restricted Boltzmann machine stack layer layer form deep structure layerwise pretraining found strong theoretical foundation broad empirical support However easy employ method pretrain model without clear multilayer structureeg recurrent neural network RNNs paper present new pretraining approach based knowledge transfer learning contrast layerwise approach train model component incrementally new approach train entire model whole easier objective function achieved utilizing soft target produced prior trained model teacher model Compared conventional layerwise method new method care model structure used pretrain complex model Experiments speech recognition task demonstrated approach complex RNNs well trained weaker deep neural network DNN model Furthermore new method combined conventional layerwise pretraining deliver additional gain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.012281407602131367, 0.05470841005444527, 0.009022770449519157, 0.024301918223500252, 0.02616831474006176, -0.01433711126446724, 0.016306878998875618, -0.03301497548818588, -0.04225548729300499, -0.01741214282810688, -0.0617535226047039, 0.03170616924762726, -0.0021883330773562193, 0.028970902785658836, 0.005755655001848936, -0.08019335567951202, 0.006268675439059734, 0.011232540011405945, -0.01696106605231762, 0.014103774912655354, 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884 | 884 | ['Junbo Zhao', 'Michael Mathieu', 'Ross Goroshin', 'Yann LeCun'] | 1506.02351v8 | We present a novel architecture, the "stacked what-where auto-encoders"
(SWWAE), which integrates discriminative and generative pathways and provides a
unified approach to supervised, semi-supervised and unsupervised learning
without relying on sampling during training. An instantiation of SWWAE uses a
convolutional net (Convnet) (LeCun et al. (1998)) to encode the input, and
employs a deconvolutional net (Deconvnet) (Zeiler et al. (2010)) to produce the
reconstruction. The objective function includes reconstruction terms that
induce the hidden states in the Deconvnet to be similar to those of the
Convnet. Each pooling layer produces two sets of variables: the "what" which
are fed to the next layer, and its complementary variable "where" that are fed
to the corresponding layer in the generative decoder. | Stacked What-Where Auto-encoders | 2,015 | http://arxiv.org/pdf/1506.02351v8 | Title Stacked WhatWhere Autoencoders Summary present novel architecture stacked whatwhere autoencoders SWWAE integrates discriminative generative pathway provides unified approach supervised semisupervised unsupervised learning without relying sampling training instantiation SWWAE us convolutional net Convnet LeCun et al 1998 encode input employ deconvolutional net Deconvnet Zeiler et al 2010 produce reconstruction objective function includes reconstruction term induce hidden state Deconvnet similar Convnet pooling layer produce two set variable fed next layer complementary variable fed corresponding layer generative decoder Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.01981442980468273, 0.08821431547403336, -0.01798466593027115, 0.053672514855861664, -0.0015582708874717355, 0.007265186402946711, 0.06560763716697693, -0.007751486264169216, -0.011257002130150795, -0.020326735451817513, -0.043799884617328644, 0.010031813755631447, -0.005495553836226463, 0.05339856073260307, 0.0500161312520504, -0.06552159786224365, -0.00020338127797003835, 0.005250017158687115, -0.09010592848062515, -0.06711723655462265, -0.014889958314597607, -0.00985971000045538, -0.028515659272670746, 0.012213228270411491, -0.033100295811891556, 0.01906551979482174, -0.05250100418925285, -0.016619082540273666, -0.006465889513492584, 0.025444164872169495, 0.011004418134689331, 0.016471652314066887, 0.05888324975967407, 0.00937530305236578, 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885 | 885 | ['Yann Ollivier', 'Corentin Tallec', 'Guillaume Charpiat'] | 1507.07680v2 | We introduce the "NoBackTrack" algorithm to train the parameters of dynamical
systems such as recurrent neural networks. This algorithm works in an online,
memoryless setting, thus requiring no backpropagation through time, and is
scalable, avoiding the large computational and memory cost of maintaining the
full gradient of the current state with respect to the parameters.
The algorithm essentially maintains, at each time, a single search direction
in parameter space. The evolution of this search direction is partly stochastic
and is constructed in such a way to provide, at every time, an unbiased random
estimate of the gradient of the loss function with respect to the parameters.
Because the gradient estimate is unbiased, on average over time the parameter
is updated as it should.
The resulting gradient estimate can then be fed to a lightweight Kalman-like
filter to yield an improved algorithm. For recurrent neural networks, the
resulting algorithms scale linearly with the number of parameters.
Small-scale experiments confirm the suitability of the approach, showing that
the stochastic approximation of the gradient introduced in the algorithm is not
detrimental to learning. In particular, the Kalman-like version of NoBackTrack
is superior to backpropagation through time (BPTT) when the time span of
dependencies in the data is longer than the truncation span for BPTT. | Training recurrent networks online without backtracking | 2,015 | http://arxiv.org/pdf/1507.07680v2 | Title Training recurrent network online without backtracking Summary introduce NoBackTrack algorithm train parameter dynamical system recurrent neural network algorithm work online memoryless setting thus requiring backpropagation time scalable avoiding large computational memory cost maintaining full gradient current state respect parameter algorithm essentially maintains time single search direction parameter space evolution search direction partly stochastic constructed way provide every time unbiased random estimate gradient loss function respect parameter gradient estimate unbiased average time parameter updated resulting gradient estimate fed lightweight Kalmanlike filter yield improved algorithm recurrent neural network resulting algorithm scale linearly number parameter Smallscale experiment confirm suitability approach showing stochastic approximation gradient introduced algorithm detrimental learning particular Kalmanlike version NoBackTrack superior backpropagation time BPTT time span dependency data longer truncation span BPTT Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.010960999876260757, 0.04125826060771942, 0.006236083339899778, -0.0005737123428843915, 0.001664411392994225, -0.019761379808187485, -0.024306312203407288, -0.019674770534038544, -0.020873045548796654, -0.006680076476186514, 0.009317561984062195, 0.023852698504924774, 0.02771538496017456, 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886 | 886 | ['John R. Hershey', 'Zhuo Chen', 'Jonathan Le Roux', 'Shinji Watanabe'] | 1508.04306v1 | We address the problem of acoustic source separation in a deep learning
framework we call "deep clustering." Rather than directly estimating signals or
masking functions, we train a deep network to produce spectrogram embeddings
that are discriminative for partition labels given in training data. Previous
deep network approaches provide great advantages in terms of learning power and
speed, but previously it has been unclear how to use them to separate signals
in a class-independent way. In contrast, spectral clustering approaches are
flexible with respect to the classes and number of items to be segmented, but
it has been unclear how to leverage the learning power and speed of deep
networks. To obtain the best of both worlds, we use an objective function that
to train embeddings that yield a low-rank approximation to an ideal pairwise
affinity matrix, in a class-independent way. This avoids the high cost of
spectral factorization and instead produces compact clusters that are amenable
to simple clustering methods. The segmentations are therefore implicitly
encoded in the embeddings, and can be "decoded" by clustering. Preliminary
experiments show that the proposed method can separate speech: when trained on
spectrogram features containing mixtures of two speakers, and tested on
mixtures of a held-out set of speakers, it can infer masking functions that
improve signal quality by around 6dB. We show that the model can generalize to
three-speaker mixtures despite training only on two-speaker mixtures. The
framework can be used without class labels, and therefore has the potential to
be trained on a diverse set of sound types, and to generalize to novel sources.
We hope that future work will lead to segmentation of arbitrary sounds, with
extensions to microphone array methods as well as image segmentation and other
domains. | Deep clustering: Discriminative embeddings for segmentation and
separation | 2,015 | http://arxiv.org/pdf/1508.04306v1 | Title Deep clustering Discriminative embeddings segmentation separation Summary address problem acoustic source separation deep learning framework call deep clustering Rather directly estimating signal masking function train deep network produce spectrogram embeddings discriminative partition label given training data Previous deep network approach provide great advantage term learning power speed previously unclear use separate signal classindependent way contrast spectral clustering approach flexible respect class number item segmented unclear leverage learning power speed deep network obtain best world use objective function train embeddings yield lowrank approximation ideal pairwise affinity matrix classindependent way avoids high cost spectral factorization instead produce compact cluster amenable simple clustering method segmentation therefore implicitly encoded embeddings decoded clustering Preliminary experiment show proposed method separate speech trained spectrogram feature containing mixture two speaker tested mixture heldout set speaker infer masking function improve signal quality around 6dB show model generalize threespeaker mixture despite training twospeaker mixture framework used without class label therefore potential trained diverse set sound type generalize novel source hope future work lead segmentation arbitrary sound extension microphone array method well image segmentation domain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.031613484025001526, 0.0043369196355342865, 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887 | 887 | ['Aren Jansen', 'Gregory Sell', 'Vince Lyzinski'] | 1508.04422v3 | Several popular graph embedding techniques for representation learning and
dimensionality reduction rely on performing computationally expensive
eigendecompositions to derive a nonlinear transformation of the input data
space. The resulting eigenvectors encode the embedding coordinates for the
training samples only, and so the embedding of novel data samples requires
further costly computation. In this paper, we present a method for the
out-of-sample extension of graph embeddings using deep neural networks (DNN) to
parametrically approximate these nonlinear maps. Compared with traditional
nonparametric out-of-sample extension methods, we demonstrate that the DNNs can
generalize with equal or better fidelity and require orders of magnitude less
computation at test time. Moreover, we find that unsupervised pretraining of
the DNNs improves optimization for larger network sizes, thus removing
sensitivity to model selection. | Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural
Networks | 2,015 | http://arxiv.org/pdf/1508.04422v3 | Title Scalable OutofSample Extension Graph Embeddings Using Deep Neural Networks Summary Several popular graph embedding technique representation learning dimensionality reduction rely performing computationally expensive eigendecompositions derive nonlinear transformation input data space resulting eigenvectors encode embedding coordinate training sample embedding novel data sample requires costly computation paper present method outofsample extension graph embeddings using deep neural network DNN parametrically approximate nonlinear map Compared traditional nonparametric outofsample extension method demonstrate DNNs generalize equal better fidelity require order magnitude le computation test time Moreover find unsupervised pretraining DNNs improves optimization larger network size thus removing sensitivity model selection Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.026009386405348778, -0.004638715647161007, -0.00652187317609787, 0.007820820435881615, 0.02224012091755867, 0.008739415556192398, 0.0071080937050282955, -0.002112424233928323, 0.03175557032227516, 0.03627576306462288, 0.00741588743403554, 0.05737767741084099, -0.005602273158729076, 0.007984697818756104, 0.049217622727155685, -0.06499754637479782, -0.015354315750300884, 0.029747450724244118, -0.06396368891000748, -0.04761992394924164, -0.0061880252324044704, 0.01024879515171051, 0.04664969816803932, 0.03323911875486374, -0.03864938020706177, 0.0006179539486765862, -0.05246232450008392, 0.023480145260691643, -0.0034488586243242025, 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888 | 888 | ['Suyog Gupta', 'Wei Zhang', 'Fei Wang'] | 1509.04210v3 | This paper presents Rudra, a parameter server based distributed computing
framework tuned for training large-scale deep neural networks. Using variants
of the asynchronous stochastic gradient descent algorithm we study the impact
of synchronization protocol, stale gradient updates, minibatch size, learning
rates, and number of learners on runtime performance and model accuracy. We
introduce a new learning rate modulation strategy to counter the effect of
stale gradients and propose a new synchronization protocol that can effectively
bound the staleness in gradients, improve runtime performance and achieve good
model accuracy. Our empirical investigation reveals a principled approach for
distributed training of neural networks: the mini-batch size per learner should
be reduced as more learners are added to the system to preserve the model
accuracy. We validate this approach using commonly-used image classification
benchmarks: CIFAR10 and ImageNet. | Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A
Systematic Study | 2,015 | http://arxiv.org/pdf/1509.04210v3 | Title Model Accuracy Runtime Tradeoff Distributed Deep LearningA Systematic Study Summary paper present Rudra parameter server based distributed computing framework tuned training largescale deep neural network Using variant asynchronous stochastic gradient descent algorithm study impact synchronization protocol stale gradient update minibatch size learning rate number learner runtime performance model accuracy introduce new learning rate modulation strategy counter effect stale gradient propose new synchronization protocol effectively bound staleness gradient improve runtime performance achieve good model accuracy empirical investigation reveals principled approach distributed training neural network minibatch size per learner reduced learner added system preserve model accuracy validate approach using commonlyused image classification benchmark CIFAR10 ImageNet Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0023449964355677366, 0.04158483445644379, 0.010681022889912128, 0.05070742592215538, 0.0064909085631370544, 0.0016931610880419612, 0.06247880682349205, -0.03652351349592209, -0.039995741099119186, 0.03298861160874367, -0.024701006710529327, -0.010304884985089302, 0.02436564676463604, 0.03155328333377838, -0.040155116468667984, -0.03288380429148674, 0.010384537279605865, 0.06234797462821007, -0.05389366298913956, -0.045248206704854965, 0.03249402344226837, 0.01730484887957573, -0.012439627200365067, 0.0018039523856714368, -0.024410884827375412, -0.07129552960395813, -0.026194462552666664, 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889 | 889 | ['David Duvenaud', 'Dougal Maclaurin', 'Jorge Aguilera-Iparraguirre', 'Rafael Gómez-Bombarelli', 'Timothy Hirzel', 'Alán Aspuru-Guzik', 'Ryan P. Adams'] | 1509.09292v2 | We introduce a convolutional neural network that operates directly on graphs.
These networks allow end-to-end learning of prediction pipelines whose inputs
are graphs of arbitrary size and shape. The architecture we present generalizes
standard molecular feature extraction methods based on circular fingerprints.
We show that these data-driven features are more interpretable, and have better
predictive performance on a variety of tasks. | Convolutional Networks on Graphs for Learning Molecular Fingerprints | 2,015 | http://arxiv.org/pdf/1509.09292v2 | Title Convolutional Networks Graphs Learning Molecular Fingerprints Summary introduce convolutional neural network operates directly graph network allow endtoend learning prediction pipeline whose input graph arbitrary size shape architecture present generalizes standard molecular feature extraction method based circular fingerprint show datadriven feature interpretable better predictive performance variety task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed 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890 | 890 | ['Oswin Krause', 'Asja Fischer', 'Christian Igel'] | 1510.01624v4 | Estimating the log-likelihood gradient with respect to the parameters of a
Restricted Boltzmann Machine (RBM) typically requires sampling using Markov
Chain Monte Carlo (MCMC) techniques. To save computation time, the Markov
chains are only run for a small number of steps, which leads to a biased
estimate. This bias can cause RBM training algorithms such as Contrastive
Divergence (CD) learning to deteriorate. We adopt the idea behind Population
Monte Carlo (PMC) methods to devise a new RBM training algorithm termed
Population-Contrastive-Divergence (pop-CD). Compared to CD, it leads to a
consistent estimate and may have a significantly lower bias. Its computational
overhead is negligible compared to CD. However, the variance of the gradient
estimate increases. We experimentally show that pop-CD can significantly
outperform CD. In many cases, we observed a smaller bias and achieved higher
log-likelihood values. However, when the RBM distribution has many hidden
neurons, the consistent estimate of pop-CD may still have a considerable bias
and the variance of the gradient estimate requires a smaller learning rate.
Thus, despite its superior theoretical properties, it is not advisable to use
pop-CD in its current form on large problems. | Population-Contrastive-Divergence: Does Consistency help with RBM
training? | 2,015 | http://arxiv.org/pdf/1510.01624v4 | Title PopulationContrastiveDivergence Consistency help RBM training Summary Estimating loglikelihood gradient respect parameter Restricted Boltzmann Machine RBM typically requires sampling using Markov Chain Monte Carlo MCMC technique save computation time Markov chain run small number step lead biased estimate bias cause RBM training algorithm Contrastive Divergence CD learning deteriorate adopt idea behind Population Monte Carlo PMC method devise new RBM training algorithm termed PopulationContrastiveDivergence popCD Compared CD lead consistent estimate may significantly lower bias computational overhead negligible compared CD However variance gradient estimate increase experimentally show popCD significantly outperform CD many case observed smaller bias achieved higher loglikelihood value However RBM distribution many hidden neuron consistent estimate popCD may still considerable bias variance gradient estimate requires smaller learning rate Thus despite superior theoretical property advisable use popCD current form large problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.01718243584036827, -0.009649938903748989, -0.010894007980823517, -0.009393826127052307, -0.006687752902507782, -0.025903603062033653, 0.034462735056877136, -0.005490076262503862, -0.05730156973004341, 0.031931161880493164, 0.008940186351537704, 0.0010358005529269576, 0.04077569395303726, 0.005169510841369629, 0.022836346179246902, -0.017432743683457375, -0.0030665688682347536, 0.005868304520845413, -0.01725097745656967, 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891 | 891 | ['Izhar Wallach', 'Michael Dzamba', 'Abraham Heifets'] | 1510.02855v1 | Deep convolutional neural networks comprise a subclass of deep neural
networks (DNN) with a constrained architecture that leverages the spatial and
temporal structure of the domain they model. Convolutional networks achieve the
best predictive performance in areas such as speech and image recognition by
hierarchically composing simple local features into complex models. Although
DNNs have been used in drug discovery for QSAR and ligand-based bioactivity
predictions, none of these models have benefited from this powerful
convolutional architecture. This paper introduces AtomNet, the first
structure-based, deep convolutional neural network designed to predict the
bioactivity of small molecules for drug discovery applications. We demonstrate
how to apply the convolutional concepts of feature locality and hierarchical
composition to the modeling of bioactivity and chemical interactions. In
further contrast to existing DNN techniques, we show that AtomNet's application
of local convolutional filters to structural target information successfully
predicts new active molecules for targets with no previously known modulators.
Finally, we show that AtomNet outperforms previous docking approaches on a
diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9
on 57.8% of the targets in the DUDE benchmark. | AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction
in Structure-based Drug Discovery | 2,015 | http://arxiv.org/pdf/1510.02855v1 | Title AtomNet Deep Convolutional Neural Network Bioactivity Prediction Structurebased Drug Discovery Summary Deep convolutional neural network comprise subclass deep neural network DNN constrained architecture leverage spatial temporal structure domain model Convolutional network achieve best predictive performance area speech image recognition hierarchically composing simple local feature complex model Although DNNs used drug discovery QSAR ligandbased bioactivity prediction none model benefited powerful convolutional architecture paper introduces AtomNet first structurebased deep convolutional neural network designed predict bioactivity small molecule drug discovery application demonstrate apply convolutional concept feature locality hierarchical composition modeling bioactivity chemical interaction contrast existing DNN technique show AtomNets application local convolutional filter structural target information successfully predicts new active molecule target previously known modulators Finally show AtomNet outperforms previous docking approach diverse set benchmark large margin achieving AUC greater 09 578 target DUDE benchmark Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.010271945968270302, 0.053097814321517944, -0.019327670335769653, 0.025630924850702286, 0.045571018010377884, -0.03217602148652077, 0.06780460476875305, 0.019847920164465904, 0.048916690051555634, 0.017969787120819092, -0.04063716530799866, 0.03772808238863945, -0.016215762123465538, 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892 | 892 | ['Nicolas Papernot', 'Patrick McDaniel', 'Xi Wu', 'Somesh Jha', 'Ananthram Swami'] | 1511.04508v2 | Deep learning algorithms have been shown to perform extremely well on many
classical machine learning problems. However, recent studies have shown that
deep learning, like other machine learning techniques, is vulnerable to
adversarial samples: inputs crafted to force a deep neural network (DNN) to
provide adversary-selected outputs. Such attacks can seriously undermine the
security of the system supported by the DNN, sometimes with devastating
consequences. For example, autonomous vehicles can be crashed, illicit or
illegal content can bypass content filters, or biometric authentication systems
can be manipulated to allow improper access. In this work, we introduce a
defensive mechanism called defensive distillation to reduce the effectiveness
of adversarial samples on DNNs. We analytically investigate the
generalizability and robustness properties granted by the use of defensive
distillation when training DNNs. We also empirically study the effectiveness of
our defense mechanisms on two DNNs placed in adversarial settings. The study
shows that defensive distillation can reduce effectiveness of sample creation
from 95% to less than 0.5% on a studied DNN. Such dramatic gains can be
explained by the fact that distillation leads gradients used in adversarial
sample creation to be reduced by a factor of 10^30. We also find that
distillation increases the average minimum number of features that need to be
modified to create adversarial samples by about 800% on one of the DNNs we
tested. | Distillation as a Defense to Adversarial Perturbations against Deep
Neural Networks | 2,015 | http://arxiv.org/pdf/1511.04508v2 | Title Distillation Defense Adversarial Perturbations Deep Neural Networks Summary Deep learning algorithm shown perform extremely well many classical machine learning problem However recent study shown deep learning like machine learning technique vulnerable adversarial sample input crafted force deep neural network DNN provide adversaryselected output attack seriously undermine security system supported DNN sometimes devastating consequence example autonomous vehicle crashed illicit illegal content bypass content filter biometric authentication system manipulated allow improper access work introduce defensive mechanism called defensive distillation reduce effectiveness adversarial sample DNNs analytically investigate generalizability robustness property granted use defensive distillation training DNNs also empirically study effectiveness defense mechanism two DNNs placed adversarial setting study show defensive distillation reduce effectiveness sample creation 95 le 05 studied DNN dramatic gain explained fact distillation lead gradient used adversarial sample creation reduced factor 1030 also find distillation increase average minimum number feature need modified create adversarial sample 800 one DNNs tested Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.00978070218116045, 0.056645773351192474, -0.016303276643157005, 0.007887858897447586, -0.03942437097430229, -0.020568089559674263, 0.04720168560743332, -0.01884683221578598, -0.028821565210819244, -0.017682690173387527, 0.023345734924077988, 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893 | 893 | ['Dustin Tran', 'Rajesh Ranganath', 'David M. Blei'] | 1511.06499v4 | Variational inference is a powerful tool for approximate inference, and it
has been recently applied for representation learning with deep generative
models. We develop the variational Gaussian process (VGP), a Bayesian
nonparametric variational family, which adapts its shape to match complex
posterior distributions. The VGP generates approximate posterior samples by
generating latent inputs and warping them through random non-linear mappings;
the distribution over random mappings is learned during inference, enabling the
transformed outputs to adapt to varying complexity. We prove a universal
approximation theorem for the VGP, demonstrating its representative power for
learning any model. For inference we present a variational objective inspired
by auto-encoders and perform black box inference over a wide class of models.
The VGP achieves new state-of-the-art results for unsupervised learning,
inferring models such as the deep latent Gaussian model and the recently
proposed DRAW. | The Variational Gaussian Process | 2,015 | http://arxiv.org/pdf/1511.06499v4 | Title Variational Gaussian Process Summary Variational inference powerful tool approximate inference recently applied representation learning deep generative model develop variational Gaussian process VGP Bayesian nonparametric variational family adapts shape match complex posterior distribution VGP generates approximate posterior sample generating latent input warping random nonlinear mapping distribution random mapping learned inference enabling transformed output adapt varying complexity prove universal approximation theorem VGP demonstrating representative power learning model inference present variational objective inspired autoencoders perform black box inference wide class model VGP achieves new stateoftheart result unsupervised learning inferring model deep latent Gaussian model recently proposed DRAW Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.020552849397063255, 0.09730485081672668, -0.01959320902824402, -0.021689001470804214, -0.012611591257154942, -0.008213704451918602, 0.015472936443984509, -0.02307627536356449, -0.08204922080039978, 0.02722841314971447, 0.01838023029267788, -0.0056540523655712605, 0.0158979631960392, 0.08420240879058838, 0.05253027379512787, -0.10512328147888184, -0.01731140725314617, -0.04108366370201111, -0.015310111455619335, -0.03645196184515953, 0.005200076382607222, -0.011414436623454094, -0.0017574902158230543, 0.04867912828922272, -0.06823458522558212, 0.027473513036966324, -0.01919875107705593, -0.005790406372398138, 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894 | 894 | ['Ilia Zintchenko', 'Matthew Hastings', 'Nathan Wiebe', 'Ethan Brown', 'Matthias Troyer'] | 1512.03025v1 | Heuristic optimisers which search for an optimal configuration of variables
relative to an objective function often get stuck in local optima where the
algorithm is unable to find further improvement. The standard approach to
circumvent this problem involves periodically restarting the algorithm from
random initial configurations when no further improvement can be found. We
propose a method of partial reinitialization, whereby, in an attempt to find a
better solution, only sub-sets of variables are re-initialised rather than the
whole configuration. Much of the information gained from previous runs is hence
retained. This leads to significant improvements in the quality of the solution
found in a given time for a variety of optimisation problems in machine
learning. | Partial Reinitialisation for Optimisers | 2,015 | http://arxiv.org/pdf/1512.03025v1 | Title Partial Reinitialisation Optimisers Summary Heuristic optimisers search optimal configuration variable relative objective function often get stuck local optimum algorithm unable find improvement standard approach circumvent problem involves periodically restarting algorithm random initial configuration improvement found propose method partial reinitialization whereby attempt find better solution subset variable reinitialised rather whole configuration Much information gained previous run hence retained lead significant improvement quality solution found given time variety optimisation problem machine learning Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.037638649344444275, -0.0100403456017375, -0.012705590575933456, -0.029467321932315826, 0.01377557497471571, -0.05436856299638748, -0.03977998346090317, 0.008652768097817898, -0.03583249822258949, -0.031532831490039825, 0.06650076061487198, 0.03275195509195328, -0.010251748375594616, 0.05194227397441864, -0.024784373119473457, -0.009407231584191322, -0.00811169482767582, 0.02171306125819683, -0.030379392206668854, -0.005436483770608902, 0.014201173558831215, -0.05877307802438736, 0.02395523153245449, -0.003689180826768279, -0.04944963753223419, -0.004456052556633949, -0.05078992620110512, 0.03763028234243393, 0.03749583661556244, -0.003713727928698063, 0.017861954867839813, -0.016975177451968193, 0.012948279269039631, 0.007824461907148361, 2.5608076157368487e-06, -0.05009600892663002, 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895 | 895 | ['Ronen Basri', 'David Jacobs'] | 1602.04723v1 | We consider the ability of deep neural networks to represent data that lies
near a low-dimensional manifold in a high-dimensional space. We show that deep
networks can efficiently extract the intrinsic, low-dimensional coordinates of
such data. We first show that the first two layers of a deep network can
exactly embed points lying on a monotonic chain, a special type of piecewise
linear manifold, mapping them to a low-dimensional Euclidean space. Remarkably,
the network can do this using an almost optimal number of parameters. We also
show that this network projects nearby points onto the manifold and then embeds
them with little error. We then extend these results to more general manifolds. | Efficient Representation of Low-Dimensional Manifolds using Deep
Networks | 2,016 | http://arxiv.org/pdf/1602.04723v1 | Title Efficient Representation LowDimensional Manifolds using Deep Networks Summary consider ability deep neural network represent data lie near lowdimensional manifold highdimensional space show deep network efficiently extract intrinsic lowdimensional coordinate data first show first two layer deep network exactly embed point lying monotonic chain special type piecewise linear manifold mapping lowdimensional Euclidean space Remarkably network using almost optimal number parameter also show network project nearby point onto manifold embeds little error extend result general manifold Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.08361966162919998, 0.02978036180138588, -0.018027890473604202, 0.045523494482040405, -0.05180705338716507, 0.012776571325957775, 0.04660985991358757, 0.000518214306794107, 0.020451143383979797, 0.00042285610106773674, 0.00424810079857707, 0.040981099009513855, -0.025472229346632957, 0.059133823961019516, 0.06639036536216736, -0.0898451879620552, -0.054076097905635834, -0.008612526580691338, -0.023523129522800446, -0.05845676362514496, -0.043613653630018234, -0.01610761322081089, 0.0055643427185714245, -0.001816338044591248, -0.057211149483919144, -0.03420537710189819, -0.02670525386929512, -0.02769327722489834, -0.0026370235718786716, -0.04706328734755516, -0.017318658530712128, -0.016574835404753685, 0.059445980936288834, 0.015660127624869347, 2.0084694369870704e-06, 0.02196616679430008, -0.004836792591959238, -0.007265908643603325, 0.025026505813002586, 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896 | 896 | ['André L. V. Coelho', 'Fabrício O. de França'] | 1603.06859v1 | Perceptrons are neuronal devices capable of fully discriminating linearly
separable classes. Although straightforward to implement and train, their
applicability is usually hindered by non-trivial requirements imposed by
real-world classification problems. Therefore, several approaches, such as
kernel perceptrons, have been conceived to counteract such difficulties. In
this paper, we investigate an enhanced perceptron model based on the notion of
contrastive biclusters. From this perspective, a good discriminative bicluster
comprises a subset of data instances belonging to one class that show high
coherence across a subset of features and high differentiation from nearest
instances of the other class under the same features (referred to as its
contrastive bicluster). Upon each local subspace associated with a pair of
contrastive biclusters a perceptron is trained and the model with highest area
under the receiver operating characteristic curve (AUC) value is selected as
the final classifier. Experiments conducted on a range of data sets, including
those related to a difficult biosignal classification problem, show that the
proposed variant can be indeed very useful, prevailing in most of the cases
upon standard and kernel perceptrons in terms of accuracy and AUC measures. | Enhanced perceptrons using contrastive biclusters | 2,016 | http://arxiv.org/pdf/1603.06859v1 | Title Enhanced perceptrons using contrastive biclusters Summary Perceptrons neuronal device capable fully discriminating linearly separable class Although straightforward implement train applicability usually hindered nontrivial requirement imposed realworld classification problem Therefore several approach kernel perceptrons conceived counteract difficulty paper investigate enhanced perceptron model based notion contrastive biclusters perspective good discriminative bicluster comprises subset data instance belonging one class show high coherence across subset feature high differentiation nearest instance class feature referred contrastive bicluster Upon local subspace associated pair contrastive biclusters perceptron trained model highest area receiver operating characteristic curve AUC value selected final classifier Experiments conducted range data set including related difficult biosignal classification problem show proposed variant indeed useful prevailing case upon standard kernel perceptrons term accuracy AUC measure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.025995424017310143, -0.018527960404753685, -0.04075160250067711, 0.07005468755960464, -0.03558843210339546, 0.002725470345467329, 0.07659794390201569, 0.009443908929824829, 0.06532938033342361, -0.012848589569330215, -0.04604990780353546, 0.011575860902667046, -0.01611573062837124, 0.020199354737997055, 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897 | 897 | ['Tetsuya Sakurai', 'Akira Imakura', 'Yuto Inoue', 'Yasunori Futamura'] | 1605.04639v1 | The backpropagation algorithm for calculating gradients has been widely used
in computation of weights for deep neural networks (DNNs). This method requires
derivatives of objective functions and has some difficulties finding
appropriate parameters such as learning rate. In this paper, we propose a novel
approach for computing weight matrices of fully-connected DNNs by using two
types of semi-nonnegative matrix factorizations (semi-NMFs). In this method,
optimization processes are performed by calculating weight matrices
alternately, and backpropagation (BP) is not used. We also present a method to
calculate stacked autoencoder using a NMF. The output results of the
autoencoder are used as pre-training data for DNNs. The experimental results
show that our method using three types of NMFs attains similar error rates to
the conventional DNNs with BP. | Alternating optimization method based on nonnegative matrix
factorizations for deep neural networks | 2,016 | http://arxiv.org/pdf/1605.04639v1 | Title Alternating optimization method based nonnegative matrix factorization deep neural network Summary backpropagation algorithm calculating gradient widely used computation weight deep neural network DNNs method requires derivative objective function difficulty finding appropriate parameter learning rate paper propose novel approach computing weight matrix fullyconnected DNNs using two type seminonnegative matrix factorization semiNMFs method optimization process performed calculating weight matrix alternately backpropagation BP used also present method calculate stacked autoencoder using NMF output result autoencoder used pretraining data DNNs experimental result show method using three type NMFs attains similar error rate conventional DNNs BP Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02732893079519272, 0.02925928123295307, -0.006862843409180641, 0.055406827479600906, 0.027116430923342705, -0.028970245271921158, 0.01844571717083454, -0.021081620827317238, -0.022841472178697586, -0.01237912755459547, -0.07110622525215149, -0.017962569370865822, -0.008172176778316498, 0.041417695581912994, 0.03918153792619705, 0.03658575564622879, -0.0063641853630542755, 0.02413022145628929, -0.07167334109544754, -0.010269162245094776, -0.0030979427974671125, -0.031584981828927994, -0.02881918102502823, -0.02082490175962448, -0.035898201167583466, 0.02374892681837082, -0.044002242386341095, 0.022514881566166878, 0.03110707923769951, -0.033079687505960464, 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898 | 898 | ['Jure Sokolic', 'Raja Giryes', 'Guillermo Sapiro', 'Miguel R. D. Rodrigues'] | 1605.08254v3 | The generalization error of deep neural networks via their classification
margin is studied in this work. Our approach is based on the Jacobian matrix of
a deep neural network and can be applied to networks with arbitrary
non-linearities and pooling layers, and to networks with different
architectures such as feed forward networks and residual networks. Our analysis
leads to the conclusion that a bounded spectral norm of the network's Jacobian
matrix in the neighbourhood of the training samples is crucial for a deep
neural network of arbitrary depth and width to generalize well. This is a
significant improvement over the current bounds in the literature, which imply
that the generalization error grows with either the width or the depth of the
network. Moreover, it shows that the recently proposed batch normalization and
weight normalization re-parametrizations enjoy good generalization properties,
and leads to a novel network regularizer based on the network's Jacobian
matrix. The analysis is supported with experimental results on the MNIST,
CIFAR-10, LaRED and ImageNet datasets. | Robust Large Margin Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1605.08254v3 | Title Robust Large Margin Deep Neural Networks Summary generalization error deep neural network via classification margin studied work approach based Jacobian matrix deep neural network applied network arbitrary nonlinearities pooling layer network different architecture feed forward network residual network analysis lead conclusion bounded spectral norm network Jacobian matrix neighbourhood training sample crucial deep neural network arbitrary depth width generalize well significant improvement current bound literature imply generalization error grows either width depth network Moreover show recently proposed batch normalization weight normalization reparametrizations enjoy good generalization property lead novel network regularizer based network Jacobian matrix analysis supported experimental result MNIST CIFAR10 LaRED ImageNet datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.011793049052357674, 0.015552207827568054, -0.02487330697476864, 0.05208481103181839, 0.04645862057805061, -0.029161689803004265, 0.027501219883561134, -0.020609071478247643, -0.0381009578704834, 0.03582538664340973, -0.019938170909881592, 0.03781475871801376, -0.0024499783758074045, 0.018411971628665924, 0.019852040335536003, -0.028502382338047028, -0.051715295761823654, 0.019154639914631844, -0.06276693195104599, -0.04841093346476555, 0.026434391736984253, -0.03505290672183037, 0.03238162025809288, 0.024312224239110947, -0.05582362040877342, 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899 | 899 | ['Daniel Soudry', 'Yair Carmon'] | 1605.08361v2 | We use smoothed analysis techniques to provide guarantees on the training
loss of Multilayer Neural Networks (MNNs) at differentiable local minima.
Specifically, we examine MNNs with piecewise linear activation functions,
quadratic loss and a single output, under mild over-parametrization. We prove
that for a MNN with one hidden layer, the training error is zero at every
differentiable local minimum, for almost every dataset and dropout-like noise
realization. We then extend these results to the case of more than one hidden
layer. Our theoretical guarantees assume essentially nothing on the training
data, and are verified numerically. These results suggest why the highly
non-convex loss of such MNNs can be easily optimized using local updates (e.g.,
stochastic gradient descent), as observed empirically. | No bad local minima: Data independent training error guarantees for
multilayer neural networks | 2,016 | http://arxiv.org/pdf/1605.08361v2 | Title bad local minimum Data independent training error guarantee multilayer neural network Summary use smoothed analysis technique provide guarantee training loss Multilayer Neural Networks MNNs differentiable local minimum Specifically examine MNNs piecewise linear activation function quadratic loss single output mild overparametrization prove MNN one hidden layer training error zero every differentiable local minimum almost every dataset dropoutlike noise realization extend result case one hidden layer theoretical guarantee assume essentially nothing training data verified numerically result suggest highly nonconvex loss MNNs easily optimized using local update eg stochastic gradient descent observed empirically Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02413204498589039, 0.04211192950606346, -0.0025384053587913513, 0.04445235803723335, 0.013818440027534962, -0.03091418743133545, 0.07037099450826645, 0.00656122388318181, -0.02051335759460926, 0.011605988256633282, -0.0040564266964793205, 0.046770330518484116, -0.009697603061795235, 0.022402862086892128, 0.04765422269701958, 0.0001715153775876388, -0.007022332400083542, 0.045894842594861984, 0.007695750333368778, -0.03943213075399399, 0.0200491975992918, -0.02453768625855446, -0.009839760139584541, 0.019746433943510056, -0.06967011839151382, -0.02100425958633423, 0.014402881264686584, 0.03148796781897545, -0.012735750526189804, -0.010862291790544987, 0.07150232046842575, -0.044074930250644684, -0.005572623107582331, 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Subsets and Splits