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700 | 700 | ['A. J. Tallón-Ballesteros', 'P. A. Gutiérrez-Peña', 'C. Hervás-Martínez'] | 1205.3336v1 | This paper deals with the distributed processing in the search for an optimum
classification model using evolutionary product unit neural networks. For this
distributed search we used a cluster of computers. Our objective is to obtain a
more efficient design than those net architectures which do not use a
distributed process and which thus result in simpler designs. In order to get
the best classification models we use evolutionary algorithms to train and
design neural networks, which require a very time consuming computation. The
reasons behind the need for this distribution are various. It is complicated to
train this type of nets because of the difficulty entailed in determining their
architecture due to the complex error surface. On the other hand, the use of
evolutionary algorithms involves running a great number of tests with different
seeds and parameters, thus resulting in a high computational cost | Distribution of the search of evolutionary product unit neural networks
for classification | 2,012 | http://arxiv.org/pdf/1205.3336v1 | Title Distribution search evolutionary product unit neural network classification Summary paper deal distributed processing search optimum classification model using evolutionary product unit neural network distributed search used cluster computer objective obtain efficient design net architecture use distributed process thus result simpler design order get best classification model use evolutionary algorithm train design neural network require time consuming computation reason behind need distribution various complicated train type net difficulty entailed determining architecture due complex error surface hand use evolutionary algorithm involves running great number test different seed parameter thus resulting high computational cost 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 | [-0.017015954479575157, 0.04218994826078415, -0.0480910949409008, 0.035894256085157394, -0.011669846251606941, -0.019090872257947922, 0.07465261965990067, 0.0159330852329731, -0.03907737880945206, -0.002233824459835887, 0.02942030131816864, -0.011982315219938755, 0.005564785096794367, 0.051176197826862335, 0.008661198429763317, 0.010371286422014236, 0.026942521333694458, 0.04612082615494728, -0.060016047209501266, -0.024737266823649406, 0.008659048937261105, -0.015898019075393677, 0.05206676572561264, 0.055266886949539185, -0.031112084165215492, 0.03073553368449211, -0.050488848239183426, -0.012796798720955849, -0.015209507197141647, -0.03269601985812187, 0.048556793481111526, -0.01096528209745884, 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701 | 701 | ['Baochen Sun', 'Jiashi Feng', 'Kate Saenko'] | 1612.01939v1 | In this chapter, we present CORrelation ALignment (CORAL), a simple yet
effective method for unsupervised domain adaptation. CORAL minimizes domain
shift by aligning the second-order statistics of source and target
distributions, without requiring any target labels. In contrast to subspace
manifold methods, it aligns the original feature distributions of the source
and target domains, rather than the bases of lower-dimensional subspaces. It is
also much simpler than other distribution matching methods. CORAL performs
remarkably well in extensive evaluations on standard benchmark datasets. We
first describe a solution that applies a linear transformation to source
features to align them with target features before classifier training. For
linear classifiers, we propose to equivalently apply CORAL to the classifier
weights, leading to added efficiency when the number of classifiers is small
but the number and dimensionality of target examples are very high. The
resulting CORAL Linear Discriminant Analysis (CORAL-LDA) outperforms LDA by a
large margin on standard domain adaptation benchmarks. Finally, we extend CORAL
to learn a nonlinear transformation that aligns correlations of layer
activations in deep neural networks (DNNs). The resulting Deep CORAL approach
works seamlessly with DNNs and achieves state-of-the-art performance on
standard benchmark datasets. Our code is available
at:~\url{https://github.com/VisionLearningGroup/CORAL} | Correlation Alignment for Unsupervised Domain Adaptation | 2,016 | http://arxiv.org/pdf/1612.01939v1 | Title Correlation Alignment Unsupervised Domain Adaptation Summary chapter present CORrelation ALignment CORAL simple yet effective method unsupervised domain adaptation CORAL minimizes domain shift aligning secondorder statistic source target distribution without requiring target label contrast subspace manifold method aligns original feature distribution source target domain rather base lowerdimensional subspace also much simpler distribution matching method CORAL performs remarkably well extensive evaluation standard benchmark datasets first describe solution applies linear transformation source feature align target feature classifier training linear classifier propose equivalently apply CORAL classifier weight leading added efficiency number classifier small number dimensionality target example high resulting CORAL Linear Discriminant Analysis CORALLDA outperforms LDA large margin standard domain adaptation benchmark Finally extend CORAL learn nonlinear transformation aligns correlation layer activation deep neural network DNNs resulting Deep CORAL approach work seamlessly DNNs achieves stateoftheart performance standard benchmark datasets code available aturlhttpsgithubcomVisionLearningGroupCORAL 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 | [-0.003157225204631686, 0.055424828082323074, -0.01645541936159134, 0.009064463898539543, -0.008572671562433243, 0.014969167299568653, 0.03380617871880531, 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702 | 702 | ['Gundram Leifert', 'Tobias Strauß', 'Tobias Grüning', 'Roger Labahn'] | 1605.08412v1 | We describe CITlab's recognition system for the HTRtS competition attached to
the 13. International Conference on Document Analysis and Recognition, ICDAR
2015. The task comprises the recognition of historical handwritten documents.
The core algorithms of our system are based on multi-dimensional recurrent
neural networks (MDRNN) and connectionist temporal classification (CTC). The
software modules behind that as well as the basic utility technologies are
essentially powered by PLANET's ARGUS framework for intelligent text
recognition and image processing. | CITlab ARGUS for historical handwritten documents | 2,016 | http://arxiv.org/pdf/1605.08412v1 | Title CITlab ARGUS historical handwritten document Summary describe CITlabs recognition system HTRtS competition attached 13 International Conference Document Analysis Recognition ICDAR 2015 task comprises recognition historical handwritten document core algorithm system based multidimensional recurrent neural network MDRNN connectionist temporal classification CTC software module behind well basic utility technology essentially powered PLANETs ARGUS framework intelligent text recognition image processing 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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703 | 703 | ['Keyu Lu', 'Jian Li', 'Xiangjing An', 'Hangen He'] | 1610.09609v1 | Vision-based object detection is one of the fundamental functions in numerous
traffic scene applications such as self-driving vehicle systems and advance
driver assistance systems (ADAS). However, it is also a challenging task due to
the diversity of traffic scene and the storage, power and computing source
limitations of the platforms for traffic scene applications. This paper
presents a generalized Haar filter based deep network which is suitable for the
object detection tasks in traffic scene. In this approach, we first decompose a
object detection task into several easier local regression tasks. Then, we
handle the local regression tasks by using several tiny deep networks which
simultaneously output the bounding boxes, categories and confidence scores of
detected objects. To reduce the consumption of storage and computing resources,
the weights of the deep networks are constrained to the form of generalized
Haar filter in training phase. Additionally, we introduce the strategy of
sparse windows generation to improve the efficiency of the algorithm. Finally,
we perform several experiments to validate the performance of our proposed
approach. Experimental results demonstrate that the proposed approach is both
efficient and effective in traffic scene compared with the state-of-the-art. | Generalized Haar Filter based Deep Networks for Real-Time Object
Detection in Traffic Scene | 2,016 | http://arxiv.org/pdf/1610.09609v1 | Title Generalized Haar Filter based Deep Networks RealTime Object Detection Traffic Scene Summary Visionbased object detection one fundamental function numerous traffic scene application selfdriving vehicle system advance driver assistance system ADAS However also challenging task due diversity traffic scene storage power computing source limitation platform traffic scene application paper present generalized Haar filter based deep network suitable object detection task traffic scene approach first decompose object detection task several easier local regression task handle local regression task using several tiny deep network simultaneously output bounding box category confidence score detected object reduce consumption storage computing resource weight deep network constrained form generalized Haar filter training phase Additionally introduce strategy sparse window generation improve efficiency algorithm Finally perform several experiment validate performance proposed approach Experimental result demonstrate proposed approach efficient effective traffic scene compared stateoftheart 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 | [-0.026212334632873535, 0.0003751225012820214, -0.013209350407123566, 0.05791609361767769, -0.042799949645996094, 0.0005910324980504811, 0.03361477330327034, -0.002968650311231613, -0.0026382768992334604, -0.003895937232300639, 0.03201752156019211, -0.004890859127044678, -0.023898780345916748, 0.08317878097295761, 0.01576986536383629, -0.05350806191563606, 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704 | 704 | ['Weishan Dong', 'Ting Yuan', 'Kai Yang', 'Changsheng Li', 'Shilei Zhang'] | 1701.01272v1 | In this paper, we study learning generalized driving style representations
from automobile GPS trip data. We propose a novel Autoencoder Regularized deep
neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers'
driving styles directly from GPS records, by combining supervised and
unsupervised feature learning in a unified architecture. Experiments on a
challenging driver number estimation problem and the driver identification
problem show that ARNet can learn a good generalized driving style
representation: It significantly outperforms existing methods and alternative
architectures by reaching the least estimation error on average (0.68, less
than one driver) and the highest identification accuracy (by at least 3%
improvement) compared with traditional supervised learning methods. | Autoencoder Regularized Network For Driving Style Representation
Learning | 2,017 | http://arxiv.org/pdf/1701.01272v1 | Title Autoencoder Regularized Network Driving Style Representation Learning Summary paper study learning generalized driving style representation automobile GPS trip data propose novel Autoencoder Regularized deep neural Network ARNet trip encoding framework trip2vec learn driver driving style directly GPS record combining supervised unsupervised feature learning unified architecture Experiments challenging driver number estimation problem driver identification problem show ARNet learn good generalized driving style representation significantly outperforms existing method alternative architecture reaching least estimation error average 068 le one driver highest identification accuracy least 3 improvement compared traditional supervised 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 | [-0.03630707785487175, 0.038288988173007965, -0.03017522022128105, 0.04267675429582596, 0.026386911049485207, 0.012322071008384228, 0.02979334257543087, -0.004941744729876518, -0.04290768876671791, -0.02354791760444641, 0.02713371440768242, 0.01855294406414032, 0.0042855404317379, 0.04404690861701965, 0.05882487818598747, -0.07217371463775635, -0.03145913407206535, -0.027746785432100296, -0.02351810410618782, -0.0015881828730925918, -0.0001992895413422957, 0.0016728012124076486, 0.02338458225131035, 0.02996637672185898, -0.03684719651937485, 0.023363616317510605, 0.002010882366448641, -0.010179909877479076, 0.08599361777305603, -0.040568962693214417, 0.003685838310047984, 0.014170754700899124, 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705 | 705 | ['Prutha Date', 'Ashwinkumar Ganesan', 'Tim Oates'] | 1707.09899v1 | Convolutional Neural Networks have been highly successful in performing a
host of computer vision tasks such as object recognition, object detection,
image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how
the style of a painter can be extracted from an image of the painting and
applied to another normal photograph, thus recreating the photo in the style of
the painter. The method has been successfully applied to a wide range of images
and has since spawned multiple applications and mobile apps. In this paper, the
neural style transfer algorithm is applied to fashion so as to synthesize new
custom clothes. We construct an approach to personalize and generate new custom
clothes based on a users preference and by learning the users fashion choices
from a limited set of clothes from their closet. The approach is evaluated by
analyzing the generated images of clothes and how well they align with the
users fashion style. | Fashioning with Networks: Neural Style Transfer to Design Clothes | 2,017 | http://arxiv.org/pdf/1707.09899v1 | Title Fashioning Networks Neural Style Transfer Design Clothes Summary Convolutional Neural Networks highly successful performing host computer vision task object recognition object detection image segmentation texture synthesis 2015 Gatys et al 7 show style painter extracted image painting applied another normal photograph thus recreating photo style painter method successfully applied wide range image since spawned multiple application mobile apps paper neural style transfer algorithm applied fashion synthesize new custom clothes construct approach personalize generate new custom clothes based user preference learning user fashion choice limited set clothes closet approach evaluated analyzing generated image clothes well align user fashion style 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 | [-0.00823851116001606, 0.062297869473695755, 0.004127266816794872, 0.0646948516368866, 0.0053047193214297295, -0.0337434858083725, 0.020034287124872208, 0.00061299919616431, -0.0553683266043663, 0.0040498278103768826, -0.006196638569235802, 0.02744789980351925, 0.00698753260076046, 0.06729361414909363, 0.047597866505384445, -0.02112150751054287, -0.05843931809067726, 0.010825692676007748, -0.007755887694656849, 0.010980750434100628, 0.029478777199983597, 0.008940855972468853, 0.031533826142549515, 0.012346794828772545, 0.015844197943806648, 0.011492752470076084, -0.018452955409884453, 0.0502014234662056, -0.020669635385274887, 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706 | 706 | ['Seungkyun Hong', 'Seongchan Kim', 'Minsu Joh', 'Sa-kwang Song'] | 1708.03417v1 | Advances in remote sensing technologies have made it possible to use
high-resolution visual data for weather observation and forecasting tasks. We
propose the use of multi-layer neural networks for understanding complex
atmospheric dynamics based on multichannel satellite images. The capability of
our model was evaluated by using a linear regression task for single typhoon
coordinates prediction. A specific combination of models and different
activation policies enabled us to obtain an interesting prediction result in
the northeastern hemisphere (ENH). | GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from
Remote Sensing Imagery | 2,017 | http://arxiv.org/pdf/1708.03417v1 | Title GlobeNet Convolutional Neural Networks Typhoon Eye Tracking Remote Sensing Imagery Summary Advances remote sensing technology made possible use highresolution visual data weather observation forecasting task propose use multilayer neural network understanding complex atmospheric dynamic based multichannel satellite image capability model evaluated using linear regression task single typhoon coordinate prediction specific combination model different activation policy enabled u obtain interesting prediction result northeastern hemisphere ENH 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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707 | 707 | ['Dat Thanh Tran', 'Alexandros Iosifidis', 'Moncef Gabbouj'] | 1709.09902v3 | The excellent performance of deep neural networks has enabled us to solve
several automatization problems, opening an era of autonomous devices. However,
current deep net architectures are heavy with millions of parameters and
require billions of floating point operations. Several works have been
developed to compress a pre-trained deep network to reduce memory footprint
and, possibly, computation. Instead of compressing a pre-trained network, in
this work, we propose a generic neural network layer structure employing
multilinear projection as the primary feature extractor. The proposed
architecture requires several times less memory as compared to the traditional
Convolutional Neural Networks (CNN), while inherits the similar design
principles of a CNN. In addition, the proposed architecture is equipped with
two computation schemes that enable computation reduction or scalability.
Experimental results show the effectiveness of our compact projection that
outperforms traditional CNN, while requiring far fewer parameters. | Improving Efficiency in Convolutional Neural Network with Multilinear
Filters | 2,017 | http://arxiv.org/pdf/1709.09902v3 | Title Improving Efficiency Convolutional Neural Network Multilinear Filters Summary excellent performance deep neural network enabled u solve several automatization problem opening era autonomous device However current deep net architecture heavy million parameter require billion floating point operation Several work developed compress pretrained deep network reduce memory footprint possibly computation Instead compressing pretrained network work propose generic neural network layer structure employing multilinear projection primary feature extractor proposed architecture requires several time le memory compared traditional Convolutional Neural Networks CNN inherits similar design principle CNN addition proposed architecture equipped two computation scheme enable computation reduction scalability Experimental result show effectiveness compact projection outperforms traditional CNN requiring 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 | [-0.0230800062417984, 0.06493087112903595, -0.007753004319965839, 0.052645500749349594, 0.00351553107611835, -0.03316081687808037, 0.06656789034605026, -0.014636198990046978, -0.04129568859934807, -0.00214259116910398, -0.017294492572546005, 0.03075144626200199, 4.964257823303342e-05, 0.03679436445236206, 0.03370646387338638, -0.0507630780339241, 0.0005417118081822991, 0.0487605519592762, 0.020495332777500153, -0.01618516817688942, -0.0028052227571606636, -0.002611402655020356, 0.005132843740284443, 0.03659030795097351, -0.05476070195436478, 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708 | 708 | ['Devinder Kumar', 'Graham W. Taylor', 'Alexander Wong'] | 1710.10675v1 | Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown
considerable promise in recent years as a potential tool for improving clinical
decision support in medical oncology, particularly those based around the
concept of Discovery Radiomics, where radiomic sequencers are discovered
through the analysis of medical imaging data. One of the main limitations with
current CAD approaches is that it is very difficult to gain insight or
rationale as to how decisions are made, thus limiting their utility to
clinicians. Methods: In this study, we propose CLEAR-DR, a novel interpretable
CAD system based on the notion of CLass-Enhanced Attentive Response Discovery
Radiomics for the purpose of clinical decision support for diabetic
retinopathy. Results: In addition to disease grading via the discovered deep
radiomic sequencer, the CLEAR-DR system also produces a visual interpretation
of the decision-making process to provide better insight and understanding into
the decision-making process of the system. Conclusion: We demonstrate the
effectiveness and utility of the proposed CLEAR-DR system of enhancing the
interpretability of diagnostic grading results for the application of diabetic
retinopathy grading. Significance: CLEAR-DR can act as a potential powerful
tool to address the uninterpretability issue of current CAD systems, thus
improving their utility to clinicians. | Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided
Diagnosis of Diabetic Retinopathy | 2,017 | http://arxiv.org/pdf/1710.10675v1 | Title Discovery Radiomics CLEARDR Interpretable Computer Aided Diagnosis Diabetic Retinopathy Summary Objective Radiomicsdriven Computer Aided Diagnosis CAD shown considerable promise recent year potential tool improving clinical decision support medical oncology particularly based around concept Discovery Radiomics radiomic sequencer discovered analysis medical imaging data One main limitation current CAD approach difficult gain insight rationale decision made thus limiting utility clinician Methods study propose CLEARDR novel interpretable CAD system based notion CLassEnhanced Attentive Response Discovery Radiomics purpose clinical decision support diabetic retinopathy Results addition disease grading via discovered deep radiomic sequencer CLEARDR system also produce visual interpretation decisionmaking process provide better insight understanding decisionmaking process system Conclusion demonstrate effectiveness utility proposed CLEARDR system enhancing interpretability diagnostic grading result application diabetic retinopathy grading Significance CLEARDR act potential powerful tool address uninterpretability issue current CAD system thus improving utility clinician 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 | [0.004513891413807869, -0.01456287782639265, -0.0058048442006111145, -0.010569184087216854, 0.026145469397306442, 0.04538270831108093, 0.014838271774351597, 0.07058724015951157, -0.007606544066220522, 0.05095557123422623, 0.0695001408457756, 0.0015230110147967935, 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709 | 709 | ['Emad Barsoum', 'John Kender', 'Zicheng Liu'] | 1711.09561v1 | Predicting and understanding human motion dynamics has many applications,
such as motion synthesis, augmented reality, security, and autonomous vehicles.
Due to the recent success of generative adversarial networks (GAN), there has
been much interest in probabilistic estimation and synthetic data generation
using deep neural network architectures and learning algorithms.
We propose a novel sequence-to-sequence model for probabilistic human motion
prediction, trained with a modified version of improved Wasserstein generative
adversarial networks (WGAN-GP), in which we use a custom loss function designed
for human motion prediction. Our model, which we call HP-GAN, learns a
probability density function of future human poses conditioned on previous
poses. It predicts multiple sequences of possible future human poses, each from
the same input sequence but a different vector z drawn from a random
distribution. Furthermore, to quantify the quality of the non-deterministic
predictions, we simultaneously train a motion-quality-assessment model that
learns the probability that a given skeleton sequence is a real human motion.
We test our algorithm on two of the largest skeleton datasets: NTURGB-D and
Human3.6M. We train our model on both single and multiple action types. Its
predictive power for long-term motion estimation is demonstrated by generating
multiple plausible futures of more than 30 frames from just 10 frames of input.
We show that most sequences generated from the same input have more than 50\%
probabilities of being judged as a real human sequence. We will release all the
code used in this paper to Github. | HP-GAN: Probabilistic 3D human motion prediction via GAN | 2,017 | http://arxiv.org/pdf/1711.09561v1 | Title HPGAN Probabilistic 3D human motion prediction via GAN Summary Predicting understanding human motion dynamic many application motion synthesis augmented reality security autonomous vehicle Due recent success generative adversarial network GAN much interest probabilistic estimation synthetic data generation using deep neural network architecture learning algorithm propose novel sequencetosequence model probabilistic human motion prediction trained modified version improved Wasserstein generative adversarial network WGANGP use custom loss function designed human motion prediction model call HPGAN learns probability density function future human pose conditioned previous pose predicts multiple sequence possible future human pose input sequence different vector z drawn random distribution Furthermore quantify quality nondeterministic prediction simultaneously train motionqualityassessment model learns probability given skeleton sequence real human motion test algorithm two largest skeleton datasets NTURGBD Human36M train model single multiple action type predictive power longterm motion estimation demonstrated generating multiple plausible future 30 frame 10 frame input show sequence generated input 50 probability judged real human sequence release code used paper Github 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 | [-0.0058366525918245316, 0.06477829813957214, -0.04030698537826538, 0.006125688087195158, 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710 | 710 | ['Qiangeng Xu', 'John Kender'] | 1712.09709v2 | In the research of the impact of gestures using by a lecturer, one
challenging task is to infer the attention of a group of audiences. Two
important measurements that can help infer the level of attention are eye
movement data and Electroencephalography (EEG) data. Under the fundamental
assumption that a group of people would look at the same place if they all pay
attention at the same time, we apply a method, "Time Warp Edit Distance", to
calculate the similarity of their eye movement trajectories. Moreover, we also
cluster eye movement pattern of audiences based on these pair-wised similarity
metrics. Besides, since we don't have a direct metric for the "attention"
ground truth, a visual assessment would be beneficial to evaluate the
gesture-attention relationship. Thus we also implement a visualization tool. | Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro
Gesture | 2,017 | http://arxiv.org/pdf/1712.09709v2 | Title Report Dynamic Eye Movement Matching Visualization Tool Neuro Gesture Summary research impact gesture using lecturer one challenging task infer attention group audience Two important measurement help infer level attention eye movement data Electroencephalography EEG data fundamental assumption group people would look place pay attention time apply method Time Warp Edit Distance calculate similarity eye movement trajectory Moreover also cluster eye movement pattern audience based pairwised similarity metric Besides since dont direct metric attention ground truth visual assessment would beneficial evaluate gestureattention relationship Thus also implement visualization tool 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 | [0.02106880210340023, 0.0004780478775501251, -0.03516867756843567, 0.017498597502708435, 0.004785249475389719, 0.04110534489154816, 0.011310875415802002, 0.06284011155366898, -0.003938641864806414, -0.024829421192407608, -0.026670614257454872, 0.013832620345056057, 0.061912354081869125, 0.044799890369176865, 0.06801147013902664, -0.07838044315576553, -0.0028164396062493324, 0.022157780826091766, -0.04293399676680565, 0.05163981392979622, 0.06351964920759201, -0.002021956257522106, -0.03803658112883568, 0.05202917382121086, -0.06354492902755737, 0.01028348132967949, -0.0372500978410244, -0.0500325970351696, 0.07643547654151917, -0.04786182567477226, -0.01075273659080267, 0.027428481727838516, 0.008860955946147442, -0.007771178148686886, 2.1921111965639284e-06, -0.011781997047364712, 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711 | 711 | ['Audrey G. Chung', 'Paul Fieguth', 'Alexander Wong'] | 1802.03318v1 | Evolutionary deep intelligence synthesizes highly efficient deep neural
networks architectures over successive generations. Inspired by the nature
versus nurture debate, we propose a study to examine the role of external
factors on the network synthesis process by varying the availability of
simulated environmental resources. Experimental results were obtained for
networks synthesized via asexual evolutionary synthesis (1-parent) and sexual
evolutionary synthesis (2-parent, 3-parent, and 5-parent) using a 10% subset of
the MNIST dataset. Results show that a lower environmental factor model
resulted in a more gradual loss in performance accuracy and decrease in storage
size. This potentially allows significantly reduced storage size with minimal
to no drop in performance accuracy, and the best networks were synthesized
using the lowest environmental factor models. | Nature vs. Nurture: The Role of Environmental Resources in Evolutionary
Deep Intelligence | 2,018 | http://arxiv.org/pdf/1802.03318v1 | Title Nature v Nurture Role Environmental Resources Evolutionary Deep Intelligence Summary Evolutionary deep intelligence synthesizes highly efficient deep neural network architecture successive generation Inspired nature versus nurture debate propose study examine role external factor network synthesis process varying availability simulated environmental resource Experimental result obtained network synthesized via asexual evolutionary synthesis 1parent sexual evolutionary synthesis 2parent 3parent 5parent using 10 subset MNIST dataset Results show lower environmental factor model resulted gradual loss performance accuracy decrease storage size potentially allows significantly reduced storage size minimal drop performance accuracy best network synthesized using lowest environmental factor 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 | [-0.024060428142547607, 0.05777814984321594, -0.057195376604795456, 0.0167063120752573, -0.006461993791162968, -0.015112362802028656, 0.032274022698402405, -0.02153223194181919, -0.040376462042331696, 0.020567379891872406, -0.03807233273983002, 0.006133691407740116, -0.021327001973986626, 0.050032876431941986, 0.03689418360590935, 0.027402235195040703, 0.019516972824931145, -0.006966089829802513, -0.0324363149702549, -0.03469625860452652, -0.04292956739664078, -0.037843719124794006, 0.07128653675317764, 0.00821565929800272, -0.002542270580306649, 0.035971496254205704, -0.011739930137991905, -0.0001924148527905345, 0.0004931081784889102, -0.007874433882534504, 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712 | 712 | ['Akisato kimura', 'Derek Pang', 'Tatsuto Takeuchi', 'Kouji Miyazato', 'Junji Yamato', 'Kunio Kashino'] | 1004.0085v1 | Recent studies in the field of human vision science suggest that the human
responses to the stimuli on a visual display are non-deterministic. People may
attend to different locations on the same visual input at the same time. Based
on this knowledge, we propose a new stochastic model of visual attention by
introducing a dynamic Bayesian network to predict the likelihood of where
humans typically focus on a video scene. The proposed model is composed of a
dynamic Bayesian network with 4 layers. Our model provides a framework that
simulates and combines the visual saliency response and the cognitive state of
a person to estimate the most probable attended regions. Sample-based inference
with Markov chain Monte-Carlo based particle filter and stream processing with
multi-core processors enable us to estimate human visual attention in near real
time. Experimental results have demonstrated that our model performs
significantly better in predicting human visual attention compared to the
previous deterministic models. | A stochastic model of human visual attention with a dynamic Bayesian
network | 2,010 | http://arxiv.org/pdf/1004.0085v1 | Title stochastic model human visual attention dynamic Bayesian network Summary Recent study field human vision science suggest human response stimulus visual display nondeterministic People may attend different location visual input time Based knowledge propose new stochastic model visual attention introducing dynamic Bayesian network predict likelihood human typically focus video scene proposed model composed dynamic Bayesian network 4 layer model provides framework simulates combine visual saliency response cognitive state person estimate probable attended region Samplebased inference Markov chain MonteCarlo based particle filter stream processing multicore processor enable u estimate human visual attention near real time Experimental result demonstrated model performs significantly better predicting human visual attention compared previous deterministic 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 | [-0.010042090900242329, -0.02305353246629238, -0.019794777035713196, -0.0253529641777277, 0.010284334421157837, -0.005860269069671631, 0.004688510671257973, 0.0102235684171319, -0.007809461560100317, -0.035747282207012177, 0.025163181126117706, -0.04932325333356857, 0.047812964767217636, 0.06632932275533676, 0.05536041781306267, -0.1095789298415184, -0.020938677713274956, 0.017098387703299522, 0.010987192392349243, 0.04689574986696243, 0.025119002908468246, -0.05415073409676552, 0.024706633761525154, 0.023099808022379875, -0.01821586675941944, -0.00840307492762804, 0.0027152474503964186, 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713 | 713 | ['Tee Connie', 'Mundher Al-Shabi', 'Michael Goh'] | 1612.09506v2 | With rapid development of the Internet, web contents become huge. Most of the
websites are publicly available, and anyone can access the contents from
anywhere such as workplace, home and even schools. Nevertheless, not all the
web contents are appropriate for all users, especially children. An example of
these contents is pornography images which should be restricted to certain age
group. Besides, these images are not safe for work (NSFW) in which employees
should not be seen accessing such contents during work. Recently, convolutional
neural networks have been successfully applied to many computer vision
problems. Inspired by these successes, we propose a mixture of convolutional
neural networks for adult content recognition. Unlike other works, our method
is formulated on a weighted sum of multiple deep neural network models. The
weights of each CNN models are expressed as a linear regression problem learned
using Ordinary Least Squares (OLS). Experimental results demonstrate that the
proposed model outperforms both single CNN model and the average sum of CNN
models in adult content recognition. | Smart Content Recognition from Images Using a Mixture of Convolutional
Neural Networks | 2,016 | http://arxiv.org/pdf/1612.09506v2 | Title Smart Content Recognition Images Using Mixture Convolutional Neural Networks Summary rapid development Internet web content become huge website publicly available anyone access content anywhere workplace home even school Nevertheless web content appropriate user especially child example content pornography image restricted certain age group Besides image safe work NSFW employee seen accessing content work Recently convolutional neural network successfully applied many computer vision problem Inspired success propose mixture convolutional neural network adult content recognition Unlike work method formulated weighted sum multiple deep neural network model weight CNN model expressed linear regression problem learned using Ordinary Least Squares OLS Experimental result demonstrate proposed model outperforms single CNN model average sum CNN model adult content recognition 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 | [0.053268421441316605, 0.04292959347367287, -0.025858022272586823, 0.04832489788532257, -0.04007880762219429, 0.05105172470211983, 0.07456593215465546, 0.04035274311900139, 0.017997123301029205, -0.048988133668899536, -0.028000997379422188, -0.020716283470392227, -0.01718912459909916, 0.04956079646945, -0.021976672112941742, -0.0154309943318367, -0.0026718301232904196, 0.05041618272662163, 0.007397789042443037, 0.014268884435296059, 0.016385897994041443, -0.012800019234418869, 0.03138979524374008, -0.020075015723705292, -0.02421272173523903, -0.0016931365244090557, 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714 | 714 | ['Giacomo Cocci', 'Davide Barbieri', 'Giovanna Citti', 'Alessandro Sarti'] | 1407.0733v2 | The visual systems of many mammals, including humans, is able to integrate
the geometric information of visual stimuli and to perform cognitive tasks
already at the first stages of the cortical processing. This is thought to be
the result of a combination of mechanisms, which include feature extraction at
single cell level and geometric processing by means of cells connectivity. We
present a geometric model of such connectivities in the space of detected
features associated to spatio-temporal visual stimuli, and show how they can be
used to obtain low-level object segmentation. The main idea is that of defining
a spectral clustering procedure with anisotropic affinities over datasets
consisting of embeddings of the visual stimuli into higher dimensional spaces.
Neural plausibility of the proposed arguments will be discussed. | Cortical spatio-temporal dimensionality reduction for visual grouping | 2,014 | http://arxiv.org/pdf/1407.0733v2 | Title Cortical spatiotemporal dimensionality reduction visual grouping Summary visual system many mammal including human able integrate geometric information visual stimulus perform cognitive task already first stage cortical processing thought result combination mechanism include feature extraction single cell level geometric processing mean cell connectivity present geometric model connectivity space detected feature associated spatiotemporal visual stimulus show used obtain lowlevel object segmentation main idea defining spectral clustering procedure anisotropic affinity datasets consisting embeddings visual stimulus higher dimensional space Neural plausibility proposed argument discussed 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 | [-0.029008204117417336, -0.04715174436569214, -0.0460982471704483, 0.024282919242978096, -0.018221618607640266, 0.03414541482925415, 0.05285128951072693, -0.008611221797764301, 0.0069997296668589115, 0.018568407744169235, -0.02713211253285408, -0.014310279861092567, 0.057770781219005585, 0.057154033333063126, 0.06586456298828125, -0.09002749621868134, -0.02063855342566967, 0.028334977105259895, 0.005688515491783619, -0.026609329506754875, -0.007712492719292641, -0.04850749298930168, -0.024329302832484245, 0.007179582491517067, -0.07108873873949051, -0.0466231070458889, -0.010886075906455517, -0.05758354440331459, -0.003558208467438817, 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715 | 715 | ['Can Xu', 'Suleyman Cetintas', 'Kuang-Chih Lee', 'Li-Jia Li'] | 1411.5731v1 | Images have become one of the most popular types of media through which users
convey their emotions within online social networks. Although vast amount of
research is devoted to sentiment analysis of textual data, there has been very
limited work that focuses on analyzing sentiment of image data. In this work,
we propose a novel visual sentiment prediction framework that performs image
understanding with Deep Convolutional Neural Networks (CNN). Specifically, the
proposed sentiment prediction framework performs transfer learning from a CNN
with millions of parameters, which is pre-trained on large-scale data for
object recognition. Experiments conducted on two real-world datasets from
Twitter and Tumblr demonstrate the effectiveness of the proposed visual
sentiment analysis framework. | Visual Sentiment Prediction with Deep Convolutional Neural Networks | 2,014 | http://arxiv.org/pdf/1411.5731v1 | Title Visual Sentiment Prediction Deep Convolutional Neural Networks Summary Images become one popular type medium user convey emotion within online social network Although vast amount research devoted sentiment analysis textual data limited work focus analyzing sentiment image data work propose novel visual sentiment prediction framework performs image understanding Deep Convolutional Neural Networks CNN Specifically proposed sentiment prediction framework performs transfer learning CNN million parameter pretrained largescale data object recognition Experiments conducted two realworld datasets Twitter Tumblr demonstrate effectiveness proposed visual sentiment analysis framework 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 | [0.026351680979132652, 0.06894616782665253, -0.00021954286785330623, 0.04812313988804817, -0.02541949413716793, 0.028199469670653343, -0.009609222412109375, 0.01943696103990078, 0.014297880232334137, -0.02676563896238804, -0.011985491029918194, 0.015088360756635666, -0.03671792522072792, 0.05788110941648483, 0.012684499844908714, -0.0839122086763382, -0.00745777552947402, 0.057911813259124756, 0.024364028126001358, -0.004467441700398922, 0.04559265822172165, 0.0024996132124215364, 0.03626127168536186, 0.02811102569103241, -0.03643253073096275, -0.027234312146902084, 0.016321124508976936, -0.005510399118065834, -0.018631985411047935, -0.02796286717057228, 0.03019093908369541, 0.016563529148697853, 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716 | 716 | ['Fei Zhu', 'Abderrahim Halimi', 'Paul Honeine', 'Badong Chen', 'Nanning Zheng'] | 1602.01729v1 | In hyperspectral images, some spectral bands suffer from low signal-to-noise
ratio due to noisy acquisition and atmospheric effects, thus requiring robust
techniques for the unmixing problem. This paper presents a robust supervised
spectral unmixing approach for hyperspectral images. The robustness is achieved
by writing the unmixing problem as the maximization of the correntropy
criterion subject to the most commonly used constraints. Two unmixing problems
are derived: the first problem considers the fully-constrained unmixing, with
both the non-negativity and sum-to-one constraints, while the second one deals
with the non-negativity and the sparsity-promoting of the abundances. The
corresponding optimization problems are solved efficiently using an alternating
direction method of multipliers (ADMM) approach. Experiments on synthetic and
real hyperspectral images validate the performance of the proposed algorithms
for different scenarios, demonstrating that the correntropy-based unmixing is
robust to outlier bands. | Correntropy Maximization via ADMM - Application to Robust Hyperspectral
Unmixing | 2,016 | http://arxiv.org/pdf/1602.01729v1 | Title Correntropy Maximization via ADMM Application Robust Hyperspectral Unmixing Summary hyperspectral image spectral band suffer low signaltonoise ratio due noisy acquisition atmospheric effect thus requiring robust technique unmixing problem paper present robust supervised spectral unmixing approach hyperspectral image robustness achieved writing unmixing problem maximization correntropy criterion subject commonly used constraint Two unmixing problem derived first problem considers fullyconstrained unmixing nonnegativity sumtoone constraint second one deal nonnegativity sparsitypromoting abundance corresponding optimization problem solved efficiently using alternating direction method multiplier ADMM approach Experiments synthetic real hyperspectral image validate performance proposed algorithm different scenario demonstrating correntropybased unmixing robust outlier band 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 | [-0.01128374319523573, 0.008524861186742783, 0.006219339091330767, 0.035685356706380844, 0.011910916306078434, 0.012478083372116089, -0.0077212220057845116, -0.007883419282734394, -0.004385383799672127, 0.031793300062417984, -0.019009126350283623, 0.05858924612402916, 0.016866693273186684, 0.026558484882116318, -0.0159581508487463, 0.004704786464571953, 0.0019977542106062174, -0.07898968458175659, 0.05059600621461868, -0.015597781166434288, 0.0009211238939315081, -0.0954437404870987, 0.016205335035920143, -0.04951591417193413, -0.05772610381245613, -0.04139389470219612, 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717 | 717 | ['Farhad Arbabzadah', 'Grégoire Montavon', 'Klaus-Robert Müller', 'Wojciech Samek'] | 1606.07285v2 | This paper focuses on the problem of explaining predictions of psychological
attributes such as attractiveness, happiness, confidence and intelligence from
face photographs using deep neural networks. Since psychological attribute
datasets typically suffer from small sample sizes, we apply transfer learning
with two base models to avoid overfitting. These models were trained on an age
and gender prediction task, respectively. Using a novel explanation method we
extract heatmaps that highlight the parts of the image most responsible for the
prediction. We further observe that the explanation method provides important
insights into the nature of features of the base model, which allow one to
assess the aptitude of the base model for a given transfer learning task.
Finally, we observe that the multiclass model is more feature rich than its
binary counterpart. The experimental evaluation is performed on the 2222 images
from the 10k US faces dataset containing psychological attribute labels as well
as on a subset of KDEF images. | Identifying individual facial expressions by deconstructing a neural
network | 2,016 | http://arxiv.org/pdf/1606.07285v2 | Title Identifying individual facial expression deconstructing neural network Summary paper focus problem explaining prediction psychological attribute attractiveness happiness confidence intelligence face photograph using deep neural network Since psychological attribute datasets typically suffer small sample size apply transfer learning two base model avoid overfitting model trained age gender prediction task respectively Using novel explanation method extract heatmaps highlight part image responsible prediction observe explanation method provides important insight nature feature base model allow one ass aptitude base model given transfer learning task Finally observe multiclass model feature rich binary counterpart experimental evaluation performed 2222 image 10k US face dataset containing psychological attribute label well subset KDEF image 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 | [0.01171519048511982, 0.10384302586317062, -0.03140038624405861, 0.006184595637023449, -0.0005059328977949917, 0.08105877041816711, 0.04882337898015976, -0.01604016311466694, 0.012622016482055187, 0.036673445254564285, -0.04585159569978714, -0.0045763589441776276, 0.02939782291650772, 0.027108021080493927, 0.03133971244096756, -0.08195586502552032, -0.030276969075202942, 0.06316181272268295, 0.020502766594290733, -0.00992381852120161, 0.03597433865070343, 0.025958741083741188, 0.04268833249807358, 0.015758091583848, -0.04367099702358246, -0.016907522454857826, 0.0034212851896882057, 0.0021605598740279675, 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718 | 718 | ['Jing Yu Koh', 'Wojciech Samek', 'Klaus-Robert Müller', 'Alexander Binder'] | 1606.09187v3 | Semantic boundary and edge detection aims at simultaneously detecting object
edge pixels in images and assigning class labels to them. Systematic training
of predictors for this task requires the labeling of edges in images which is a
particularly tedious task. We propose a novel strategy for solving this task,
when pixel-level annotations are not available, performing it in an almost
zero-shot manner by relying on conventional whole image neural net classifiers
that were trained using large bounding boxes. Our method performs the following
two steps at test time. Firstly it predicts the class labels by applying the
trained whole image network to the test images. Secondly, it computes
pixel-wise scores from the obtained predictions by applying backprop gradients
as well as recent visualization algorithms such as deconvolution and layer-wise
relevance propagation. We show that high pixel-wise scores are indicative for
the location of semantic boundaries, which suggests that the semantic boundary
problem can be approached without using edge labels during the training phase. | Object Boundary Detection and Classification with Image-level Labels | 2,016 | http://arxiv.org/pdf/1606.09187v3 | Title Object Boundary Detection Classification Imagelevel Labels Summary Semantic boundary edge detection aim simultaneously detecting object edge pixel image assigning class label Systematic training predictor task requires labeling edge image particularly tedious task propose novel strategy solving task pixellevel annotation available performing almost zeroshot manner relying conventional whole image neural net classifier trained using large bounding box method performs following two step test time Firstly predicts class label applying trained whole image network test image Secondly computes pixelwise score obtained prediction applying backprop gradient well recent visualization algorithm deconvolution layerwise relevance propagation show high pixelwise score indicative location semantic boundary suggests semantic boundary problem approached without using edge label training phase 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 | [0.014435085467994213, -0.009498577564954758, 0.016130737960338593, 0.0587863065302372, -0.027480414137244225, -0.04759363457560539, 0.057943981140851974, -0.020749565213918686, 0.0026499656960368156, -0.03737781569361687, -0.010966208763420582, 0.05082504823803902, -0.005800250452011824, 0.05133979022502899, -0.026573054492473602, -0.06298895925283432, -0.056692466139793396, 0.03845095634460449, -0.019076231867074966, -0.023270992562174797, 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719 | 719 | ['Sam Kriegman', 'Marcin Szubert', 'Josh C. Bongard', 'Christian Skalka'] | 1706.07888v2 | Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine learning process, yielding regional model features
whose construction is driven by model prediction performance rather than prior
assumptions. Our results demonstrate that Genetic Programming is particularly
well suited to this type of feature construction because it can automatically
synthesize appropriate aggregations, as well as better incorporate them into
predictive models compared to other regression methods we tested. In our
experiments we consider a specific problem instance and real-world dataset
relevant to predicting snow properties in high-mountain Asia. | Evolving Spatially Aggregated Features from Satellite Imagery for
Regional Modeling | 2,017 | http://arxiv.org/pdf/1706.07888v2 | Title Evolving Spatially Aggregated Features Satellite Imagery Regional Modeling Summary Satellite imagery remote sensing provide explanatory variable relatively high resolution modeling geospatial phenomenon yet regional summary often desirable analysis actionable insight paper propose novel method inducing spatial aggregation component machine learning process yielding regional model feature whose construction driven model prediction performance rather prior assumption result demonstrate Genetic Programming particularly well suited type feature construction automatically synthesize appropriate aggregation well better incorporate predictive model compared regression method tested experiment consider specific problem instance realworld dataset relevant predicting snow property highmountain Asia 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 | [-0.015096725896000862, 0.0686153993010521, -0.03705476596951485, -0.02326796017587185, -0.019166309386491776, 0.039359673857688904, 0.018243933096528053, -0.07028821110725403, 0.026447806507349014, 0.025008250027894974, 0.07410223037004471, 0.004483410157263279, 0.04304669424891472, 0.07307964563369751, 0.04744701087474823, -0.10104692727327347, 0.02606133185327053, -0.009378133341670036, -0.0220106802880764, -0.0006279670633375645, 0.017338678240776062, -0.031400494277477264, 0.038125406950712204, 0.01966690830886364, -0.02411463111639023, 0.0503368005156517, 0.006659097969532013, -0.025347592309117317, 0.00011457991058705375, -0.0090080127120018, 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720 | 720 | ['Biswa Sengupta', 'Yu Qian'] | 1708.06250v1 | In recent work, it was shown that combining multi-kernel based support vector
machines (SVMs) can lead to near state-of-the-art performance on an action
recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks
that used hand-crafted features in addition to the deep convolutional feature
extractors. In the present work, we show that combining distributed Gaussian
Processes with multi-stream deep convolutional neural networks (CNN) alleviate
the need to augment a neural network with hand-crafted features. In contrast to
prior work, we treat each deep neural convolutional network as an expert
wherein the individual predictions (and their respective uncertainties) are
combined into a Product of Experts (PoE) framework. | Pillar Networks++: Distributed non-parametric deep and wide networks | 2,017 | http://arxiv.org/pdf/1708.06250v1 | Title Pillar Networks Distributed nonparametric deep wide network Summary recent work shown combining multikernel based support vector machine SVMs lead near stateoftheart performance action recognition dataset HMDB51 dataset 04 lower framework used handcrafted feature addition deep convolutional feature extractor present work show combining distributed Gaussian Processes multistream deep convolutional neural network CNN alleviate need augment neural network handcrafted feature contrast prior work treat deep neural convolutional network expert wherein individual prediction respective uncertainty combined Product Experts PoE framework 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 | [-0.02186121791601181, 0.019998127594590187, -0.011713789775967598, 0.04303362965583801, 0.006795182358473539, 0.023230986669659615, 0.08459728956222534, -0.02022259682416916, -0.06779662519693375, 0.017613446339964867, -0.04614480212330818, -0.015191325917840004, 0.03995179757475853, 0.07688998430967331, 0.011778458952903748, -0.038212064653635025, -0.017255477607250214, 0.05198528617620468, -0.022230256348848343, -0.03376911208033562, 0.01541858073323965, -0.01056311558932066, -0.004292713478207588, 0.047119610011577606, -0.04498976469039917, 0.001683219219557941, -0.01923646591603756, -0.014984606765210629, 0.03565491363406181, -0.02872273698449135, 0.032757483422756195, -0.01333802193403244, 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721 | 721 | ['Ivo Kwee', 'Marcus Hutter', 'Juergen Schmidhuber'] | cs/0105025v1 | Unlike traditional reinforcement learning (RL), market-based RL is in
principle applicable to worlds described by partially observable Markov
Decision Processes (POMDPs), where an agent needs to learn short-term memories
of relevant previous events in order to execute optimal actions. Most previous
work, however, has focused on reactive settings (MDPs) instead of POMDPs. Here
we reimplement a recent approach to market-based RL and for the first time
evaluate it in a toy POMDP setting. | Market-Based Reinforcement Learning in Partially Observable Worlds | 2,001 | http://arxiv.org/pdf/cs/0105025v1 | Title MarketBased Reinforcement Learning Partially Observable Worlds Summary Unlike traditional reinforcement learning RL marketbased RL principle applicable world described partially observable Markov Decision Processes POMDPs agent need learn shortterm memory relevant previous event order execute optimal action previous work however focused reactive setting MDPs instead POMDPs reimplement recent approach marketbased RL first time evaluate toy POMDP setting 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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722 | 722 | ['Andras Lorincz'] | cs/0308025v1 | A model of sensory information processing is presented. The model assumes
that learning of internal (hidden) generative models, which can predict the
future and evaluate the precision of that prediction, is of central importance
for information extraction. Furthermore, the model makes a bridge to
goal-oriented systems and builds upon the structural similarity between the
architecture of a robust controller and that of the hippocampal entorhinal
loop. This generative control architecture is mapped to the neocortex and to
the hippocampal entorhinal loop. Implicit memory phenomena; priming and
prototype learning are emerging features of the model. Mathematical theorems
ensure stability and attractive learning properties of the architecture.
Connections to reinforcement learning are also established: both the control
network, and the network with a hidden model converge to (near) optimal policy
under suitable conditions. Falsifying predictions, including the role of the
feedback connections between neocortical areas are made. | Controlled hierarchical filtering: Model of neocortical sensory
processing | 2,003 | http://arxiv.org/pdf/cs/0308025v1 | Title Controlled hierarchical filtering Model neocortical sensory processing Summary model sensory information processing presented model assumes learning internal hidden generative model predict future evaluate precision prediction central importance information extraction Furthermore model make bridge goaloriented system build upon structural similarity architecture robust controller hippocampal entorhinal loop generative control architecture mapped neocortex hippocampal entorhinal loop Implicit memory phenomenon priming prototype learning emerging feature model Mathematical theorem ensure stability attractive learning property architecture Connections reinforcement learning also established control network network hidden model converge near optimal policy suitable condition Falsifying prediction including role feedback connection neocortical area made 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 | [0.0038238430861383677, -0.016976026818156242, -0.030160972848534584, -0.007917379960417747, 0.0030014747753739357, 0.012925976887345314, -0.014209343120455742, 0.021481959149241447, 0.046543776988983154, -0.013778923079371452, -0.05295243486762047, 0.015117663890123367, -0.01441823597997427, 0.06966102868318558, 0.04722685366868973, -0.04712994396686554, 0.0371340736746788, 0.01800030842423439, -0.0012094001285731792, -0.031026817858219147, -0.014532903209328651, -0.037219077348709106, 0.00015084291226230562, 0.04483471065759659, -0.002477194182574749, -0.021347766742110252, 0.010920998640358448, 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723 | 723 | ['David J. Finton'] | cs/0404032v1 | For an intelligent agent to be truly autonomous, it must be able to adapt its
representation to the requirements of its task as it interacts with the world.
Most current approaches to on-line feature extraction are ad hoc; in contrast,
this paper presents an algorithm that bases judgments of state compatibility
and state-space abstraction on principled criteria derived from the
psychological principle of cognitive economy. The algorithm incorporates an
active form of Q-learning, and partitions continuous state-spaces by merging
and splitting Voronoi regions. The experiments illustrate a new methodology for
testing and comparing representations by means of learning curves. Results from
the puck-on-a-hill task demonstrate the algorithm's ability to learn effective
representations, superior to those produced by some other, well-known, methods. | When Do Differences Matter? On-Line Feature Extraction Through Cognitive
Economy | 2,004 | http://arxiv.org/pdf/cs/0404032v1 | Title Differences Matter OnLine Feature Extraction Cognitive Economy Summary intelligent agent truly autonomous must able adapt representation requirement task interacts world current approach online feature extraction ad hoc contrast paper present algorithm base judgment state compatibility statespace abstraction principled criterion derived psychological principle cognitive economy algorithm incorporates active form Qlearning partition continuous statespaces merging splitting Voronoi region experiment illustrate new methodology testing comparing representation mean learning curve Results puckonahill task demonstrate algorithm ability learn effective representation superior produced wellknown 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 | [-0.0025872213300317526, 0.04461510851979256, -0.04202467203140259, -0.0055188341066241264, -0.031070062890648842, 0.015614354982972145, 0.06376229971647263, 0.0013927760301157832, 0.05910900607705116, -0.03941641002893448, 0.0278855599462986, 0.018414955586194992, 0.0176840890198946, 0.10981688648462296, 0.005450707394629717, -0.0802326649427414, -0.0020518028177320957, -0.0022737004328519106, 0.04053178429603577, -0.003457113169133663, -0.011812536045908928, -0.04301096126437187, -0.029860632494091988, 0.0177592970430851, -0.004150155931711197, -0.03758179768919945, -0.03097536414861679, -0.06773409247398376, 0.006988238543272018, -0.019474489614367485, -0.02288684993982315, 0.0009572894196026027, 0.005754575598984957, 0.010769342072308064, 2.543588152548182e-06, 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724 | 724 | ['I. Szita', 'A. Lorincz'] | cs/0410004v1 | Recurrent neural networks are often used for learning time-series data. Based
on a few assumptions we model this learning task as a minimization problem of a
nonlinear least-squares cost function. The special structure of the cost
function allows us to build a connection to reinforcement learning. We exploit
this connection and derive a convergent, policy iteration-based algorithm.
Furthermore, we argue that RNN training can be fit naturally into the
reinforcement learning framework. | Applying Policy Iteration for Training Recurrent Neural Networks | 2,004 | http://arxiv.org/pdf/cs/0410004v1 | Title Applying Policy Iteration Training Recurrent Neural Networks Summary Recurrent neural network often used learning timeseries data Based assumption model learning task minimization problem nonlinear leastsquares cost function special structure cost function allows u build connection reinforcement learning exploit connection derive convergent policy iterationbased algorithm Furthermore argue RNN training fit naturally reinforcement learning framework 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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725 | 725 | ['Vitaly Schetinin', 'Joachim Schult'] | cs/0504069v1 | A new technique is presented developed to learn multi-class concepts from
clinical electroencephalograms. A desired concept is represented as a neuronal
computational model consisting of the input, hidden, and output neurons. In
this model the hidden neurons learn independently to classify the
electroencephalogram segments presented by spectral and statistical features.
This technique has been applied to the electroencephalogram data recorded from
65 sleeping healthy newborns in order to learn a brain maturation concept of
newborns aged between 35 and 51 weeks. The 39399 and 19670 segments from these
data have been used for learning and testing the concept, respectively. As a
result, the concept has correctly classified 80.1% of the testing segments or
87.7% of the 65 records. | A Neural-Network Technique to Learn Concepts from Electroencephalograms | 2,005 | http://arxiv.org/pdf/cs/0504069v1 | Title NeuralNetwork Technique Learn Concepts Electroencephalograms Summary new technique presented developed learn multiclass concept clinical electroencephalogram desired concept represented neuronal computational model consisting input hidden output neuron model hidden neuron learn independently classify electroencephalogram segment presented spectral statistical feature technique applied electroencephalogram data recorded 65 sleeping healthy newborn order learn brain maturation concept newborn aged 35 51 week 39399 19670 segment data used learning testing concept respectively result concept correctly classified 801 testing segment 877 65 record 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 | [-0.026561444625258446, 0.02696835808455944, -0.013183407485485077, -0.007017889525741339, -0.026515575125813484, 0.021268894895911217, 0.06517685949802399, 0.012261785566806793, -0.013266931287944317, -0.014424309134483337, 0.003456740640103817, -0.02011762000620365, 0.04236244410276413, 0.02565435692667961, 0.044657573103904724, -0.01962164044380188, 0.0323653370141983, 0.05587492510676384, 0.01805874891579151, -0.01163537334650755, 0.003271150402724743, -0.016354501247406006, -0.039561349898576736, 0.08115382492542267, -0.04226326569914818, 0.056302912533283234, -0.03381751477718353, -0.06044524535536766, -0.009683174081146717, -0.05179787799715996, 0.03704320266842842, 0.0026778830215334892, -0.020720528438687325, 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726 | 726 | ['Ridwan Al Iqbal'] | 1005.5556v2 | Standard hybrid learners that use domain knowledge require stronger knowledge
that is hard and expensive to acquire. However, weaker domain knowledge can
benefit from prior knowledge while being cost effective. Weak knowledge in the
form of feature relative importance (FRI) is presented and explained. Feature
relative importance is a real valued approximation of a feature's importance
provided by experts. Advantage of using this knowledge is demonstrated by IANN,
a modified multilayer neural network algorithm. IANN is a very simple
modification of standard neural network algorithm but attains significant
performance gains. Experimental results in the field of molecular biology show
higher performance over other empirical learning algorithms including standard
backpropagation and support vector machines. IANN performance is even
comparable to a theory refinement system KBANN that uses stronger domain
knowledge. This shows Feature relative importance can improve performance of
existing empirical learning algorithms significantly with minimal effort. | Empirical learning aided by weak domain knowledge in the form of feature
importance | 2,010 | http://arxiv.org/pdf/1005.5556v2 | Title Empirical learning aided weak domain knowledge form feature importance Summary Standard hybrid learner use domain knowledge require stronger knowledge hard expensive acquire However weaker domain knowledge benefit prior knowledge cost effective Weak knowledge form feature relative importance FRI presented explained Feature relative importance real valued approximation feature importance provided expert Advantage using knowledge demonstrated IANN modified multilayer neural network algorithm IANN simple modification standard neural network algorithm attains significant performance gain Experimental result field molecular biology show higher performance empirical learning algorithm including standard backpropagation support vector machine IANN performance even comparable theory refinement system KBANN us stronger domain knowledge show Feature relative importance improve performance existing empirical learning algorithm significantly minimal effort 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 | [-0.014742983505129814, -0.013229891657829285, -0.019791970029473305, -0.030785612761974335, -0.028199950233101845, -0.02570103108882904, 0.08041764795780182, 0.024752169847488403, 0.032797474414110184, 0.01318944338709116, 0.05035974830389023, -0.00034811056684702635, 0.01610279083251953, 0.08712884038686752, 0.03261253610253334, 0.01584864780306816, 0.0011928626336157322, 0.048078522086143494, 0.022607257589697838, -0.033413875848054886, -0.002196939429268241, -0.05494774505496025, 0.02308480255305767, 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727 | 727 | ['J. J. Grefenstette', 'D. E. Moriarty', 'A. C. Schultz'] | 1106.0221v1 | There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey of temporal difference methods. This article focuses on the
application of evolutionary algorithms to the reinforcement learning problem,
emphasizing alternative policy representations, credit assignment methods, and
problem-specific genetic operators. Strengths and weaknesses of the
evolutionary approach to reinforcement learning are presented, along with a
survey of representative applications. | Evolutionary Algorithms for Reinforcement Learning | 2,011 | http://arxiv.org/pdf/1106.0221v1 | Title Evolutionary Algorithms Reinforcement Learning Summary two distinct approach solving reinforcement learning problem namely searching value function space searching policy space Temporal difference method evolutionary algorithm wellknown example approach Kaelbling Littman Moore recently provided informative survey temporal difference method article focus application evolutionary algorithm reinforcement learning problem emphasizing alternative policy representation credit assignment method problemspecific genetic operator Strengths weakness evolutionary approach reinforcement learning presented along survey representative 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 | [0.03982983157038689, 0.02611774392426014, -0.03365381062030792, -0.0257871076464653, -0.042755842208862305, -0.027617596089839935, -0.026217561215162277, 0.020619787275791168, -0.0354771688580513, 0.01724240370094776, 0.04367851838469505, 0.032135769724845886, -0.010958706960082054, 0.03356756642460823, -0.03311513364315033, -0.03665165603160858, 0.028848836198449135, 0.01698894239962101, 0.02342720329761505, 0.012672296725213528, 0.008183026686310768, -0.06869328767061234, 0.02507665939629078, 0.015421965159475803, 0.018604151904582977, 0.051650162786245346, -0.041921403259038925, -0.05678547918796539, 0.07893719524145126, -0.05359470844268799, 0.06463954597711563, -0.012562737800180912, 0.006464797537773848, 0.12734432518482208, 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728 | 728 | ['Guillaume Desjardins', 'Aaron Courville', 'Yoshua Bengio'] | 1203.4416v1 | The deep Boltzmann machine (DBM) has been an important development in the
quest for powerful "deep" probabilistic models. To date, simultaneous or joint
training of all layers of the DBM has been largely unsuccessful with existing
training methods. We introduce a simple regularization scheme that encourages
the weight vectors associated with each hidden unit to have similar norms. We
demonstrate that this regularization can be easily combined with standard
stochastic maximum likelihood to yield an effective training strategy for the
simultaneous training of all layers of the deep Boltzmann machine. | On Training Deep Boltzmann Machines | 2,012 | http://arxiv.org/pdf/1203.4416v1 | Title Training Deep Boltzmann Machines Summary deep Boltzmann machine DBM important development quest powerful deep probabilistic model date simultaneous joint training layer DBM largely unsuccessful existing training method introduce simple regularization scheme encourages weight vector associated hidden unit similar norm demonstrate regularization easily combined standard stochastic maximum likelihood yield effective training strategy simultaneous training layer deep Boltzmann machine 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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729 | 729 | ['Farnood Merrikh-Bayat', 'Saeed Bagheri Shouraki'] | 1109.4609v1 | Fuzzy inference systems always suffer from the lack of efficient structures
or platforms for their hardware implementation. In this paper, we tried to
overcome this problem by proposing new method for the implementation of those
fuzzy inference systems which use fuzzy rule base to make inference. To achieve
this goal, we have designed a multi-layer neuro-fuzzy computing system based on
the memristor crossbar structure by introducing some new concepts like fuzzy
minterms. Although many applications can be realized through the use of our
proposed system, in this study we show how the fuzzy XOR function can be
constructed and how it can be used to extract edges from grayscale images. Our
memristive fuzzy edge detector (implemented in analog form) compared with other
common edge detectors has this advantage that it can extract edges of any given
image all at once in real-time. | Memristive fuzzy edge detector | 2,011 | http://arxiv.org/pdf/1109.4609v1 | Title Memristive fuzzy edge detector Summary Fuzzy inference system always suffer lack efficient structure platform hardware implementation paper tried overcome problem proposing new method implementation fuzzy inference system use fuzzy rule base make inference achieve goal designed multilayer neurofuzzy computing system based memristor crossbar structure introducing new concept like fuzzy minterms Although many application realized use proposed system study show fuzzy XOR function constructed used extract edge grayscale image memristive fuzzy edge detector implemented analog form compared common edge detector advantage extract edge given image 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 | [-0.054506924003362656, -0.008144539780914783, -0.07892411202192307, 0.0748184472322464, -0.011441108770668507, -0.013800231739878654, 0.02545432560145855, -0.0005565573810599744, 0.028147999197244644, 0.008172736503183842, 0.004141718149185181, 0.01474747620522976, -0.03236301615834236, 0.07134664803743362, 0.018242619931697845, -0.028131596744060516, 0.04181274026632309, 0.013005425222218037, 0.05680059641599655, -0.051148634403944016, 0.03833349794149399, -0.04435908794403076, -0.013631624169647694, -0.011055877432227135, -0.057520750910043716, 0.047922421246767044, -0.027063287794589996, -0.008944080211222172, 0.028745515272021294, -0.03971808776259422, -0.02080918848514557, -0.03130604699254036, -0.04412924125790596, 0.008968560956418514, 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730 | 730 | ['Sebastián Basterrech', 'Gerardo Rubino'] | 1212.6276v1 | In the last decade, a new computational paradigm was introduced in the field
of Machine Learning, under the name of Reservoir Computing (RC). RC models are
neural networks which a recurrent part (the reservoir) that does not
participate in the learning process, and the rest of the system where no
recurrence (no neural circuit) occurs. This approach has grown rapidly due to
its success in solving learning tasks and other computational applications.
Some success was also observed with another recently proposed neural network
designed using Queueing Theory, the Random Neural Network (RandNN). Both
approaches have good properties and identified drawbacks. In this paper, we
propose a new RC model called Echo State Queueing Network (ESQN), where we use
ideas coming from RandNNs for the design of the reservoir. ESQNs consist in
ESNs where the reservoir has a new dynamics inspired by recurrent RandNNs. The
paper positions ESQNs in the global Machine Learning area, and provides
examples of their use and performances. We show on largely used benchmarks that
ESQNs are very accurate tools, and we illustrate how they compare with standard
ESNs. | Echo State Queueing Network: a new reservoir computing learning tool | 2,012 | http://arxiv.org/pdf/1212.6276v1 | Title Echo State Queueing Network new reservoir computing learning tool Summary last decade new computational paradigm introduced field Machine Learning name Reservoir Computing RC RC model neural network recurrent part reservoir participate learning process rest system recurrence neural circuit occurs approach grown rapidly due success solving learning task computational application success also observed another recently proposed neural network designed using Queueing Theory Random Neural Network RandNN approach good property identified drawback paper propose new RC model called Echo State Queueing Network ESQN use idea coming RandNNs design reservoir ESQNs consist ESNs reservoir new dynamic inspired recurrent RandNNs paper position ESQNs global Machine Learning area provides example use performance show largely used benchmark ESQNs accurate tool illustrate compare standard ESNs 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 | [-0.04387815669178963, -0.0168597511947155, -0.046949636191129684, -0.007753667421638966, -0.04462617263197899, -0.01876181922852993, 0.012994497083127499, -0.04813118651509285, -0.04522783309221268, 0.0007096731569617987, 0.00988808274269104, -0.04099180921912193, 0.003933105152100325, 0.05911489576101303, 0.0066724964417517185, -0.027765601873397827, 0.04508982226252556, 0.032554741948843, -0.008101830258965492, -0.005734728183597326, 0.012036233209073544, -0.018223872408270836, 0.010828105732798576, 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731 | 731 | ['David Silver', 'Hado van Hasselt', 'Matteo Hessel', 'Tom Schaul', 'Arthur Guez', 'Tim Harley', 'Gabriel Dulac-Arnold', 'David Reichert', 'Neil Rabinowitz', 'Andre Barreto', 'Thomas Degris'] | 1612.08810v3 | One of the key challenges of artificial intelligence is to learn models that
are effective in the context of planning. In this document we introduce the
predictron architecture. The predictron consists of a fully abstract model,
represented by a Markov reward process, that can be rolled forward multiple
"imagined" planning steps. Each forward pass of the predictron accumulates
internal rewards and values over multiple planning depths. The predictron is
trained end-to-end so as to make these accumulated values accurately
approximate the true value function. We applied the predictron to procedurally
generated random mazes and a simulator for the game of pool. The predictron
yielded significantly more accurate predictions than conventional deep neural
network architectures. | The Predictron: End-To-End Learning and Planning | 2,016 | http://arxiv.org/pdf/1612.08810v3 | Title Predictron EndToEnd Learning Planning Summary One key challenge artificial intelligence learn model effective context planning document introduce predictron architecture predictron consists fully abstract model represented Markov reward process rolled forward multiple imagined planning step forward pas predictron accumulates internal reward value multiple planning depth predictron trained endtoend make accumulated value accurately approximate true value function applied predictron procedurally generated random maze simulator game pool predictron yielded significantly accurate prediction conventional deep neural network architecture 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 | [0.0056665921583771706, -0.006368445698171854, -0.021463168784976006, -0.013271157629787922, -0.01797153428196907, -0.03033430315554142, -0.02080725133419037, -0.024465681985020638, -0.019865550100803375, 0.025212226435542107, -0.001936144195497036, 0.07284921407699585, -0.028282826766371727, 0.07988160848617554, -0.005089794751256704, -0.053487975150346756, 0.016277069225907326, -0.019842784851789474, -0.05493547394871712, 0.0005379766225814819, -0.013008001260459423, -0.02923283912241459, -0.010590306483209133, 0.050023097544908524, -0.03528772294521332, -0.0222712941467762, -0.08061473816633224, 0.016439316794276237, 0.01600014418363571, -0.1474914848804474, 0.02777101844549179, -0.037437617778778076, 0.02572433277964592, 0.05444609001278877, 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732 | 732 | ['Deepak Kumar', 'A G Ramakrishnan'] | 1407.6315v1 | Particle swarm optimization is used in several combinatorial optimization
problems. In this work, particle swarms are used to solve quadratic programming
problems with quadratic constraints. The approach of particle swarms is an
example for interior point methods in optimization as an iterative technique.
This approach is novel and deals with classification problems without the use
of a traditional classifier. Our method determines the optimal hyperplane or
classification boundary for a data set. In a binary classification problem, we
constrain each class as a cluster, which is enclosed by an ellipsoid. The
estimation of the optimal hyperplane between the two clusters is posed as a
quadratically constrained quadratic problem. The optimization problem is solved
in distributed format using modified particle swarms. Our method has the
advantage of using the direction towards optimal solution rather than searching
the entire feasible region. Our results on the Iris, Pima, Wine, and Thyroid
datasets show that the proposed method works better than a neural network and
the performance is close to that of SVM. | Quadratically constrained quadratic programming for classification using
particle swarms and applications | 2,014 | http://arxiv.org/pdf/1407.6315v1 | Title Quadratically constrained quadratic programming classification using particle swarm application Summary Particle swarm optimization used several combinatorial optimization problem work particle swarm used solve quadratic programming problem quadratic constraint approach particle swarm example interior point method optimization iterative technique approach novel deal classification problem without use traditional classifier method determines optimal hyperplane classification boundary data set binary classification problem constrain class cluster enclosed ellipsoid estimation optimal hyperplane two cluster posed quadratically constrained quadratic problem optimization problem solved distributed format using modified particle swarm method advantage using direction towards optimal solution rather searching entire feasible region result Iris Pima Wine Thyroid datasets show proposed method work better neural network performance close SVM 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 | [-0.03507625684142113, 0.029305221512913704, -0.02904825657606125, -0.016544800251722336, 0.057384174317121506, 0.010084465146064758, 0.005685581360012293, 0.02721221186220646, 0.016257494688034058, -0.00974691566079855, -0.018319960683584213, 0.04222463443875313, 0.012938722968101501, 0.008114258758723736, -0.027141148224473, -0.036331649869680405, 0.010760226286947727, 0.04090992733836174, -0.00475454144179821, -0.017622489482164383, 0.01122292224317789, -0.04345235973596573, 0.007840639911592007, 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733 | 733 | ['Wojciech Zaremba', 'Ilya Sutskever'] | 1410.4615v3 | Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are
widely used because they are expressive and are easy to train. Our interest
lies in empirically evaluating the expressiveness and the learnability of LSTMs
in the sequence-to-sequence regime by training them to evaluate short computer
programs, a domain that has traditionally been seen as too complex for neural
networks. We consider a simple class of programs that can be evaluated with a
single left-to-right pass using constant memory. Our main result is that LSTMs
can learn to map the character-level representations of such programs to their
correct outputs. Notably, it was necessary to use curriculum learning, and
while conventional curriculum learning proved ineffective, we developed a new
variant of curriculum learning that improved our networks' performance in all
experimental conditions. The improved curriculum had a dramatic impact on an
addition problem, making it possible to train an LSTM to add two 9-digit
numbers with 99% accuracy. | Learning to Execute | 2,014 | http://arxiv.org/pdf/1410.4615v3 | Title Learning Execute Summary Recurrent Neural Networks RNNs Long ShortTerm Memory unit LSTM widely used expressive easy train interest lie empirically evaluating expressiveness learnability LSTMs sequencetosequence regime training evaluate short computer program domain traditionally seen complex neural network consider simple class program evaluated single lefttoright pas using constant memory main result LSTMs learn map characterlevel representation program correct output Notably necessary use curriculum learning conventional curriculum learning proved ineffective developed new variant curriculum learning improved network performance experimental condition improved curriculum dramatic impact addition problem making possible train LSTM add two 9digit number 99 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 | [0.005658090114593506, 0.008094490505754948, -0.02637835592031479, 0.01725480705499649, -0.021237267181277275, -0.009866801090538502, 0.02591564506292343, -0.02976294979453087, -0.025311168283224106, -0.038631077855825424, 0.006735583301633596, -0.03363580256700516, 0.016377123072743416, 0.032064393162727356, 0.04117317497730255, -0.07828515768051147, 0.049533020704984665, -0.00869816355407238, -0.02559031918644905, 0.031622786074876785, -0.007362258154898882, -0.011639997363090515, -0.01551967952400446, 0.05068458244204521, 0.023278575390577316, -0.014057920314371586, -0.035174716264009476, -0.031224006786942482, -0.002643769606947899, 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734 | 734 | ['Minje Kim', 'Paris Smaragdis'] | 1601.06071v1 | Based on the assumption that there exists a neural network that efficiently
represents a set of Boolean functions between all binary inputs and outputs, we
propose a process for developing and deploying neural networks whose weight
parameters, bias terms, input, and intermediate hidden layer output signals,
are all binary-valued, and require only basic bit logic for the feedforward
pass. The proposed Bitwise Neural Network (BNN) is especially suitable for
resource-constrained environments, since it replaces either floating or
fixed-point arithmetic with significantly more efficient bitwise operations.
Hence, the BNN requires for less spatial complexity, less memory bandwidth, and
less power consumption in hardware. In order to design such networks, we
propose to add a few training schemes, such as weight compression and noisy
backpropagation, which result in a bitwise network that performs almost as well
as its corresponding real-valued network. We test the proposed network on the
MNIST dataset, represented using binary features, and show that BNNs result in
competitive performance while offering dramatic computational savings. | Bitwise Neural Networks | 2,016 | http://arxiv.org/pdf/1601.06071v1 | Title Bitwise Neural Networks Summary Based assumption exists neural network efficiently represents set Boolean function binary input output propose process developing deploying neural network whose weight parameter bias term input intermediate hidden layer output signal binaryvalued require basic bit logic feedforward pas proposed Bitwise Neural Network BNN especially suitable resourceconstrained environment since replaces either floating fixedpoint arithmetic significantly efficient bitwise operation Hence BNN requires le spatial complexity le memory bandwidth le power consumption hardware order design network propose add training scheme weight compression noisy backpropagation result bitwise network performs almost well corresponding realvalued network test proposed network MNIST dataset represented using binary feature show BNNs result competitive performance offering dramatic computational saving 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 | [-0.033424027264118195, 0.03408483415842056, -0.03259963542222977, -0.00326509028673172, -0.0326678566634655, -0.017755355685949326, 0.07308824360370636, -0.0013033131835982203, -0.04563900828361511, -0.01738762855529785, 0.05179448798298836, 0.014192993752658367, -0.015645496547222137, 0.09992236644029617, 0.03849896043539047, 0.031820524483919144, 0.030747128650546074, 0.04635796695947647, 0.017137782648205757, -0.011740066111087799, -0.015772413462400436, 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735 | 735 | ['Tom Zahavy', 'Nir Ben Zrihem', 'Shie Mannor'] | 1602.02658v4 | In recent years there is a growing interest in using deep representations for
reinforcement learning. In this paper, we present a methodology and tools to
analyze Deep Q-networks (DQNs) in a non-blind matter. Moreover, we propose a
new model, the Semi Aggregated Markov Decision Process (SAMDP), and an
algorithm that learns it automatically. The SAMDP model allows us to identify
spatio-temporal abstractions directly from features and may be used as a
sub-goal detector in future work. Using our tools we reveal that the features
learned by DQNs aggregate the state space in a hierarchical fashion, explaining
its success. Moreover, we are able to understand and describe the policies
learned by DQNs for three different Atari2600 games and suggest ways to
interpret, debug and optimize deep neural networks in reinforcement learning. | Graying the black box: Understanding DQNs | 2,016 | http://arxiv.org/pdf/1602.02658v4 | Title Graying black box Understanding DQNs Summary recent year growing interest using deep representation reinforcement learning paper present methodology tool analyze Deep Qnetworks DQNs nonblind matter Moreover propose new model Semi Aggregated Markov Decision Process SAMDP algorithm learns automatically SAMDP model allows u identify spatiotemporal abstraction directly feature may used subgoal detector future work Using tool reveal feature learned DQNs aggregate state space hierarchical fashion explaining success Moreover able understand describe policy learned DQNs three different Atari2600 game suggest way interpret debug optimize deep neural network reinforcement 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 | [-0.04897117614746094, 0.006604126654565334, -0.014694291166961193, -0.0004318010760471225, 0.018252242356538773, 0.004054898861795664, 0.014945746399462223, -0.024500226601958275, -0.05785233899950981, 0.018399419263005257, 0.0006828439072705805, 0.02430298924446106, -0.012191477231681347, 0.08019841462373734, 0.009793009608983994, -0.07097247242927551, 0.03137693181633949, 0.006076529622077942, -0.024547966197133064, -0.0048619890585541725, 0.011543236672878265, -0.06523098051548004, -0.0150546720251441, 0.040972139686346054, -0.019877811893820763, -0.015793463215231895, -0.04129357263445854, -0.021180547773838043, 0.04245886579155922, -0.07480435818433762, 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736 | 736 | ['Randal S. Olson', 'Nathan Bartley', 'Ryan J. Urbanowicz', 'Jason H. Moore'] | 1603.06212v1 | As the field of data science continues to grow, there will be an
ever-increasing demand for tools that make machine learning accessible to
non-experts. In this paper, we introduce the concept of tree-based pipeline
optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement an open source Tree-based Pipeline
Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a
series of simulated and real-world benchmark data sets. In particular, we show
that TPOT can design machine learning pipelines that provide a significant
improvement over a basic machine learning analysis while requiring little to no
input nor prior knowledge from the user. We also address the tendency for TPOT
to design overly complex pipelines by integrating Pareto optimization, which
produces compact pipelines without sacrificing classification accuracy. As
such, this work represents an important step toward fully automating machine
learning pipeline design. | Evaluation of a Tree-based Pipeline Optimization Tool for Automating
Data Science | 2,016 | http://arxiv.org/pdf/1603.06212v1 | Title Evaluation Treebased Pipeline Optimization Tool Automating Data Science Summary field data science continues grow everincreasing demand tool make machine learning accessible nonexperts paper introduce concept treebased pipeline optimization automating one tedious part machine learningpipeline design implement open source Treebased Pipeline Optimization Tool TPOT Python demonstrate effectiveness series simulated realworld benchmark data set particular show TPOT design machine learning pipeline provide significant improvement basic machine learning analysis requiring little input prior knowledge user also address tendency TPOT design overly complex pipeline integrating Pareto optimization produce compact pipeline without sacrificing classification accuracy work represents important step toward fully automating machine learning pipeline design 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 | [-0.021703533828258514, 0.04981401935219765, -0.04987595975399017, -0.022133897989988327, -0.020270589739084244, 0.0028025531210005283, 0.02102573961019516, 0.005663945805281401, 0.00994651485234499, -0.029387181624770164, 0.0325920432806015, 0.0328822024166584, 0.014238768257200718, 0.11373019218444824, -0.01991215907037258, -0.083404041826725, -0.006345164962112904, -0.001979153137654066, 0.015868254005908966, 0.009349721483886242, -0.028457773849368095, 0.0042005241848528385, -0.008912975899875164, 0.03119862638413906, -0.02638900652527809, 0.004243395756930113, -0.00425180746242404, 0.003782181069254875, 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737 | 737 | ['Quan Liu', 'Hui Jiang', 'Andrew Evdokimov', 'Zhen-Hua Ling', 'Xiaodan Zhu', 'Si Wei', 'Yu Hu'] | 1603.07704v2 | In this paper, we propose a new deep learning approach, called neural
association model (NAM), for probabilistic reasoning in artificial
intelligence. We propose to use neural networks to model association between
any two events in a domain. Neural networks take one event as input and compute
a conditional probability of the other event to model how likely these two
events are to be associated. The actual meaning of the conditional
probabilities varies between applications and depends on how the models are
trained. In this work, as two case studies, we have investigated two NAM
structures, namely deep neural networks (DNN) and relation-modulated neural
nets (RMNN), on several probabilistic reasoning tasks in AI, including
recognizing textual entailment, triple classification in multi-relational
knowledge bases and commonsense reasoning. Experimental results on several
popular datasets derived from WordNet, FreeBase and ConceptNet have all
demonstrated that both DNNs and RMNNs perform equally well and they can
significantly outperform the conventional methods available for these reasoning
tasks. Moreover, compared with DNNs, RMNNs are superior in knowledge transfer,
where a pre-trained model can be quickly extended to an unseen relation after
observing only a few training samples. To further prove the effectiveness of
the proposed models, in this work, we have applied NAMs to solving challenging
Winograd Schema (WS) problems. Experiments conducted on a set of WS problems
prove that the proposed models have the potential for commonsense reasoning. | Probabilistic Reasoning via Deep Learning: Neural Association Models | 2,016 | http://arxiv.org/pdf/1603.07704v2 | Title Probabilistic Reasoning via Deep Learning Neural Association Models Summary paper propose new deep learning approach called neural association model NAM probabilistic reasoning artificial intelligence propose use neural network model association two event domain Neural network take one event input compute conditional probability event model likely two event associated actual meaning conditional probability varies application depends model trained work two case study investigated two NAM structure namely deep neural network DNN relationmodulated neural net RMNN several probabilistic reasoning task AI including recognizing textual entailment triple classification multirelational knowledge base commonsense reasoning Experimental result several popular datasets derived WordNet FreeBase ConceptNet demonstrated DNNs RMNNs perform equally well significantly outperform conventional method available reasoning task Moreover compared DNNs RMNNs superior knowledge transfer pretrained model quickly extended unseen relation observing training sample prove effectiveness proposed model work applied NAMs solving challenging Winograd Schema WS problem Experiments conducted set WS problem prove proposed model potential commonsense reasoning 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 | [0.018449990078806877, 0.017635352909564972, -0.001346656703390181, 0.05619872361421585, 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738 | 738 | ['Ishan P. Durugkar', 'Clemens Rosenbaum', 'Stefan Dernbach', 'Sridhar Mahadevan'] | 1606.04615v1 | Deep reinforcement learning has been shown to be a powerful framework for
learning policies from complex high-dimensional sensory inputs to actions in
complex tasks, such as the Atari domain. In this paper, we explore output
representation modeling in the form of temporal abstraction to improve
convergence and reliability of deep reinforcement learning approaches. We
concentrate on macro-actions, and evaluate these on different Atari 2600 games,
where we show that they yield significant improvements in learning speed.
Additionally, we show that they can even achieve better scores than DQN. We
offer analysis and explanation for both convergence and final results,
revealing a problem deep RL approaches have with sparse reward signals. | Deep Reinforcement Learning With Macro-Actions | 2,016 | http://arxiv.org/pdf/1606.04615v1 | Title Deep Reinforcement Learning MacroActions Summary Deep reinforcement learning shown powerful framework learning policy complex highdimensional sensory input action complex task Atari domain paper explore output representation modeling form temporal abstraction improve convergence reliability deep reinforcement learning approach concentrate macroactions evaluate different Atari 2600 game show yield significant improvement learning speed Additionally show even achieve better score DQN offer analysis explanation convergence final result revealing problem deep RL approach sparse reward signal 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 | [-0.02120278775691986, 0.005359957925975323, -0.047610633075237274, -0.005514223128557205, -0.008647213689982891, -0.0020072441548109055, 0.01049203984439373, -0.026103924959897995, -0.04826332628726959, 0.010998442769050598, -0.033669933676719666, 0.000630987691693008, -0.009201161563396454, 0.07648267596960068, 0.02152256667613983, -0.06910228729248047, 0.02127394638955593, -0.031430646777153015, 0.004282558802515268, -0.009992837905883789, 0.021758927032351494, -0.07223143428564072, -0.012340758927166462, 0.0556282103061676, -0.0165083184838295, 0.008937081322073936, -0.026354972273111343, -0.02479151450097561, 0.0302021112293005, -0.07485825568437576, 0.06105111166834831, -0.009767726995050907, 0.05764956399798393, 0.04908299818634987, 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739 | 739 | ['Edward Choi', 'Mohammad Taha Bahadori', 'Joshua A. Kulas', 'Andy Schuetz', 'Walter F. Stewart', 'Jimeng Sun'] | 1608.05745v4 | Accuracy and interpretability are two dominant features of successful
predictive models. Typically, a choice must be made in favor of complex black
box models such as recurrent neural networks (RNN) for accuracy versus less
accurate but more interpretable traditional models such as logistic regression.
This tradeoff poses challenges in medicine where both accuracy and
interpretability are important. We addressed this challenge by developing the
REverse Time AttentIoN model (RETAIN) for application to Electronic Health
Records (EHR) data. RETAIN achieves high accuracy while remaining clinically
interpretable and is based on a two-level neural attention model that detects
influential past visits and significant clinical variables within those visits
(e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR
data in a reverse time order so that recent clinical visits are likely to
receive higher attention. RETAIN was tested on a large health system EHR
dataset with 14 million visits completed by 263K patients over an 8 year period
and demonstrated predictive accuracy and computational scalability comparable
to state-of-the-art methods such as RNN, and ease of interpretability
comparable to traditional models. | RETAIN: An Interpretable Predictive Model for Healthcare using Reverse
Time Attention Mechanism | 2,016 | http://arxiv.org/pdf/1608.05745v4 | Title RETAIN Interpretable Predictive Model Healthcare using Reverse Time Attention Mechanism Summary Accuracy interpretability two dominant feature successful predictive model Typically choice must made favor complex black box model recurrent neural network RNN accuracy versus le accurate interpretable traditional model logistic regression tradeoff pose challenge medicine accuracy interpretability important addressed challenge developing REverse Time AttentIoN model RETAIN application Electronic Health Records EHR data RETAIN achieves high accuracy remaining clinically interpretable based twolevel neural attention model detects influential past visit significant clinical variable within visit eg key diagnosis RETAIN mimic physician practice attending EHR data reverse time order recent clinical visit likely receive higher attention RETAIN tested large health system EHR dataset 14 million visit completed 263K patient 8 year period demonstrated predictive accuracy computational scalability comparable stateoftheart method RNN ease interpretability comparable traditional 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 | [0.013573057949543, 0.05023243650794029, -0.022835073992609978, -0.03899867832660675, -0.005059984978288412, 0.01578308641910553, -0.024581685662269592, 0.020174631848931313, 0.01758205145597458, 0.020548513159155846, 0.03386103734374046, -0.06458643823862076, 0.03096551075577736, 0.07153351604938507, -0.001317542977631092, -0.04054313153028488, 0.037643980234861374, 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740 | 740 | ['Meng Joo Er', 'Rajasekar Venkatesan', 'Ning Wang'] | 1608.08898v1 | In this paper a high speed neural network classifier based on extreme
learning machines for multi-label classification problem is proposed and
dis-cussed. Multi-label classification is a superset of traditional binary and
multi-class classification problems. The proposed work extends the extreme
learning machine technique to adapt to the multi-label problems. As opposed to
the single-label problem, both the number of labels the sample belongs to, and
each of those target labels are to be identified for multi-label classification
resulting in in-creased complexity. The proposed high speed multi-label
classifier is applied to six benchmark datasets comprising of different
application areas such as multi-media, text and biology. The training time and
testing time of the classifier are compared with those of the state-of-the-arts
methods. Experimental studies show that for all the six datasets, our proposed
technique have faster execution speed and better performance, thereby
outperforming all the existing multi-label clas-sification methods. | A High Speed Multi-label Classifier based on Extreme Learning Machines | 2,016 | http://arxiv.org/pdf/1608.08898v1 | Title High Speed Multilabel Classifier based Extreme Learning Machines Summary paper high speed neural network classifier based extreme learning machine multilabel classification problem proposed discussed Multilabel classification superset traditional binary multiclass classification problem proposed work extends extreme learning machine technique adapt multilabel problem opposed singlelabel problem number label sample belongs target label identified multilabel classification resulting increased complexity proposed high speed multilabel classifier applied six benchmark datasets comprising different application area multimedia text biology training time testing time classifier compared stateofthearts method Experimental study show six datasets proposed technique faster execution speed better performance thereby outperforming existing multilabel classification 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 | [0.011641849763691425, -0.019172636792063713, -0.013563303276896477, -0.0055283233523368835, 0.01633145846426487, 0.04251297563314438, 0.01093616709113121, 0.0065765297040343285, 0.02636253461241722, -0.032061949372291565, -0.018313191831111908, 0.05077899247407913, -0.03119768761098385, 0.03670485317707062, 0.010051202028989792, -0.007994105108082294, 0.003776588011533022, 0.016403833404183388, 0.044983871281147, -0.009328670799732208, 0.03417855501174927, 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741 | 741 | ['Meng Joo Er', 'Rajasekar Venkatesan', 'Ning Wang'] | 1609.00843v1 | Classification involves the learning of the mapping function that associates
input samples to corresponding target label. There are two major categories of
classification problems: Single-label classification and Multi-label
classification. Traditional binary and multi-class classifications are
sub-categories of single-label classification. Several classifiers are
developed for binary, multi-class and multi-label classification problems, but
there are no classifiers available in the literature capable of performing all
three types of classification. In this paper, a novel online universal
classifier capable of performing all the three types of classification is
proposed. Being a high speed online classifier, the proposed technique can be
applied to streaming data applications. The performance of the developed
classifier is evaluated using datasets from binary, multi-class and multi-label
problems. The results obtained are compared with state-of-the-art techniques
from each of the classification types. | An Online Universal Classifier for Binary, Multi-class and Multi-label
Classification | 2,016 | http://arxiv.org/pdf/1609.00843v1 | Title Online Universal Classifier Binary Multiclass Multilabel Classification Summary Classification involves learning mapping function associate input sample corresponding target label two major category classification problem Singlelabel classification Multilabel classification Traditional binary multiclass classification subcategories singlelabel classification Several classifier developed binary multiclass multilabel classification problem classifier available literature capable performing three type classification paper novel online universal classifier capable performing three type classification proposed high speed online classifier proposed technique applied streaming data application performance developed classifier evaluated using datasets binary multiclass multilabel problem result obtained compared stateoftheart technique classification 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 | [0.00635999022051692, 0.026650086045265198, -0.005068789701908827, -0.023504823446273804, 0.010311434976756573, 0.033111415803432465, 0.06899004429578781, 0.015605117194354534, 0.028564417734742165, -0.06869341433048248, 0.0148243959993124, 0.009656363166868687, 0.0006846588221378624, 0.05772661790251732, 0.0012934055412188172, -0.05769435316324234, 0.01777089387178421, 0.018677739426493645, -0.040413450449705124, -0.010695855133235455, 0.01580454781651497, -0.008688682690262794, -0.02167380601167679, 0.035475801676511765, -0.050941091030836105, -0.005882055498659611, -0.037817925214767456, 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742 | 742 | ['Arif Budiman', 'Mohamad Ivan Fanany', 'Chan Basaruddin'] | 1610.01922v1 | A machine learning method needs to adapt to over time changes in the
environment. Such changes are known as concept drift. In this paper, we propose
concept drift tackling method as an enhancement of Online Sequential Extreme
Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by
adding adaptive capability for classification and regression problem. The
scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme
that works well to handle real drift, virtual drift, and hybrid drift. The
AOS-ELM also works well for sudden drift and recurrent context change type. The
scheme is a simple unified method implemented in simple lines of code. We
evaluated AOS-ELM on regression and classification problem by using concept
drift public data set (SEA and STAGGER) and other public data sets such as
MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value
compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice
does not need hidden nodes increase, we address some issues related to the
increasing of the hidden nodes such as error condition and rank values. We
propose taking the rank of the pseudoinverse matrix as an indicator parameter
to detect underfitting condition. | Adaptive Online Sequential ELM for Concept Drift Tackling | 2,016 | http://arxiv.org/pdf/1610.01922v1 | Title Adaptive Online Sequential ELM Concept Drift Tackling Summary machine learning method need adapt time change environment change known concept drift paper propose concept drift tackling method enhancement Online Sequential Extreme Learning Machine OSELM Constructive Enhancement OSELM CEOSELM adding adaptive capability classification regression problem scheme named adaptive OSELM AOSELM single classifier scheme work well handle real drift virtual drift hybrid drift AOSELM also work well sudden drift recurrent context change type scheme simple unified method implemented simple line code evaluated AOSELM regression classification problem using concept drift public data set SEA STAGGER public data set MNIST USPS IDS Experiments show method give higher kappa value compared multiclassifier ELM ensemble Even though AOSELM practice need hidden node increase address issue related increasing hidden node error condition rank value propose taking rank pseudoinverse matrix indicator parameter detect underfitting condition 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 | [-0.018714556470513344, 0.01274622417986393, -0.026175880804657936, -0.039601970463991165, 0.021359985694289207, 0.005881111603230238, -0.005135837011039257, 0.016426902264356613, -0.029276644811034203, -0.01450412068516016, 0.03420611843466759, 0.05599755793809891, 0.005857973359525204, 0.08668451756238937, -0.014963220804929733, -0.022098025307059288, 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743 | 743 | ['Arif Budiman', 'Mohamad Ivan Fanany', 'Chan Basaruddin'] | 1610.02348v1 | In big data era, the data continuously generated and its distribution may
keep changes overtime. These challenges in online stream of data are known as
concept drift. In this paper, we proposed the Adaptive Convolutional ELM method
(ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid
Extreme Learning Machine (ELM) model plus adaptive capability. This method is
aimed for concept drift handling. We enhanced the CNN as convolutional
hiererchical features representation learner combined with Elastic ELM
(E$^2$LM) as a parallel supervised classifier. We propose an Adaptive OS-ELM
(AOS-ELM) for concept drift adaptability in classifier level (named ACNNELM-1)
and matrices concatenation ensembles for concept drift adaptability in ensemble
level (named ACNNELM-2). Our proposed Adaptive CNNELM is flexible that works
well in classifier level and ensemble level while most current methods only
proposed to work on either one of the levels.
We verified our method in extended MNIST data set and not MNIST data set. We
set the experiment to simulate virtual drift, real drift, and hybrid drift
event and we demonstrated how our CNNELM adaptability works. Our proposed
method works well and gives better accuracy, computation scalability, and
concept drifts adaptability compared to the regular ELM and CNN. Further
researches are still required to study the optimum parameters and to use more
varied image data set. | Adaptive Convolutional ELM For Concept Drift Handling in Online Stream
Data | 2,016 | http://arxiv.org/pdf/1610.02348v1 | Title Adaptive Convolutional ELM Concept Drift Handling Online Stream Data Summary big data era data continuously generated distribution may keep change overtime challenge online stream data known concept drift paper proposed Adaptive Convolutional ELM method ACNNELM enhancement Convolutional Neural Network CNN hybrid Extreme Learning Machine ELM model plus adaptive capability method aimed concept drift handling enhanced CNN convolutional hiererchical feature representation learner combined Elastic ELM E2LM parallel supervised classifier propose Adaptive OSELM AOSELM concept drift adaptability classifier level named ACNNELM1 matrix concatenation ensemble concept drift adaptability ensemble level named ACNNELM2 proposed Adaptive CNNELM flexible work well classifier level ensemble level current method proposed work either one level verified method extended MNIST data set MNIST data set set experiment simulate virtual drift real drift hybrid drift event demonstrated CNNELM adaptability work proposed method work well give better accuracy computation scalability concept drift adaptability compared regular ELM CNN research still required study optimum parameter use varied image data set 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 | [-0.023038430139422417, 0.016558583825826645, -0.04026084765791893, 0.013589044101536274, -0.015497859567403793, 0.009319085627794266, -0.005719159264117479, -0.011203566566109657, -0.046573903411626816, -0.005502530373632908, 0.06766276806592941, 0.019047144800424576, 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744 | 744 | ['Daniel Hein', 'Alexander Hentschel', 'Thomas Runkler', 'Steffen Udluft'] | 1610.05984v5 | Fuzzy controllers are efficient and interpretable system controllers for
continuous state and action spaces. To date, such controllers have been
constructed manually or trained automatically either using expert-generated
problem-specific cost functions or incorporating detailed knowledge about the
optimal control strategy. Both requirements for automatic training processes
are not found in most real-world reinforcement learning (RL) problems. In such
applications, online learning is often prohibited for safety reasons because
online learning requires exploration of the problem's dynamics during policy
training. We introduce a fuzzy particle swarm reinforcement learning (FPSRL)
approach that can construct fuzzy RL policies solely by training parameters on
world models that simulate real system dynamics. These world models are created
by employing an autonomous machine learning technique that uses previously
generated transition samples of a real system. To the best of our knowledge,
this approach is the first to relate self-organizing fuzzy controllers to
model-based batch RL. Therefore, FPSRL is intended to solve problems in domains
where online learning is prohibited, system dynamics are relatively easy to
model from previously generated default policy transition samples, and it is
expected that a relatively easily interpretable control policy exists. The
efficiency of the proposed approach with problems from such domains is
demonstrated using three standard RL benchmarks, i.e., mountain car, cart-pole
balancing, and cart-pole swing-up. Our experimental results demonstrate
high-performing, interpretable fuzzy policies. | Particle Swarm Optimization for Generating Interpretable Fuzzy
Reinforcement Learning Policies | 2,016 | http://arxiv.org/pdf/1610.05984v5 | Title Particle Swarm Optimization Generating Interpretable Fuzzy Reinforcement Learning Policies Summary Fuzzy controller efficient interpretable system controller continuous state action space date controller constructed manually trained automatically either using expertgenerated problemspecific cost function incorporating detailed knowledge optimal control strategy requirement automatic training process found realworld reinforcement learning RL problem application online learning often prohibited safety reason online learning requires exploration problem dynamic policy training introduce fuzzy particle swarm reinforcement learning FPSRL approach construct fuzzy RL policy solely training parameter world model simulate real system dynamic world model created employing autonomous machine learning technique us previously generated transition sample real system best knowledge approach first relate selforganizing fuzzy controller modelbased batch RL Therefore FPSRL intended solve problem domain online learning prohibited system dynamic relatively easy model previously generated default policy transition sample expected relatively easily interpretable control policy exists efficiency proposed approach problem domain demonstrated using three standard RL benchmark ie mountain car cartpole balancing cartpole swingup experimental result demonstrate highperforming interpretable fuzzy 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 | [-0.023040346801280975, 0.003841106314212084, 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745 | 745 | ['Marc Pickett', 'Rami Al-Rfou', 'Louis Shao', 'Chris Tar'] | 1610.06402v1 | The long-term memory of most connectionist systems lies entirely in the
weights of the system. Since the number of weights is typically fixed, this
bounds the total amount of knowledge that can be learned and stored. Though
this is not normally a problem for a neural network designed for a specific
task, such a bound is undesirable for a system that continually learns over an
open range of domains. To address this, we describe a lifelong learning system
that leverages a fast, though non-differentiable, content-addressable memory
which can be exploited to encode both a long history of sequential episodic
knowledge and semantic knowledge over many episodes for an unbounded number of
domains. This opens the door for investigation into transfer learning, and
leveraging prior knowledge that has been learned over a lifetime of experiences
to new domains. | A Growing Long-term Episodic & Semantic Memory | 2,016 | http://arxiv.org/pdf/1610.06402v1 | Title Growing Longterm Episodic Semantic Memory Summary longterm memory connectionist system lie entirely weight system Since number weight typically fixed bound total amount knowledge learned stored Though normally problem neural network designed specific task bound undesirable system continually learns open range domain address describe lifelong learning system leverage fast though nondifferentiable contentaddressable memory exploited encode long history sequential episodic knowledge semantic knowledge many episode unbounded number domain open door investigation transfer learning leveraging prior knowledge learned lifetime experience new 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 | [0.017698826268315315, 0.06042559817433357, -0.028343917801976204, 0.01917206309735775, 0.008936609141528606, 0.006127943750470877, 0.003440682077780366, -0.022848647087812424, -0.03195356950163841, -0.009028660133481026, 0.033631667494773865, -0.0035851760767400265, -0.034466084092855453, 0.0609319843351841, -0.008430685847997665, -0.07649809867143631, 0.04145418852567673, -0.01694689691066742, -0.02728785015642643, 0.0036528324708342552, -0.021572204306721687, -0.048582132905721664, -0.0038379591424018145, -0.03428308665752411, -0.049485091120004654, -0.01788143627345562, -0.03230496123433113, -0.0743037760257721, -0.027053434401750565, -0.056628771126270294, -0.00496952747926116, -0.009019995108246803, 0.024979420006275177, 0.013021410442888737, 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746 | 746 | ['Wen-Chieh Fang', 'Yi-ting Chiang'] | 1611.02512v1 | Humans can learn concepts or recognize items from just a handful of examples,
while machines require many more samples to perform the same task. In this
paper, we build a computational model to investigate the possibility of this
kind of rapid learning. The proposed method aims to improve the learning task
of input from sensory memory by leveraging the information retrieved from
long-term memory. We present a simple and intuitive technique called cognitive
discriminative mappings (CDM) to explore the cognitive problem. First, CDM
separates and clusters the data instances retrieved from long-term memory into
distinct classes with a discrimination method in working memory when a sensory
input triggers the algorithm. CDM then maps each sensory data instance to be as
close as possible to the median point of the data group with the same class.
The experimental results demonstrate that the CDM approach is effective for
learning the discriminative features of supervised classifications with few
training sensory input instances. | Cognitive Discriminative Mappings for Rapid Learning | 2,016 | http://arxiv.org/pdf/1611.02512v1 | Title Cognitive Discriminative Mappings Rapid Learning Summary Humans learn concept recognize item handful example machine require many sample perform task paper build computational model investigate possibility kind rapid learning proposed method aim improve learning task input sensory memory leveraging information retrieved longterm memory present simple intuitive technique called cognitive discriminative mapping CDM explore cognitive problem First CDM separate cluster data instance retrieved longterm memory distinct class discrimination method working memory sensory input trigger algorithm CDM map sensory data instance close possible median point data group class experimental result demonstrate CDM approach effective learning discriminative feature supervised classification training sensory input instance 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 | [-0.029868775978684425, -0.07687529176473618, -0.04442603141069412, 0.012023000977933407, 0.0004023114452138543, 0.03359444439411163, 0.012297326698899269, 0.0009574511204846203, 0.05177688226103783, -0.030614037066698074, 0.005810556001961231, 0.010457903146743774, 0.018516918644309044, 0.001411361270584166, -0.002868007170036435, -0.057820819318294525, 0.015432336367666721, 0.020043615251779556, 0.027609096840023994, 0.002618378959596157, -0.014683420769870281, -0.053706094622612, -0.02425229363143444, 0.01423853263258934, -0.02256462164223194, -0.027403805404901505, 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747 | 747 | ['Hao Shen'] | 1611.05827v3 | Training deep neural networks for solving machine learning problems is one
great challenge in the field, mainly due to its associated optimisation problem
being highly non-convex. Recent developments have suggested that many training
algorithms do not suffer from undesired local minima under certain scenario,
and consequently led to great efforts in pursuing mathematical explanations for
such observations. This work provides an alternative mathematical understanding
of the challenge from a smooth optimisation perspective. By assuming exact
learning of finite samples, sufficient conditions are identified via a critical
point analysis to ensure any local minimum to be globally minimal as well.
Furthermore, a state of the art algorithm, known as the Generalised
Gauss-Newton (GGN) algorithm, is rigorously revisited as an approximate
Newton's algorithm, which shares the property of being locally quadratically
convergent to a global minimum under the condition of exact learning. | Towards a Mathematical Understanding of the Difficulty in Learning with
Feedforward Neural Networks | 2,016 | http://arxiv.org/pdf/1611.05827v3 | Title Towards Mathematical Understanding Difficulty Learning Feedforward Neural Networks Summary Training deep neural network solving machine learning problem one great challenge field mainly due associated optimisation problem highly nonconvex Recent development suggested many training algorithm suffer undesired local minimum certain scenario consequently led great effort pursuing mathematical explanation observation work provides alternative mathematical understanding challenge smooth optimisation perspective assuming exact learning finite sample sufficient condition identified via critical point analysis ensure local minimum globally minimal well Furthermore state art algorithm known Generalised GaussNewton GGN algorithm rigorously revisited approximate Newtons algorithm share property locally quadratically convergent global minimum condition exact 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 | [0.001356947934255004, 0.0023668515495955944, -0.0050458284094929695, 0.02698616497218609, -0.017796842381358147, 0.006771114654839039, 0.002815478714182973, 0.002154191955924034, -0.032144974917173386, 0.029983893036842346, 0.01813541166484356, -0.012416559271514416, 0.013703123666346073, 0.03948216885328293, 0.04141166806221008, -0.005026301834732294, -0.019735660403966904, 0.0243728905916214, -0.022205645218491554, -0.06306621432304382, 0.019317571073770523, -0.042305245995521545, -0.02186477929353714, -0.00400456553325057, -0.09650064259767532, -0.0014098737156018615, 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748 | 748 | ['Gonzalo Diaz', 'Achille Fokoue', 'Giacomo Nannicini', 'Horst Samulowitz'] | 1705.08520v1 | A major challenge in designing neural network (NN) systems is to determine
the best structure and parameters for the network given the data for the
machine learning problem at hand. Examples of parameters are the number of
layers and nodes, the learning rates, and the dropout rates. Typically, these
parameters are chosen based on heuristic rules and manually fine-tuned, which
may be very time-consuming, because evaluating the performance of a single
parametrization of the NN may require several hours. This paper addresses the
problem of choosing appropriate parameters for the NN by formulating it as a
box-constrained mathematical optimization problem, and applying a
derivative-free optimization tool that automatically and effectively searches
the parameter space. The optimization tool employs a radial basis function
model of the objective function (the prediction accuracy of the NN) to
accelerate the discovery of configurations yielding high accuracy. Candidate
configurations explored by the algorithm are trained to a small number of
epochs, and only the most promising candidates receive full training. The
performance of the proposed methodology is assessed on benchmark sets and in
the context of predicting drug-drug interactions, showing promising results.
The optimization tool used in this paper is open-source. | An effective algorithm for hyperparameter optimization of neural
networks | 2,017 | http://arxiv.org/pdf/1705.08520v1 | Title effective algorithm hyperparameter optimization neural network Summary major challenge designing neural network NN system determine best structure parameter network given data machine learning problem hand Examples parameter number layer node learning rate dropout rate Typically parameter chosen based heuristic rule manually finetuned may timeconsuming evaluating performance single parametrization NN may require several hour paper address problem choosing appropriate parameter NN formulating boxconstrained mathematical optimization problem applying derivativefree optimization tool automatically effectively search parameter space optimization tool employ radial basis function model objective function prediction accuracy NN accelerate discovery configuration yielding high accuracy Candidate configuration explored algorithm trained small number epoch promising candidate receive full training performance proposed methodology assessed benchmark set context predicting drugdrug interaction showing promising result optimization tool used paper opensource 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 | [-0.0006385190645232797, -0.001494747120887041, -0.02385726384818554, -0.02010945975780487, 0.033184126019477844, -0.02971462346613407, 0.015958281233906746, -0.00976563710719347, 0.0197676420211792, 0.017336972057819366, -0.014380546286702156, 0.046589069068431854, 0.002216424560174346, 0.07431766390800476, 0.01886870339512825, 0.027635468170046806, 0.01991172879934311, 0.04337938502430916, 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749 | 749 | ['Decebal Constantin Mocanu', 'Elena Mocanu', 'Peter Stone', 'Phuong H. Nguyen', 'Madeleine Gibescu', 'Antonio Liotta'] | 1707.04780v1 | Through the success of deep learning, Artificial Neural Networks (ANNs) are
among the most used artificial intelligence methods nowadays. ANNs have led to
major breakthroughs in various domains, such as particle physics, reinforcement
learning, speech recognition, computer vision, and so on. Taking inspiration
from the network properties of biological neural networks (e.g. sparsity,
scale-freeness), we argue that (contrary to general practice) Artificial Neural
Networks (ANN), too, should not have fully-connected layers. We show how ANNs
perform perfectly well with sparsely-connected layers. Following a Darwinian
evolutionary approach, we propose a novel algorithm which evolves an initial
random sparse topology (i.e. an Erd\H{o}s-R\'enyi random graph) of two
consecutive layers of neurons into a scale-free topology, during the ANN
training process. The resulting sparse layers can safely replace the
corresponding fully-connected layers. Our method allows to quadratically reduce
the number of parameters in the fully conencted layers of ANNs, yielding
quadratically faster computational times in both phases (i.e. training and
inference), at no decrease in accuracy. We demonstrate our claims on two
popular ANN types (restricted Boltzmann machine and multi-layer perceptron), on
two types of tasks (supervised and unsupervised learning), and on 14 benchmark
datasets. We anticipate that our approach will enable ANNs having billions of
neurons and evolved topologies to be capable of handling complex real-world
tasks that are intractable using state-of-the-art methods. | Evolutionary Training of Sparse Artificial Neural Networks: A Network
Science Perspective | 2,017 | http://arxiv.org/pdf/1707.04780v1 | Title Evolutionary Training Sparse Artificial Neural Networks Network Science Perspective Summary success deep learning Artificial Neural Networks ANNs among used artificial intelligence method nowadays ANNs led major breakthrough various domain particle physic reinforcement learning speech recognition computer vision Taking inspiration network property biological neural network eg sparsity scalefreeness argue contrary general practice Artificial Neural Networks ANN fullyconnected layer show ANNs perform perfectly well sparselyconnected layer Following Darwinian evolutionary approach propose novel algorithm evolves initial random sparse topology ie ErdHosRenyi random graph two consecutive layer neuron scalefree topology ANN training process resulting sparse layer safely replace corresponding fullyconnected layer method allows quadratically reduce number parameter fully conencted layer ANNs yielding quadratically faster computational time phase ie training inference decrease accuracy demonstrate claim two popular ANN type restricted Boltzmann machine multilayer perceptron two type task supervised unsupervised learning 14 benchmark datasets anticipate approach enable ANNs billion neuron evolved topology capable handling complex realworld task intractable using stateoftheart 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 | [-0.027887728065252304, 0.0174647718667984, -0.032964251935482025, 0.02248745784163475, -0.00527630839496851, -0.034026794135570526, 0.012799931690096855, 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750 | 750 | ['Ritambhara Singh', 'Jack Lanchantin', 'Arshdeep Sekhon', 'Yanjun Qi'] | 1708.00339v3 | The past decade has seen a revolution in genomic technologies that enable a
flood of genome-wide profiling of chromatin marks. Recent literature tried to
understand gene regulation by predicting gene expression from large-scale
chromatin measurements. Two fundamental challenges exist for such learning
tasks: (1) genome-wide chromatin signals are spatially structured,
high-dimensional and highly modular; and (2) the core aim is to understand what
are the relevant factors and how they work together? Previous studies either
failed to model complex dependencies among input signals or relied on separate
feature analysis to explain the decisions. This paper presents an
attention-based deep learning approach; we call AttentiveChrome, that uses a
unified architecture to model and to interpret dependencies among chromatin
factors for controlling gene regulation. AttentiveChrome uses a hierarchy of
multiple Long short-term memory (LSTM) modules to encode the input signals and
to model how various chromatin marks cooperate automatically. AttentiveChrome
trains two levels of attention jointly with the target prediction, enabling it
to attend differentially to relevant marks and to locate important positions
per mark. We evaluate the model across 56 different cell types (tasks) in
human. Not only is the proposed architecture more accurate, but its attention
scores also provide a better interpretation than state-of-the-art feature
visualization methods such as saliency map.
Code and data are shared at www.deepchrome.org | Attend and Predict: Understanding Gene Regulation by Selective Attention
on Chromatin | 2,017 | http://arxiv.org/pdf/1708.00339v3 | Title Attend Predict Understanding Gene Regulation Selective Attention Chromatin Summary past decade seen revolution genomic technology enable flood genomewide profiling chromatin mark Recent literature tried understand gene regulation predicting gene expression largescale chromatin measurement Two fundamental challenge exist learning task 1 genomewide chromatin signal spatially structured highdimensional highly modular 2 core aim understand relevant factor work together Previous study either failed model complex dependency among input signal relied separate feature analysis explain decision paper present attentionbased deep learning approach call AttentiveChrome us unified architecture model interpret dependency among chromatin factor controlling gene regulation AttentiveChrome us hierarchy multiple Long shortterm memory LSTM module encode input signal model various chromatin mark cooperate automatically AttentiveChrome train two level attention jointly target prediction enabling attend differentially relevant mark locate important position per mark evaluate model across 56 different cell type task human proposed architecture accurate attention score also provide better interpretation stateoftheart feature visualization method saliency map Code data shared wwwdeepchromeorg 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 | [-0.004478110931813717, -0.001050654798746109, -0.033037297427654266, -0.04848727956414223, 0.021697215735912323, 0.007799375336617231, 0.06353030353784561, 0.007420167792588472, 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751 | 751 | ['Eric Martin', 'Chris Cundy'] | 1709.04057v2 | Recurrent neural networks (RNNs) are widely used to model sequential data but
their non-linear dependencies between sequence elements prevent parallelizing
training over sequence length. We show the training of RNNs with only linear
sequential dependencies can be parallelized over the sequence length using the
parallel scan algorithm, leading to rapid training on long sequences even with
small minibatch size. We develop a parallel linear recurrence CUDA kernel and
show that it can be applied to immediately speed up training and inference of
several state of the art RNN architectures by up to 9x. We abstract recent work
on linear RNNs into a new framework of linear surrogate RNNs and develop a
linear surrogate model for the long short-term memory unit, the GILR-LSTM, that
utilizes parallel linear recurrence. We extend sequence learning to new
extremely long sequence regimes that were previously out of reach by
successfully training a GILR-LSTM on a synthetic sequence classification task
with a one million timestep dependency. | Parallelizing Linear Recurrent Neural Nets Over Sequence Length | 2,017 | http://arxiv.org/pdf/1709.04057v2 | Title Parallelizing Linear Recurrent Neural Nets Sequence Length Summary Recurrent neural network RNNs widely used model sequential data nonlinear dependency sequence element prevent parallelizing training sequence length show training RNNs linear sequential dependency parallelized sequence length using parallel scan algorithm leading rapid training long sequence even small minibatch size develop parallel linear recurrence CUDA kernel show applied immediately speed training inference several state art RNN architecture 9x abstract recent work linear RNNs new framework linear surrogate RNNs develop linear surrogate model long shortterm memory unit GILRLSTM utilizes parallel linear recurrence extend sequence learning new extremely long sequence regime previously reach successfully training GILRLSTM synthetic sequence classification task one million timestep dependency 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 | [0.02666763961315155, 0.032647982239723206, -0.01742280274629593, 0.04196585342288017, -0.020144179463386536, 0.006452351342886686, 0.03652665764093399, -0.009637504816055298, -0.022059965878725052, -0.01910150796175003, 0.024840177968144417, -0.03230572119355202, 0.033829059451818466, 0.011337578296661377, -0.002574031939730048, -0.04265701770782471, 0.017084265127778053, 0.019798830151557922, 0.03138524293899536, -0.01209393609315157, 0.02727261371910572, 0.0056690978817641735, -0.01601395010948181, 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752 | 752 | ['Filipe Alves Neto Verri', 'Renato Tinós', 'Liang Zhao'] | 1710.09300v1 | Data and knowledge representation are fundamental concepts in machine
learning. The quality of the representation impacts the performance of the
learning model directly. Feature learning transforms or enhances raw data to
structures that are effectively exploited by those models. In recent years,
several works have been using complex networks for data representation and
analysis. However, no feature learning method has been proposed for such
category of techniques. Here, we present an unsupervised feature learning
mechanism that works on datasets with binary features. First, the dataset is
mapped into a feature--sample network. Then, a multi-objective optimization
process selects a set of new vertices to produce an enhanced version of the
network. The new features depend on a nonlinear function of a combination of
preexisting features. Effectively, the process projects the input data into a
higher-dimensional space. To solve the optimization problem, we design two
metaheuristics based on the lexicographic genetic algorithm and the improved
strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced
network contains more information and can be exploited to improve the
performance of machine learning methods. The advantages and disadvantages of
each optimization strategy are discussed. | Feature learning in feature-sample networks using multi-objective
optimization | 2,017 | http://arxiv.org/pdf/1710.09300v1 | Title Feature learning featuresample network using multiobjective optimization Summary Data knowledge representation fundamental concept machine learning quality representation impact performance learning model directly Feature learning transforms enhances raw data structure effectively exploited model recent year several work using complex network data representation analysis However feature learning method proposed category technique present unsupervised feature learning mechanism work datasets binary feature First dataset mapped featuresample network multiobjective optimization process selects set new vertex produce enhanced version network new feature depend nonlinear function combination preexisting feature Effectively process project input data higherdimensional space solve optimization problem design two metaheuristics based lexicographic genetic algorithm improved strength Pareto evolutionary algorithm SPEA2 show enhanced network contains information exploited improve performance machine learning method advantage disadvantage optimization strategy discussed 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 | [-0.0015126197831705213, 0.02810029499232769, -0.0386347770690918, -0.0009802425047382712, -0.003071610815823078, -0.02640114165842533, 0.022930234670639038, 0.006832682061940432, -0.020855717360973358, -0.02538805827498436, -9.471612429479137e-05, 0.011832165531814098, 0.0013946352992206812, 0.07586498558521271, -0.0018622549250721931, -0.027139754965901375, 0.016381287947297096, 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753 | 753 | ['Chelsea Finn', 'Sergey Levine'] | 1710.11622v3 | Learning to learn is a powerful paradigm for enabling models to learn from
data more effectively and efficiently. A popular approach to meta-learning is
to train a recurrent model to read in a training dataset as input and output
the parameters of a learned model, or output predictions for new test inputs.
Alternatively, a more recent approach to meta-learning aims to acquire deep
representations that can be effectively fine-tuned, via standard gradient
descent, to new tasks. In this paper, we consider the meta-learning problem
from the perspective of universality, formalizing the notion of learning
algorithm approximation and comparing the expressive power of the
aforementioned recurrent models to the more recent approaches that embed
gradient descent into the meta-learner. In particular, we seek to answer the
following question: does deep representation combined with standard gradient
descent have sufficient capacity to approximate any learning algorithm? We find
that this is indeed true, and further find, in our experiments, that
gradient-based meta-learning consistently leads to learning strategies that
generalize more widely compared to those represented by recurrent models. | Meta-Learning and Universality: Deep Representations and Gradient
Descent can Approximate any Learning Algorithm | 2,017 | http://arxiv.org/pdf/1710.11622v3 | Title MetaLearning Universality Deep Representations Gradient Descent Approximate Learning Algorithm Summary Learning learn powerful paradigm enabling model learn data effectively efficiently popular approach metalearning train recurrent model read training dataset input output parameter learned model output prediction new test input Alternatively recent approach metalearning aim acquire deep representation effectively finetuned via standard gradient descent new task paper consider metalearning problem perspective universality formalizing notion learning algorithm approximation comparing expressive power aforementioned recurrent model recent approach embed gradient descent metalearner particular seek answer following question deep representation combined standard gradient descent sufficient capacity approximate learning algorithm find indeed true find experiment gradientbased metalearning consistently lead learning strategy generalize widely compared represented recurrent 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 | [-0.02336861379444599, 0.014397252351045609, -0.011752142570912838, 0.02348981611430645, 0.025858798995614052, 0.0008374102762900293, 0.006655561272054911, -0.01857181079685688, -0.06292036175727844, -0.00812552496790886, -0.02127445861697197, 0.0339849479496479, -0.01750265806913376, 0.030898740515112877, 0.046642400324344635, -0.07624586671590805, 0.013487979769706726, 0.022942136973142624, -0.017651652917265892, -0.01663357764482498, 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754 | 754 | ['Paulo Rauber', 'Filipe Mutz', 'Juergen Schmidhuber'] | 1711.06006v1 | Goal-conditional policies allow reinforcement learning agents to pursue
specific goals during different episodes. In addition to their potential to
generalize desired behavior to unseen goals, such policies may also help in
defining options for arbitrary subgoals, enabling higher-level planning. While
trying to achieve a specific goal, an agent may also be able to exploit
information about the degree to which it has achieved alternative goals.
Reinforcement learning agents have only recently been endowed with such
capacity for hindsight, which is highly valuable in environments with sparse
rewards. In this paper, we show how hindsight can be introduced to
likelihood-ratio policy gradient methods, generalizing this capacity to an
entire class of highly successful algorithms. Our preliminary experiments
suggest that hindsight may increase the sample efficiency of policy gradient
methods. | Hindsight policy gradients | 2,017 | http://arxiv.org/pdf/1711.06006v1 | Title Hindsight policy gradient Summary Goalconditional policy allow reinforcement learning agent pursue specific goal different episode addition potential generalize desired behavior unseen goal policy may also help defining option arbitrary subgoals enabling higherlevel planning trying achieve specific goal agent may also able exploit information degree achieved alternative goal Reinforcement learning agent recently endowed capacity hindsight highly valuable environment sparse reward paper show hindsight introduced likelihoodratio policy gradient method generalizing capacity entire class highly successful algorithm preliminary experiment suggest hindsight may increase sample efficiency policy gradient 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 | [0.03146608918905258, 0.06408703327178955, 0.010849918238818645, -0.061614830046892166, 0.009132280945777893, -0.0132643086835742, -0.0090391980484128, 0.02972756326198578, -0.03456856682896614, 0.016909440979361534, 0.0024515341501682997, 0.02041325531899929, -0.04846867918968201, 0.0543789304792881, 0.005326449871063232, -0.07778264582157135, 0.03177611529827118, -0.027942759916186333, 0.010614359751343727, -0.004010612145066261, 0.00789665523916483, -0.052863769233226776, 0.007338784169405699, 0.02081219293177128, -0.04459049552679062, 0.013505558483302593, -0.042685918509960175, -0.01713387854397297, 0.07597606629133224, -0.06041102111339569, 0.06838417053222656, -0.04716610908508301, 0.058965858072042465, 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755 | 755 | ['Mohammad Javad Shafiee', 'Francis Li', 'Brendan Chwyl', 'Alexander Wong'] | 1711.07459v1 | While deep neural networks have been shown in recent years to outperform
other machine learning methods in a wide range of applications, one of the
biggest challenges with enabling deep neural networks for widespread deployment
on edge devices such as mobile and other consumer devices is high computational
and memory requirements. Recently, there has been greater exploration into
small deep neural network architectures that are more suitable for edge
devices, with one of the most popular architectures being SqueezeNet, with an
incredibly small model size of 4.8MB. Taking further advantage of the notion
that many applications of machine learning on edge devices are often
characterized by a low number of target classes, this study explores the
utility of combining architectural modifications and an evolutionary synthesis
strategy for synthesizing even smaller deep neural architectures based on the
more recent SqueezeNet v1.1 macroarchitecture for applications with fewer
target classes. In particular, architectural modifications are first made to
SqueezeNet v1.1 to accommodate for a 10-class ImageNet-10 dataset, and then an
evolutionary synthesis strategy is leveraged to synthesize more efficient deep
neural networks based on this modified macroarchitecture. The resulting
SquishedNets possess model sizes ranging from 2.4MB to 0.95MB (~5.17X smaller
than SqueezeNet v1.1, or 253X smaller than AlexNet). Furthermore, the
SquishedNets are still able to achieve accuracies ranging from 81.2% to 77%,
and able to process at speeds of 156 images/sec to as much as 256 images/sec on
a Nvidia Jetson TX1 embedded chip. These preliminary results show that a
combination of architectural modifications and an evolutionary synthesis
strategy can be a useful tool for producing very small deep neural network
architectures that are well-suited for edge device scenarios. | SquishedNets: Squishing SqueezeNet further for edge device scenarios via
deep evolutionary synthesis | 2,017 | http://arxiv.org/pdf/1711.07459v1 | Title SquishedNets Squishing SqueezeNet edge device scenario via deep evolutionary synthesis Summary deep neural network shown recent year outperform machine learning method wide range application one biggest challenge enabling deep neural network widespread deployment edge device mobile consumer device high computational memory requirement Recently greater exploration small deep neural network architecture suitable edge device one popular architecture SqueezeNet incredibly small model size 48MB Taking advantage notion many application machine learning edge device often characterized low number target class study explores utility combining architectural modification evolutionary synthesis strategy synthesizing even smaller deep neural architecture based recent SqueezeNet v11 macroarchitecture application fewer target class particular architectural modification first made SqueezeNet v11 accommodate 10class ImageNet10 dataset evolutionary synthesis strategy leveraged synthesize efficient deep neural network based modified macroarchitecture resulting SquishedNets posse model size ranging 24MB 095MB 517X smaller SqueezeNet v11 253X smaller AlexNet Furthermore SquishedNets still able achieve accuracy ranging 812 77 able process speed 156 imagessec much 256 imagessec Nvidia Jetson TX1 embedded chip preliminary result show combination architectural modification evolutionary synthesis strategy useful tool producing small deep neural network architecture wellsuited edge device scenario 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 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756 | 756 | ['Pierre-Yves Oudeyer'] | 1712.01626v1 | Autonomous lifelong development and learning is a fundamental capability of
humans, differentiating them from current deep learning systems. However, other
branches of artificial intelligence have designed crucial ingredients towards
autonomous learning: curiosity and intrinsic motivation, social learning and
natural interaction with peers, and embodiment. These mechanisms guide
exploration and autonomous choice of goals, and integrating them with deep
learning opens stimulating perspectives. Deep learning (DL) approaches made
great advances in artificial intelligence, but are still far away from human
learning. As argued convincingly by Lake et al., differences include human
capabilities to learn causal models of the world from very little data,
leveraging compositional representations and priors like intuitive physics and
psychology. However, there are other fundamental differences between current DL
systems and human learning, as well as technical ingredients to fill this gap,
that are either superficially, or not adequately, discussed by Lake et al.
These fundamental mechanisms relate to autonomous development and learning.
They are bound to play a central role in artificial intelligence in the future.
Current DL systems require engineers to manually specify a task-specific
objective function for every new task, and learn through off-line processing of
large training databases. On the contrary, humans learn autonomously open-ended
repertoires of skills, deciding for themselves which goals to pursue or value,
and which skills to explore, driven by intrinsic motivation/curiosity and
social learning through natural interaction with peers. Such learning processes
are incremental, online, and progressive. Human child development involves a
progressive increase of complexity in a curriculum of learning where skills are
explored, acquired, and built on each other, through particular ordering and
timing. Finally, human learning happens in the physical world, and through
bodily and physical experimentation, under severe constraints on energy, time,
and computational resources. In the two last decades, the field of
Developmental and Cognitive Robotics (Cangelosi and Schlesinger, 2015, Asada et
al., 2009), in strong interaction with developmental psychology and
neuroscience, has achieved significant advances in computational | Autonomous development and learning in artificial intelligence and
robotics: Scaling up deep learning to human--like learning | 2,017 | http://arxiv.org/pdf/1712.01626v1 | Title Autonomous development learning artificial intelligence robotics Scaling deep learning humanlike learning Summary Autonomous lifelong development learning fundamental capability human differentiating current deep learning system However branch artificial intelligence designed crucial ingredient towards autonomous learning curiosity intrinsic motivation social learning natural interaction peer embodiment mechanism guide exploration autonomous choice goal integrating deep learning open stimulating perspective Deep learning DL approach made great advance artificial intelligence still far away human learning argued convincingly Lake et al difference include human capability learn causal model world little data leveraging compositional representation prior like intuitive physic psychology However fundamental difference current DL system human learning well technical ingredient fill gap either superficially adequately discussed Lake et al fundamental mechanism relate autonomous development learning bound play central role artificial intelligence future Current DL system require engineer manually specify taskspecific objective function every new task learn offline processing large training database contrary human learn autonomously openended repertoire skill deciding goal pursue value skill explore driven intrinsic motivationcuriosity social learning natural interaction peer learning process incremental online progressive Human child development involves progressive increase complexity curriculum learning skill explored acquired built particular ordering timing Finally human learning happens physical world bodily physical experimentation severe constraint energy time computational resource two last decade field Developmental Cognitive Robotics Cangelosi Schlesinger 2015 Asada et al 2009 strong interaction developmental psychology neuroscience achieved significant advance computational 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 | [0.04060013219714165, 0.03074970282614231, -0.05631304904818535, -0.047551605850458145, 0.00949346274137497, 0.03383752331137657, 0.05041634663939476, -0.04431520029902458, -0.01887732557952404, 0.005613050889223814, 0.006764853838831186, 0.03701455146074295, 0.012814365327358246, 0.05520724132657051, -0.005015905946493149, -0.051825448870658875, 0.016365600749850273, -0.00035972075420431793, -0.020734312012791634, 0.030424611642956734, -0.05333990231156349, -0.01097253616899252, 0.00679125776514411, 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757 | 757 | ['Leonid Peshkin', 'Christian R. Shelton'] | cs/0204043v1 | Searching the space of policies directly for the optimal policy has been one
popular method for solving partially observable reinforcement learning
problems. Typically, with each change of the target policy, its value is
estimated from the results of following that very policy. This requires a large
number of interactions with the environment as different polices are
considered. We present a family of algorithms based on likelihood ratio
estimation that use data gathered when executing one policy (or collection of
policies) to estimate the value of a different policy. The algorithms combine
estimation and optimization stages. The former utilizes experience to build a
non-parametric representation of an optimized function. The latter performs
optimization on this estimate. We show positive empirical results and provide
the sample complexity bound. | Learning from Scarce Experience | 2,002 | http://arxiv.org/pdf/cs/0204043v1 | Title Learning Scarce Experience Summary Searching space policy directly optimal policy one popular method solving partially observable reinforcement learning problem Typically change target policy value estimated result following policy requires large number interaction environment different police considered present family algorithm based likelihood ratio estimation use data gathered executing one policy collection policy estimate value different policy algorithm combine estimation optimization stage former utilizes experience build nonparametric representation optimized function latter performs optimization estimate show positive empirical result provide sample complexity bound 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 | [0.03234167769551277, 0.05274500325322151, 0.0061468263156712055, -0.060232844203710556, -0.016274088993668556, -0.03032250888645649, 0.027484552934765816, -0.0017381637590005994, -0.05202985182404518, 0.03140789270401001, -0.005477289669215679, 0.027840979397296906, -0.033138565719127655, 0.04808039218187332, -0.013696218840777874, -0.03234367072582245, 0.012908423319458961, -0.03706406056880951, -0.0012931493110954762, -0.003125116229057312, 0.03307371959090233, -0.04852057993412018, -0.014737839810550213, 0.02013324201107025, 0.0025155702605843544, 0.012550923973321915, -0.06764926761388779, -0.04362731799483299, 0.06512846797704697, -0.052889008074998856, 0.06654323637485504, -0.050052959471940994, 0.05852564051747322, 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758 | 758 | ['Martin Pelikan', 'Kumara Sastry'] | cs/0402032v1 | This paper describes how fitness inheritance can be used to estimate fitness
for a proportion of newly sampled candidate solutions in the Bayesian
optimization algorithm (BOA). The goal of estimating fitness for some candidate
solutions is to reduce the number of fitness evaluations for problems where
fitness evaluation is expensive. Bayesian networks used in BOA to model
promising solutions and generate the new ones are extended to allow not only
for modeling and sampling candidate solutions, but also for estimating their
fitness. The results indicate that fitness inheritance is a promising concept
in BOA, because population-sizing requirements for building appropriate models
of promising solutions lead to good fitness estimates even if only a small
proportion of candidate solutions is evaluated using the actual fitness
function. This can lead to a reduction of the number of actual fitness
evaluations by a factor of 30 or more. | Fitness inheritance in the Bayesian optimization algorithm | 2,004 | http://arxiv.org/pdf/cs/0402032v1 | Title Fitness inheritance Bayesian optimization algorithm Summary paper describes fitness inheritance used estimate fitness proportion newly sampled candidate solution Bayesian optimization algorithm BOA goal estimating fitness candidate solution reduce number fitness evaluation problem fitness evaluation expensive Bayesian network used BOA model promising solution generate new one extended allow modeling sampling candidate solution also estimating fitness result indicate fitness inheritance promising concept BOA populationsizing requirement building appropriate model promising solution lead good fitness estimate even small proportion candidate solution evaluated using actual fitness function lead reduction number actual fitness evaluation factor 30 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 | [-0.009404526092112064, 0.02924974448978901, -0.029009466990828514, -0.008554755710065365, 0.0031043144408613443, -0.04595917835831642, 0.0030008931644260883, 0.029038438573479652, -0.02064596489071846, -0.01607423461973667, 0.0007445363444276154, 0.02656448818743229, 0.011375466361641884, 0.008967547677457333, -0.02785949409008026, -0.02620936743915081, 0.008328892290592194, 0.011231438256800175, -0.015004863031208515, -0.013167438097298145, -0.023167643696069717, -0.03506801649928093, 0.027828486636281013, 0.03653465211391449, -0.020677147433161736, 0.04616439342498779, -0.00025351898511871696, -0.016566365957260132, 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759 | 759 | ['Vitaly Schetinin', 'Joachim Schult'] | cs/0504070v1 | In this paper we describe a new method combining the polynomial neural
network and decision tree techniques in order to derive comprehensible
classification rules from clinical electroencephalograms (EEGs) recorded from
sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement,
muscle and noise artifacts and as a consequence some EEG features are
irrelevant to classification problems. Combining the polynomial network and
decision tree techniques, we discover comprehensible classification rules
whilst also attempting to keep their classification error down. This technique
is shown to outperform a number of commonly used machine learning technique
applied to automatically recognize artifacts in the sleep EEGs. | The Combined Technique for Detection of Artifacts in Clinical
Electroencephalograms of Sleeping Newborns | 2,005 | http://arxiv.org/pdf/cs/0504070v1 | Title Combined Technique Detection Artifacts Clinical Electroencephalograms Sleeping Newborns Summary paper describe new method combining polynomial neural network decision tree technique order derive comprehensible classification rule clinical electroencephalogram EEGs recorded sleeping newborn EEGs heavily corrupted cardiac eye movement muscle noise artifact consequence EEG feature irrelevant classification problem Combining polynomial network decision tree technique discover comprehensible classification rule whilst also attempting keep classification error technique shown outperform number commonly used machine learning technique applied automatically recognize artifact sleep EEGs 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 | [-0.031679943203926086, 0.015686405822634697, -0.003519346471875906, 0.0034423188772052526, -0.005845001898705959, 0.054155103862285614, -0.01054330449551344, 0.019419606775045395, -0.05232229083776474, -0.016257239505648613, 0.0851726233959198, 0.014769934117794037, 0.05361296236515045, 0.017282292246818542, 0.026769831776618958, 0.00210990640334785, 0.01779729127883911, 0.032026924192905426, -0.0002214399428339675, 0.01680649444460869, -0.008973600342869759, -0.012000557035207748, -0.007819612510502338, 0.03627254441380501, -0.07879558950662613, -0.016002967953681946, -0.0174384918063879, -0.03615330532193184, 0.0028502268251031637, -0.019183818250894547, -0.02059975266456604, 0.015918895602226257, -0.07709560543298721, 0.02233346737921238, 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760 | 760 | ['Greg Hulley', 'Tshilidzi Marwala'] | 0709.3965v2 | The ability of a classifier to take on new information and classes by
evolving the classifier without it having to be fully retrained is known as
incremental learning. Incremental learning has been successfully applied to
many classification problems, where the data is changing and is not all
available at once. In this paper there is a comparison between Learn++, which
is one of the most recent incremental learning algorithms, and the new proposed
method of Incremental Learning Using Genetic Algorithm (ILUGA). Learn++ has
shown good incremental learning capabilities on benchmark datasets on which the
new ILUGA method has been tested. ILUGA has also shown good incremental
learning ability using only a few classifiers and does not suffer from
catastrophic forgetting. The results obtained for ILUGA on the Optical
Character Recognition (OCR) and Wine datasets are good, with an overall
accuracy of 93% and 94% respectively showing a 4% improvement over Learn++.MT
for the difficult multi-class OCR dataset. | Evolving Classifiers: Methods for Incremental Learning | 2,007 | http://arxiv.org/pdf/0709.3965v2 | Title Evolving Classifiers Methods Incremental Learning Summary ability classifier take new information class evolving classifier without fully retrained known incremental learning Incremental learning successfully applied many classification problem data changing available paper comparison Learn one recent incremental learning algorithm new proposed method Incremental Learning Using Genetic Algorithm ILUGA Learn shown good incremental learning capability benchmark datasets new ILUGA method tested ILUGA also shown good incremental learning ability using classifier suffer catastrophic forgetting result obtained ILUGA Optical Character Recognition OCR Wine datasets good overall accuracy 93 94 respectively showing 4 improvement LearnMT difficult multiclass OCR dataset 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 | [0.021815409883856773, 0.10014214366674423, 0.0016821863828226924, 0.010092966258525848, -0.029712988063693047, 0.003038417547941208, -0.020706139504909515, 0.06440241634845734, 0.017087634652853012, -0.03599880263209343, 0.04391868785023689, 0.04782071337103844, -0.016808969900012016, 0.04410529509186745, -0.020251888781785965, 0.006816455163061619, 0.010687009431421757, -0.01488024927675724, -0.019475677981972694, -0.017336178570985794, -0.021160105243325233, -0.023722175508737564, -0.0033575724810361862, -0.010233811102807522, 0.02155100554227829, 0.015192234888672829, -0.041060641407966614, 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761 | 761 | ['Dasika Ratna Deepthi', 'G. R. Aditya Krishna', 'K. Eswaran'] | 0712.0938v1 | This paper proposes an unsupervised learning technique by using Multi-layer
Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer
Mirroring Neural Network is a neural network that can be trained with
generalized data inputs (different categories of image patterns) to perform
non-linear dimensionality reduction and the resultant low-dimensional code is
used for unsupervised pattern classification using Forgy's algorithm. By
adapting the non-linear activation function (modified sigmoidal function) and
initializing the weights and bias terms to small random values, mirroring of
the input pattern is initiated. In training, the weights and bias terms are
changed in such a way that the input presented is reproduced at the output by
back propagating the error. The mirroring neural network is capable of reducing
the input vector to a great degree (approximately 1/30th the original size) and
also able to reconstruct the input pattern at the output layer from this
reduced code units. The feature set (output of central hidden layer) extracted
from this network is fed to Forgy's algorithm, which classify input data
patterns into distinguishable classes. In the implementation of Forgy's
algorithm, initial seed points are selected in such a way that they are distant
enough to be perfectly grouped into different categories. Thus a new method of
unsupervised learning is formulated and demonstrated in this paper. This method
gave impressive results when applied to classification of different image
patterns. | Automatic Pattern Classification by Unsupervised Learning Using
Dimensionality Reduction of Data with Mirroring Neural Networks | 2,007 | http://arxiv.org/pdf/0712.0938v1 | Title Automatic Pattern Classification Unsupervised Learning Using Dimensionality Reduction Data Mirroring Neural Networks Summary paper proposes unsupervised learning technique using Multilayer Mirroring Neural Network Forgys clustering algorithm Multilayer Mirroring Neural Network neural network trained generalized data input different category image pattern perform nonlinear dimensionality reduction resultant lowdimensional code used unsupervised pattern classification using Forgys algorithm adapting nonlinear activation function modified sigmoidal function initializing weight bias term small random value mirroring input pattern initiated training weight bias term changed way input presented reproduced output back propagating error mirroring neural network capable reducing input vector great degree approximately 130th original size also able reconstruct input pattern output layer reduced code unit feature set output central hidden layer extracted network fed Forgys algorithm classify input data pattern distinguishable class implementation Forgys algorithm initial seed point selected way distant enough perfectly grouped different category Thus new method unsupervised learning formulated demonstrated paper method gave impressive result applied classification different image pattern 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 | [-0.02508358657360077, 0.015034792013466358, -0.05080763250589371, 0.07208844274282455, -0.007659881375730038, 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762 | 762 | ['Alejandro Chinea Manrique De Lara', 'Juan Manuel Moreno', 'Arostegui Jordi Madrenas', 'Joan Cabestany'] | 0712.3654v1 | In this paper we shall review the common problems associated with Piecewise
Linear Separation incremental algorithms. This kind of neural models yield poor
performances when dealing with some classification problems, due to the
evolving schemes used to construct the resulting networks. So as to avoid this
undesirable behavior we shall propose a modification criterion. It is based
upon the definition of a function which will provide information about the
quality of the network growth process during the learning phase. This function
is evaluated periodically as the network structure evolves, and will permit, as
we shall show through exhaustive benchmarks, to considerably improve the
performance(measured in terms of network complexity and generalization
capabilities) offered by the networks generated by these incremental models. | Improving the Performance of PieceWise Linear Separation Incremental
Algorithms for Practical Hardware Implementations | 2,007 | http://arxiv.org/pdf/0712.3654v1 | Title Improving Performance PieceWise Linear Separation Incremental Algorithms Practical Hardware Implementations Summary paper shall review common problem associated Piecewise Linear Separation incremental algorithm kind neural model yield poor performance dealing classification problem due evolving scheme used construct resulting network avoid undesirable behavior shall propose modification criterion based upon definition function provide information quality network growth process learning phase function evaluated periodically network structure evolves permit shall show exhaustive benchmark considerably improve performancemeasured term network complexity generalization capability offered network generated incremental 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 | [0.0001494176685810089, 0.07189055532217026, -0.022032974287867546, 0.0388922281563282, -0.040404386818408966, -0.03054353967308998, 0.07194789499044418, 0.032455530017614365, -0.017937183380126953, -0.04106805846095085, 0.06806177645921707, 0.013983677141368389, -0.03122500143945217, 0.07794579118490219, -0.006947570014744997, 0.011451918631792068, 0.021859342232346535, 0.010469695553183556, 0.01284058392047882, 0.02430797554552555, -0.0005344737437553704, -0.0340370275080204, -0.003713893936946988, 0.004121191333979368, -0.020405950024724007, -0.021177468821406364, -0.02376610040664673, -0.02140810899436474, -0.0038777252193540335, -0.006767717190086842, 0.05445325747132301, 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763 | 763 | ['N. Suguna', 'K. Thanushkodi'] | 1006.4540v1 | Feature selection refers to the problem of selecting relevant features which
produce the most predictive outcome. In particular, feature selection task is
involved in datasets containing huge number of features. Rough set theory has
been one of the most successful methods used for feature selection. However,
this method is still not able to find optimal subsets. This paper proposes a
new feature selection method based on Rough set theory hybrid with Bee Colony
Optimization (BCO) in an attempt to combat this. This proposed work is applied
in the medical domain to find the minimal reducts and experimentally compared
with the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methods
such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle
Swarm Optimization (PSO). | A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee
Colony Optimization | 2,010 | http://arxiv.org/pdf/1006.4540v1 | Title Novel Rough Set Reduct Algorithm Medical Domain Based Bee Colony Optimization Summary Feature selection refers problem selecting relevant feature produce predictive outcome particular feature selection task involved datasets containing huge number feature Rough set theory one successful method used feature selection However method still able find optimal subset paper proposes new feature selection method based Rough set theory hybrid Bee Colony Optimization BCO attempt combat proposed work applied medical domain find minimal reducts experimentally compared Quick Reduct Entropy Based Reduct hybrid Rough Set method Genetic Algorithm GA Ant Colony Optimization ACO Particle Swarm Optimization PSO 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 | [-0.01260025892406702, -0.04312650486826897, -0.026841871440410614, -0.061229243874549866, -0.031140973791480064, 0.008109571412205696, -0.01014422532171011, 0.06688329577445984, 0.028713781386613846, 0.0020852144807577133, 0.07218075543642044, -0.006249288097023964, 0.018859602510929108, 0.031781069934368134, -0.04068062826991081, -0.040925558656454086, -0.016222918406128883, 0.056532327085733414, 0.01677907444536686, -0.0503791943192482, -0.008341914974153042, -0.02501179836690426, 0.007296326104551554, 0.06121556833386421, 0.013005297631025314, 0.03720337897539139, 0.044311802834272385, -0.04764530807733536, 0.001942116068676114, 0.002476259833201766, 0.023631630465388298, 0.022678660228848457, -0.04707499220967293, -0.0288575179874897, 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764 | 764 | ['Pekka Malo', 'Pyry Siitari', 'Ankur Sinha'] | 1012.0841v1 | Most of the existing information retrieval systems are based on bag of words
model and are not equipped with common world knowledge. Work has been done
towards improving the efficiency of such systems by using intelligent
algorithms to generate search queries, however, not much research has been done
in the direction of incorporating human-and-society level knowledge in the
queries. This paper is one of the first attempts where such information is
incorporated into the search queries using Wikipedia semantics. The paper
presents an essential shift from conventional token based queries to concept
based queries, leading to an enhanced efficiency of information retrieval
systems. To efficiently handle the automated query learning problem, we propose
Wikipedia-based Evolutionary Semantics (Wiki-ES) framework where concept based
queries are learnt using a co-evolving evolutionary procedure. Learning concept
based queries using an intelligent evolutionary procedure yields significant
improvement in performance which is shown through an extensive study using
Reuters newswire documents. Comparison of the proposed framework is performed
with other information retrieval systems. Concept based approach has also been
implemented on other information retrieval systems to justify the effectiveness
of a transition from token based queries to concept based queries. | Automated Query Learning with Wikipedia and Genetic Programming | 2,010 | http://arxiv.org/pdf/1012.0841v1 | Title Automated Query Learning Wikipedia Genetic Programming Summary existing information retrieval system based bag word model equipped common world knowledge Work done towards improving efficiency system using intelligent algorithm generate search query however much research done direction incorporating humanandsociety level knowledge query paper one first attempt information incorporated search query using Wikipedia semantics paper present essential shift conventional token based query concept based query leading enhanced efficiency information retrieval system efficiently handle automated query learning problem propose Wikipediabased Evolutionary Semantics WikiES framework concept based query learnt using coevolving evolutionary procedure Learning concept based query using intelligent evolutionary procedure yield significant improvement performance shown extensive study using Reuters newswire document Comparison proposed framework performed information retrieval system Concept based approach also implemented information retrieval system justify effectiveness transition token based query concept based query 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 | [0.07866102457046509, 0.035998765379190445, -0.037776295095682144, 0.02672942355275154, -0.009266302920877934, 0.009866967797279358, -0.04097622632980347, 0.06424432247877121, -0.012354201637208462, -0.04645919427275658, 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765 | 765 | ['Weishan Dong', 'Tianshi Chen', 'Peter Tino', 'Xin Yao'] | 1111.2221v1 | Since Estimation of Distribution Algorithms (EDA) were proposed, many
attempts have been made to improve EDAs' performance in the context of global
optimization. So far, the studies or applications of multivariate probabilistic
model based continuous EDAs are still restricted to rather low dimensional
problems (smaller than 100D). Traditional EDAs have difficulties in solving
higher dimensional problems because of the curse of dimensionality and their
rapidly increasing computational cost. However, scaling up continuous EDAs for
higher dimensional optimization is still necessary, which is supported by the
distinctive feature of EDAs: Because a probabilistic model is explicitly
estimated, from the learnt model one can discover useful properties or features
of the problem. Besides obtaining a good solution, understanding of the problem
structure can be of great benefit, especially for black box optimization. We
propose a novel EDA framework with Model Complexity Control (EDA-MCC) to scale
up EDAs. By using Weakly dependent variable Identification (WI) and Subspace
Modeling (SM), EDA-MCC shows significantly better performance than traditional
EDAs on high dimensional problems. Moreover, the computational cost and the
requirement of large population sizes can be reduced in EDA-MCC. In addition to
being able to find a good solution, EDA-MCC can also produce a useful problem
structure characterization. EDA-MCC is the first successful instance of
multivariate model based EDAs that can be effectively applied a general class
of up to 500D problems. It also outperforms some newly developed algorithms
designed specifically for large scale optimization. In order to understand the
strength and weakness of EDA-MCC, we have carried out extensive computational
studies of EDA-MCC. Our results have revealed when EDA-MCC is likely to
outperform others on what kind of benchmark functions. | Scaling Up Estimation of Distribution Algorithms For Continuous
Optimization | 2,011 | http://arxiv.org/pdf/1111.2221v1 | Title Scaling Estimation Distribution Algorithms Continuous Optimization Summary Since Estimation Distribution Algorithms EDA proposed many attempt made improve EDAs performance context global optimization far study application multivariate probabilistic model based continuous EDAs still restricted rather low dimensional problem smaller 100D Traditional EDAs difficulty solving higher dimensional problem curse dimensionality rapidly increasing computational cost However scaling continuous EDAs higher dimensional optimization still necessary supported distinctive feature EDAs probabilistic model explicitly estimated learnt model one discover useful property feature problem Besides obtaining good solution understanding problem structure great benefit especially black box optimization propose novel EDA framework Model Complexity Control EDAMCC scale EDAs using Weakly dependent variable Identification WI Subspace Modeling SM EDAMCC show significantly better performance traditional EDAs high dimensional problem Moreover computational cost requirement large population size reduced EDAMCC addition able find good solution EDAMCC also produce useful problem structure characterization EDAMCC first successful instance multivariate model based EDAs effectively applied general class 500D problem also outperforms newly developed algorithm designed specifically large scale optimization order understand strength weakness EDAMCC carried extensive computational study EDAMCC result revealed EDAMCC likely outperform others kind benchmark 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 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766 | 766 | ['Martin Pelikan', 'Mark W. Hauschild', 'Pier Luca Lanzi'] | 1203.5443v2 | An automated technique has recently been proposed to transfer learning in the
hierarchical Bayesian optimization algorithm (hBOA) based on distance-based
statistics. The technique enables practitioners to improve hBOA efficiency by
collecting statistics from probabilistic models obtained in previous hBOA runs
and using the obtained statistics to bias future hBOA runs on similar problems.
The purpose of this paper is threefold: (1) test the technique on several
classes of NP-complete problems, including MAXSAT, spin glasses and minimum
vertex cover; (2) demonstrate that the technique is effective even when
previous runs were done on problems of different size; (3) provide empirical
evidence that combining transfer learning with other efficiency enhancement
techniques can often yield nearly multiplicative speedups. | Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA | 2,012 | http://arxiv.org/pdf/1203.5443v2 | Title Transfer Learning Soft DistanceBased Bias Hierarchical BOA Summary automated technique recently proposed transfer learning hierarchical Bayesian optimization algorithm hBOA based distancebased statistic technique enables practitioner improve hBOA efficiency collecting statistic probabilistic model obtained previous hBOA run using obtained statistic bias future hBOA run similar problem purpose paper threefold 1 test technique several class NPcomplete problem including MAXSAT spin glass minimum vertex cover 2 demonstrate technique effective even previous run done problem different size 3 provide empirical evidence combining transfer learning efficiency enhancement technique often yield nearly multiplicative speedup 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 | [-0.014708032831549644, 0.009262935258448124, 0.012727460823953152, -0.007349533028900623, -0.016176505014300346, -0.023754090070724487, 0.04161599278450012, 0.02191346138715744, -0.0016984455287456512, -0.012812601402401924, -0.01368795521557331, 0.025898709893226624, 0.04316175729036331, 0.06633852422237396, -0.003300870070233941, -0.03861895203590393, -0.01307292003184557, -0.029697807505726814, 0.03069586306810379, -0.019968880340456963, -0.02824108675122261, -0.027848748490214348, 0.0053431023843586445, 0.026415439322590828, -0.011798704043030739, 0.027134841307997704, -0.03805941715836525, 0.011589236557483673, 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767 | 767 | ['Richard J. Preen', 'Larry Bull'] | 1204.4200v2 | A number of representation schemes have been presented for use within
Learning Classifier Systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using a discrete
dynamical system representation within the XCS Learning Classifier System. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules. It is shown possible to
use self-adaptive, open-ended evolution to design an ensemble of such discrete
dynamical systems within XCS to solve a number of well-known test problems. | Discrete Dynamical Genetic Programming in XCS | 2,012 | http://arxiv.org/pdf/1204.4200v2 | Title Discrete Dynamical Genetic Programming XCS Summary number representation scheme presented use within Learning Classifier Systems ranging binary encoding neural network paper present result investigation using discrete dynamical system representation within XCS Learning Classifier System particular asynchronous random Boolean network used represent traditional conditionaction production system rule shown possible use selfadaptive openended evolution design ensemble discrete dynamical system within XCS solve number wellknown test 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 | [-0.034023359417915344, 0.030810607597231865, -0.06012888252735138, -0.0337800532579422, -0.040345024317502975, -0.03351465240120888, 0.0017823297530412674, 0.011603031307458878, -0.003092412371188402, 0.0024450630880892277, 0.10172656178474426, 0.020060474053025246, -0.02859484776854515, 0.09362750500440598, -0.029521606862545013, 0.07002685219049454, 0.042300645262002945, 0.012277868576347828, -0.03926767781376839, -0.018147921189665794, -0.029083099216222763, -0.05697421357035637, -0.0016896672314032912, 0.010571801103651524, -0.019068164750933647, -0.03671509400010109, -0.03576422110199928, -0.04720427095890045, -0.0018793410854414105, -0.003993199206888676, 0.011922664940357208, -0.020534228533506393, 0.02954055368900299, 0.06290117651224136, 2.093606553899008e-06, -0.02822881005704403, 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768 | 768 | ['Richard J. Preen', 'Larry Bull'] | 1204.4202v1 | A number of representation schemes have been presented for use within
Learning Classifier Systems, ranging from binary encodings to Neural Networks,
and more recently Dynamical Genetic Programming (DGP). This paper presents
results from an investigation into using a fuzzy DGP representation within the
XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic
Networks are used to represent the traditional condition-action production
system rules. It is shown possible to use self-adaptive, open-ended evolution
to design an ensemble of such fuzzy dynamical systems within XCSF to solve
several well-known continuous-valued test problems. | Fuzzy Dynamical Genetic Programming in XCSF | 2,012 | http://arxiv.org/pdf/1204.4202v1 | Title Fuzzy Dynamical Genetic Programming XCSF Summary number representation scheme presented use within Learning Classifier Systems ranging binary encoding Neural Networks recently Dynamical Genetic Programming DGP paper present result investigation using fuzzy DGP representation within XCSF Learning Classifier System particular asynchronous Fuzzy Logic Networks used represent traditional conditionaction production system rule shown possible use selfadaptive openended evolution design ensemble fuzzy dynamical system within XCSF solve several wellknown continuousvalued test 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 | [-0.03398854285478592, 0.04416368901729584, -0.06830047070980072, -0.02871222421526909, -0.008317552506923676, -0.01767830178141594, -0.018961185589432716, 0.02694653533399105, -0.019403336569666862, -0.0214847344905138, 0.07777810096740723, -0.002548235235735774, -0.011316522024571896, 0.07732318341732025, -0.0246636550873518, 0.048060040920972824, 0.04392150789499283, 0.020614217966794968, -0.010532640852034092, -0.012221964076161385, -0.0197583194822073, -0.05787472054362297, 0.0017702949699014425, 0.02515285089612007, -0.031812634319067, -0.012158075347542763, -0.023002970963716507, -0.04291488602757454, 0.011814622208476067, 0.009984978474676609, 0.029384968802332878, -0.015590205788612366, 0.026908177882432938, 0.04573928192257881, 2.284649326611543e-06, -0.004915047902613878, 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769 | 769 | ['Jonathan Roberts', 'Ke Chen'] | 1308.6415v2 | Procedural content generation (PCG) has recently become one of the hottest
topics in computational intelligence and AI game researches. Among a variety of
PCG techniques, search-based approaches overwhelmingly dominate PCG development
at present. While SBPCG leads to promising results and successful applications,
it poses a number of challenges ranging from representation to evaluation of
the content being generated. In this paper, we present an alternative yet
generic PCG framework, named learning-based procedure content generation
(LBPCG), to provide potential solutions to several challenging problems in
existing PCG techniques. By exploring and exploiting information gained in game
development and public beta test via data-driven learning, our framework can
generate robust content adaptable to end-user or target players on-line with
minimal interruption to their experience. Furthermore, we develop enabling
techniques to implement the various models required in our framework. For a
proof of concept, we have developed a prototype based on the classic open
source first-person shooter game, Quake. Simulation results suggest that our
framework is promising in generating quality content. | Learning-Based Procedural Content Generation | 2,013 | http://arxiv.org/pdf/1308.6415v2 | Title LearningBased Procedural Content Generation Summary Procedural content generation PCG recently become one hottest topic computational intelligence AI game research Among variety PCG technique searchbased approach overwhelmingly dominate PCG development present SBPCG lead promising result successful application pose number challenge ranging representation evaluation content generated paper present alternative yet generic PCG framework named learningbased procedure content generation LBPCG provide potential solution several challenging problem existing PCG technique exploring exploiting information gained game development public beta test via datadriven learning framework generate robust content adaptable enduser target player online minimal interruption experience Furthermore develop enabling technique implement various model required framework proof concept developed prototype based classic open source firstperson shooter game Quake Simulation result suggest framework promising generating quality content 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 | [0.006704776082187891, 0.028883595019578934, -0.03291783481836319, 0.02905902825295925, 0.002205322030931711, -0.006693611852824688, -0.01848847232758999, -0.00043214860488660634, -0.01983494684100151, 0.04324449598789215, -0.0035593716893345118, 0.016182001680135727, -0.025613220408558846, 0.05025029927492142, -0.02052929997444153, -0.027806462720036507, 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770 | 770 | ['Wojciech Jaśkowski'] | 1406.1509v3 | N-tuple networks have been successfully used as position evaluation functions
for board games such as Othello or Connect Four. The effectiveness of such
networks depends on their architecture, which is determined by the placement of
constituent n-tuples, sequences of board locations, providing input to the
network. The most popular method of placing n-tuples consists in randomly
generating a small number of long, snake-shaped board location sequences. In
comparison, we show that learning n-tuple networks is significantly more
effective if they involve a large number of systematically placed, short,
straight n-tuples. Moreover, we demonstrate that in order to obtain the best
performance and the steepest learning curve for Othello it is enough to use
n-tuples of size just 2, yielding a network consisting of only 288 weights. The
best such network evolved in this study has been evaluated in the online
Othello League, obtaining the performance of nearly 96% --- more than any other
player to date. | Systematic N-tuple Networks for Position Evaluation: Exceeding 90% in
the Othello League | 2,014 | http://arxiv.org/pdf/1406.1509v3 | Title Systematic Ntuple Networks Position Evaluation Exceeding 90 Othello League Summary Ntuple network successfully used position evaluation function board game Othello Connect Four effectiveness network depends architecture determined placement constituent ntuples sequence board location providing input network popular method placing ntuples consists randomly generating small number long snakeshaped board location sequence comparison show learning ntuple network significantly effective involve large number systematically placed short straight ntuples Moreover demonstrate order obtain best performance steepest learning curve Othello enough use ntuples size 2 yielding network consisting 288 weight best network evolved study evaluated online Othello League obtaining performance nearly 96 player date 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 | [-0.00758735416457057, -0.0052129290997982025, -0.035774797201156616, 0.021313093602657318, -0.02567540854215622, -0.06571634858846664, 0.05021866038441658, -0.01986871287226677, -0.03476303815841675, 0.0057578133419156075, -0.0013465570518746972, -0.009042971767485142, 0.01685352623462677, 0.05219406262040138, 0.06060771271586418, -0.025890665128827095, -0.043948858976364136, 0.007500776555389166, -0.00944256130605936, 0.006940110586583614, -0.031918082386255264, -0.050180818885564804, -0.03923190385103226, 0.033401425927877426, -0.00184047338552773, 0.005064384080469608, -0.08382608741521835, -0.005186027381569147, 0.021496636793017387, 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771 | 771 | ['Önder Gürcan', 'Carole Bernon', 'Kemal S. Türker'] | 1207.3760v1 | In this paper, the early design of our self-organized agent-based simulation
model for exploration of synaptic connections that faithfully generates what is
observed in natural situation is given. While we take inspiration from
neuroscience, our intent is not to create a veridical model of processes in
neurodevelopmental biology, nor to represent a real biological system. Instead,
our goal is to design a simulation model that learns acting in the same way of
human nervous system by using findings on human subjects using reflex
methodologies in order to estimate unknown connections. | Towards a Self-Organized Agent-Based Simulation Model for Exploration of
Human Synaptic Connections | 2,012 | http://arxiv.org/pdf/1207.3760v1 | Title Towards SelfOrganized AgentBased Simulation Model Exploration Human Synaptic Connections Summary paper early design selforganized agentbased simulation model exploration synaptic connection faithfully generates observed natural situation given take inspiration neuroscience intent create veridical model process neurodevelopmental biology represent real biological system Instead goal design simulation model learns acting way human nervous system using finding human subject using reflex methodology order estimate unknown connection 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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772 | 772 | ['G. N. Tripathi', 'V. Rihani'] | 1207.4931v1 | The paper presents the electronic design and motion planning of a robot based
on decision making regarding its straight motion and precise turn using
Artificial Neural Network (ANN). The ANN helps in learning of robot so that it
performs motion autonomously. The weights calculated are implemented in
microcontroller. The performance has been tested to be excellent. | Motion Planning Of an Autonomous Mobile Robot Using Artificial Neural
Network | 2,012 | http://arxiv.org/pdf/1207.4931v1 | Title Motion Planning Autonomous Mobile Robot Using Artificial Neural Network Summary paper present electronic design motion planning robot based decision making regarding straight motion precise turn using Artificial Neural Network ANN ANN help learning robot performs motion autonomously weight calculated implemented microcontroller performance tested excellent 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 | [0.02890363149344921, -0.04013105854392052, 0.0036974656395614147, -0.025588860735297203, 0.00411352701485157, 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|
773 | 773 | ['Rónán Daly', 'Qiang Shen'] | 1401.3464v1 | Bayesian networks are a useful tool in the representation of uncertain
knowledge. This paper proposes a new algorithm called ACO-E, to learn the
structure of a Bayesian network. It does this by conducting a search through
the space of equivalence classes of Bayesian networks using Ant Colony
Optimization (ACO). To this end, two novel extensions of traditional ACO
techniques are proposed and implemented. Firstly, multiple types of moves are
allowed. Secondly, moves can be given in terms of indices that are not based on
construction graph nodes. The results of testing show that ACO-E performs
better than a greedy search and other state-of-the-art and metaheuristic
algorithms whilst searching in the space of equivalence classes. | Learning Bayesian Network Equivalence Classes with Ant Colony
Optimization | 2,014 | http://arxiv.org/pdf/1401.3464v1 | Title Learning Bayesian Network Equivalence Classes Ant Colony Optimization Summary Bayesian network useful tool representation uncertain knowledge paper proposes new algorithm called ACOE learn structure Bayesian network conducting search space equivalence class Bayesian network using Ant Colony Optimization ACO end two novel extension traditional ACO technique proposed implemented Firstly multiple type move allowed Secondly move given term index based construction graph node result testing show ACOE performs better greedy search stateoftheart metaheuristic algorithm whilst searching space equivalence class 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 | [-0.021568967029452324, -0.0019393136026337743, -0.020777583122253418, -0.00777273578569293, -0.025497393682599068, -0.01368641946464777, -0.0033678885083645582, 0.03526681289076805, 0.02410024218261242, -0.04581594094634056, 0.0031160584185272455, 0.04981612786650658, -0.012527765706181526, 0.03706486523151398, -0.03672957047820091, -0.009205181151628494, 0.01934170536696911, 0.0636133924126625, 0.04187968373298645, -0.015323485247790813, 0.03223690390586853, -0.046813398599624634, -0.016899945214390755, 0.0500953383743763, 0.010728755034506321, 0.012597940862178802, -0.038123272359371185, -0.040844209492206573, 0.056074295192956924, -0.03207965940237045, 0.03981694206595421, 0.022361692041158676, -0.05338268727064133, 0.011641762219369411, 2.115548795700306e-06, -0.05760621652007103, -0.05554615706205368, -0.03223191201686859, 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774 | 774 | ['Kenton W. Murray', 'Jayant Krishnamurthy'] | 1612.00712v1 | We present probabilistic neural programs, a framework for program induction
that permits flexible specification of both a computational model and inference
algorithm while simultaneously enabling the use of deep neural networks.
Probabilistic neural programs combine a computation graph for specifying a
neural network with an operator for weighted nondeterministic choice. Thus, a
program describes both a collection of decisions as well as the neural network
architecture used to make each one. We evaluate our approach on a challenging
diagram question answering task where probabilistic neural programs correctly
execute nearly twice as many programs as a baseline model. | Probabilistic Neural Programs | 2,016 | http://arxiv.org/pdf/1612.00712v1 | Title Probabilistic Neural Programs Summary present probabilistic neural program framework program induction permit flexible specification computational model inference algorithm simultaneously enabling use deep neural network Probabilistic neural program combine computation graph specifying neural network operator weighted nondeterministic choice Thus program describes collection decision well neural network architecture used make one evaluate approach challenging diagram question answering task probabilistic neural program correctly execute nearly twice many program baseline 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 | [-0.02065419964492321, 0.05876593291759491, -0.023386411368846893, 0.005874189548194408, -0.036246638745069504, -0.03405193239450455, -0.015628725290298462, -0.06682796776294708, -0.027826080098748207, 0.016735929995775223, 0.06017959490418434, 0.018140411004424095, 0.03910701349377632, 0.08217211067676544, 0.01499116513878107, -0.01806032285094261, 0.008013404905796051, -0.03802480921149254, 0.018233448266983032, -0.012078821659088135, -0.002550622448325157, -0.0025980668142437935, -0.003479916602373123, 0.057027362287044525, -0.04030073806643486, 0.03272293880581856, -0.03478569537401199, 0.008067633025348186, -0.03865564987063408, -0.05775126814842224, 0.035926833748817444, 0.0718488097190857, -0.010824309661984444, 0.010918178595602512, 2.4930573090387043e-06, -0.031971704214811325, 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-0.016603391617536545, -0.02934582717716694, 3.2994512418736634e-34, 0.013012876734137535, 0.019475767388939857, -0.03630858287215233, -0.028085606172680855, -0.023247862234711647, 0.025203784927725792, -0.019975783303380013, 0.024005068466067314, 0.004714295268058777, -0.029024194926023483, -0.06480090320110321] |
775 | 775 | ['András Lőrincz', 'Máté Csákvári', 'Áron Fóthi', 'Zoltán Ádám Milacski', 'András Sárkány', 'Zoltán Tősér'] | 1612.00745v1 | Machine learning is making substantial progress in diverse applications. The
success is mostly due to advances in deep learning. However, deep learning can
make mistakes and its generalization abilities to new tasks are questionable.
We ask when and how one can combine network outputs, when (i) details of the
observations are evaluated by learned deep components and (ii) facts and
confirmation rules are available in knowledge based systems. We show that in
limited contexts the required number of training samples can be low and
self-improvement of pre-trained networks in more general context is possible.
We argue that the combination of sparse outlier detection with deep components
that can support each other diminish the fragility of deep methods, an
important requirement for engineering applications. We argue that supervised
learning of labels may be fully eliminated under certain conditions: a
component based architecture together with a knowledge based system can train
itself and provide high quality answers. We demonstrate these concepts on the
State Farm Distracted Driver Detection benchmark. We argue that the view of the
Study Panel (2016) may overestimate the requirements on `years of focused
research' and `careful, unique construction' for `AI systems'. | Cognitive Deep Machine Can Train Itself | 2,016 | http://arxiv.org/pdf/1612.00745v1 | Title Cognitive Deep Machine Train Summary Machine learning making substantial progress diverse application success mostly due advance deep learning However deep learning make mistake generalization ability new task questionable ask one combine network output detail observation evaluated learned deep component ii fact confirmation rule available knowledge based system show limited context required number training sample low selfimprovement pretrained network general context possible argue combination sparse outlier detection deep component support diminish fragility deep method important requirement engineering application argue supervised learning label may fully eliminated certain condition component based architecture together knowledge based system train provide high quality answer demonstrate concept State Farm Distracted Driver Detection benchmark argue view Study Panel 2016 may overestimate requirement year focused research careful unique construction AI system 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 | [-0.01585247926414013, 0.006229399237781763, -0.011902273632586002, -0.011448919773101807, 0.01015924196690321, 0.02907133474946022, 0.04583369567990303, -0.030547264963388443, -0.0053973812609910965, -0.013417177833616734, 0.016290709376335144, 0.040741998702287674, -0.01556606125086546, 0.04691661149263382, 0.020898116752505302, -0.03179082274436951, 0.014908363111317158, 0.019027922302484512, 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776 | 776 | ['Alexander L. Gaunt', 'Marc Brockschmidt', 'Rishabh Singh', 'Nate Kushman', 'Pushmeet Kohli', 'Jonathan Taylor', 'Daniel Tarlow'] | 1612.00817v1 | We study machine learning formulations of inductive program synthesis; that
is, given input-output examples, synthesize source code that maps inputs to
corresponding outputs. Our key contribution is TerpreT, a domain-specific
language for expressing program synthesis problems. A TerpreT model is composed
of a specification of a program representation and an interpreter that
describes how programs map inputs to outputs. The inference task is to observe
a set of input-output examples and infer the underlying program. From a TerpreT
model we automatically perform inference using four different back-ends:
gradient descent (thus each TerpreT model can be seen as defining a
differentiable interpreter), linear program (LP) relaxations for graphical
models, discrete satisfiability solving, and the Sketch program synthesis
system. 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 proper
comparisons between different approaches to inference.
We illustrate the value of TerpreT by developing several interpreter models
and performing an extensive empirical comparison between alternative inference
algorithms on a variety of program models. To our knowledge, this is the first
work to compare gradient-based search over program space to traditional
search-based alternatives. Our key empirical finding is that constraint solvers
dominate the gradient descent and LP-based formulations.
This is a workshop summary of a longer report at arXiv:1608.04428 | Summary - TerpreT: A Probabilistic Programming Language for Program
Induction | 2,016 | http://arxiv.org/pdf/1612.00817v1 | Title Summary TerpreT Probabilistic Programming Language Program Induction Summary study machine learning formulation inductive program synthesis given inputoutput example synthesize source code map input corresponding output key contribution TerpreT domainspecific language expressing program synthesis problem TerpreT model composed specification program representation interpreter describes program map input output inference task observe set inputoutput example infer underlying program TerpreT model automatically perform inference using four different backends gradient descent thus TerpreT model seen defining differentiable interpreter linear program LP relaxation graphical model discrete satisfiability solving Sketch program synthesis system TerpreT two main benefit First enables rapid exploration range domain program representation interpreter model Second separate model specification inference algorithm allowing proper comparison different approach inference illustrate value TerpreT developing several interpreter model performing extensive empirical comparison alternative inference algorithm variety program model knowledge first work compare gradientbased search program space traditional searchbased alternative key empirical finding constraint solver dominate gradient descent LPbased formulation workshop summary longer report arXiv160804428 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 | [-0.019659869372844696, -0.004071400035172701, -0.02797825261950493, 0.0072052963078022, 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777 | 777 | ['Pierre Baldi', 'Peter Sadowski', 'Zhiqin Lu'] | 1612.02734v2 | Random backpropagation (RBP) is a variant of the backpropagation algorithm
for training neural networks, where the transpose of the forward matrices are
replaced by fixed random matrices in the calculation of the weight updates. It
is remarkable both because of its effectiveness, in spite of using random
matrices to communicate error information, and because it completely removes
the taxing requirement of maintaining symmetric weights in a physical neural
system. To better understand random backpropagation, we first connect it to the
notions of local learning and learning channels. Through this connection, we
derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP
(ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their
computational complexity. We then study their behavior through simulations
using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that
most of these variants work robustly, almost as well as backpropagation, and
that multiplication by the derivatives of the activation functions is
important. As a follow-up, we study also the low-end of the number of bits
required to communicate error information over the learning channel. We then
provide partial intuitive explanations for some of the remarkable properties of
RBP and its variations. Finally, we prove several mathematical results,
including the convergence to fixed points of linear chains of arbitrary length,
the convergence to fixed points of linear autoencoders with decorrelated data,
the long-term existence of solutions for linear systems with a single hidden
layer and convergence in special cases, and the convergence to fixed points of
non-linear chains, when the derivative of the activation functions is included. | Learning in the Machine: Random Backpropagation and the Deep Learning
Channel | 2,016 | http://arxiv.org/pdf/1612.02734v2 | Title Learning Machine Random Backpropagation Deep Learning Channel Summary Random backpropagation RBP variant backpropagation algorithm training neural network transpose forward matrix replaced fixed random matrix calculation weight update remarkable effectiveness spite using random matrix communicate error information completely remove taxing requirement maintaining symmetric weight physical neural system better understand random backpropagation first connect notion local learning learning channel connection derive several alternative RBP including skipped RBP SRPB adaptive RBP ARBP sparse RBP combination eg ASRBP analyze computational complexity study behavior simulation using MNIST CIFAR10 bechnmark datasets simulation show variant work robustly almost well backpropagation multiplication derivative activation function important followup study also lowend number bit required communicate error information learning channel provide partial intuitive explanation remarkable property RBP variation Finally prove several mathematical result including convergence fixed point linear chain arbitrary length convergence fixed point linear autoencoders decorrelated data longterm existence solution linear system single hidden layer convergence special case convergence fixed point nonlinear chain derivative activation function included 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 | [0.010330100543797016, -0.0180104598402977, -0.01898561604321003, 0.0033646111842244864, -0.006797737441956997, -0.028400084003806114, 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|
778 | 778 | ['Klaus Greff', 'Rupesh K. Srivastava', 'Jürgen Schmidhuber'] | 1612.07771v3 | The past year saw the introduction of new architectures such as Highway
networks and Residual networks which, for the first time, enabled the training
of feedforward networks with dozens to hundreds of layers using simple gradient
descent. While depth of representation has been posited as a primary reason for
their success, there are indications that these architectures defy a popular
view of deep learning as a hierarchical computation of increasingly abstract
features at each layer.
In this report, we argue that this view is incomplete and does not adequately
explain several recent findings. We propose an alternative viewpoint based on
unrolled iterative estimation -- a group of successive layers iteratively
refine their estimates of the same features instead of computing an entirely
new representation. We demonstrate that this viewpoint directly leads to the
construction of Highway and Residual networks. Finally we provide preliminary
experiments to discuss the similarities and differences between the two
architectures. | Highway and Residual Networks learn Unrolled Iterative Estimation | 2,016 | http://arxiv.org/pdf/1612.07771v3 | Title Highway Residual Networks learn Unrolled Iterative Estimation Summary past year saw introduction new architecture Highway network Residual network first time enabled training feedforward network dozen hundred layer using simple gradient descent depth representation posited primary reason success indication architecture defy popular view deep learning hierarchical computation increasingly abstract feature layer report argue view incomplete adequately explain several recent finding propose alternative viewpoint based unrolled iterative estimation group successive layer iteratively refine estimate feature instead computing entirely new representation demonstrate viewpoint directly lead construction Highway Residual network Finally provide preliminary experiment discus similarity difference two architecture 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 | [-0.025647025555372238, 0.08940903842449188, -0.008388910442590714, 0.05896079167723656, -0.006155331153422594, 0.015333243645727634, 0.01831926964223385, -0.005297107622027397, -0.03869413956999779, 0.006378860678523779, 0.011321782134473324, 0.01754014752805233, 0.0028352364897727966, -0.0004428575630299747, -0.0030902759172022343, -0.05163383483886719, -0.02453424036502838, 0.0211398396641016, -0.012533623725175858, -0.04424576461315155, 0.002743215300142765, -0.0218312107026577, 0.009354257956147194, 0.03965969383716583, -0.07876275479793549, 0.03050265833735466, 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779 | 779 | ['Tamas Madl'] | 1612.09205v1 | Despite of the pain and limited accuracy of blood tests for early recognition
of cardiovascular disease, they dominate risk screening and triage. On the
other hand, heart rate variability is non-invasive and cheap, but not
considered accurate enough for clinical practice. Here, we tackle heart beat
interval based classification with deep learning. We introduce an end to end
differentiable hybrid architecture, consisting of a layer of biological neuron
models of cardiac dynamics (modified FitzHugh Nagumo neurons) and several
layers of a standard feed-forward neural network. The proposed model is
evaluated on ECGs from 474 stable at-risk (coronary artery disease) patients,
and 1172 chest pain patients of an emergency department. We show that it can
significantly outperform models based on traditional heart rate variability
predictors, as well as approaching or in some cases outperforming clinical
blood tests, based only on 60 seconds of inter-beat intervals. | Deep neural heart rate variability analysis | 2,016 | http://arxiv.org/pdf/1612.09205v1 | Title Deep neural heart rate variability analysis Summary Despite pain limited accuracy blood test early recognition cardiovascular disease dominate risk screening triage hand heart rate variability noninvasive cheap considered accurate enough clinical practice tackle heart beat interval based classification deep learning introduce end end differentiable hybrid architecture consisting layer biological neuron model cardiac dynamic modified FitzHugh Nagumo neuron several layer standard feedforward neural network proposed model evaluated ECGs 474 stable atrisk coronary artery disease patient 1172 chest pain patient emergency department show significantly outperform model based traditional heart rate variability predictor well approaching case outperforming clinical blood test based 60 second interbeat interval 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 | [-0.0301542766392231, -0.0011170697398483753, -0.015254385769367218, -0.007004126440733671, 0.02874237298965454, -0.004598874598741531, 0.032159462571144104, 0.0011184316826984286, -0.008572089485824108, 0.05171731859445572, 0.04561648145318031, -0.019412219524383545, 0.03170312941074371, 0.07260346412658691, -0.014522301033139229, -0.013995624147355556, 0.020262284204363823, 0.058188941329717636, 0.05240527167916298, -0.003051391337066889, 0.024554572999477386, -0.03124411217868328, -0.0050770556554198265, 0.05336552485823631, -0.016769347712397575, 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780 | 780 | ['Jianlin Cheng'] | 0704.1028v1 | Ordinal regression is an important type of learning, which has properties of
both classification and regression. Here we describe a simple and effective
approach to adapt a traditional neural network to learn ordinal categories. Our
approach is a generalization of the perceptron method for ordinal regression.
On several benchmark datasets, our method (NNRank) outperforms a neural network
classification method. Compared with the ordinal regression methods using
Gaussian processes and support vector machines, NNRank achieves comparable
performance. Moreover, NNRank has the advantages of traditional neural
networks: learning in both online and batch modes, handling very large training
datasets, and making rapid predictions. These features make NNRank a useful and
complementary tool for large-scale data processing tasks such as information
retrieval, web page ranking, collaborative filtering, and protein ranking in
Bioinformatics. | A neural network approach to ordinal regression | 2,007 | http://arxiv.org/pdf/0704.1028v1 | Title neural network approach ordinal regression Summary Ordinal regression important type learning property classification regression describe simple effective approach adapt traditional neural network learn ordinal category approach generalization perceptron method ordinal regression several benchmark datasets method NNRank outperforms neural network classification method Compared ordinal regression method using Gaussian process support vector machine NNRank achieves comparable performance Moreover NNRank advantage traditional neural network learning online batch mode handling large training datasets making rapid prediction feature make NNRank useful complementary tool largescale data processing task information retrieval web page ranking collaborative filtering protein ranking Bioinformatics 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 | [1.9890934709110297e-05, 0.08679355680942535, -0.03430856764316559, -0.006286998745054007, 0.024378515779972076, -0.0016859039897099137, 0.03369252011179924, 0.021915830671787262, -0.0006870360812172294, -0.01151077076792717, 0.046184342354536057, 0.02408537268638611, 0.009612297639250755, 0.04741339012980461, 0.01034573931246996, -0.03345575928688049, 0.02349749207496643, 0.030536584556102753, 0.02352534420788288, 0.02048507332801819, -0.028465913608670235, -0.01645413413643837, 0.026993829756975174, 0.025489259511232376, 0.008357195183634758, -0.0011397592024877667, 0.003226540982723236, -0.019361712038517, 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781 | 781 | ['Keyvan Yahya', 'Pouyan Rafiei Fard'] | 1007.0546v4 | Although the Music Sight Reading process has been studied from the cognitive
psychology view points, but the computational learning methods like the
Reinforcement Learning have not yet been used to modeling of such processes. In
this paper, with regards to essential properties of our specific problem, we
consider the value function concept and will indicate that the optimum policy
can be obtained by the method we offer without to be getting involved with
computing of the complex value functions. Also, we will offer a normative
behavioral model for the interaction of the agent with the musical pitch
environment and by using a slightly different version of Partially observable
Markov decision processes we will show that our method helps for faster
learning of state-action pairs in our implemented agents. | Computational Model of Music Sight Reading: A Reinforcement Learning
Approach | 2,010 | http://arxiv.org/pdf/1007.0546v4 | Title Computational Model Music Sight Reading Reinforcement Learning Approach Summary Although Music Sight Reading process studied cognitive psychology view point computational learning method like Reinforcement Learning yet used modeling process paper regard essential property specific problem consider value function concept indicate optimum policy obtained method offer without getting involved computing complex value function Also offer normative behavioral model interaction agent musical pitch environment using slightly different version Partially observable Markov decision process show method help faster learning stateaction pair implemented agent 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 | [-0.015092303976416588, -0.036629535257816315, -0.02216220460832119, 0.012726135551929474, -0.027104927226901054, -0.028467558324337006, 0.0231417715549469, 0.02002018131315708, -0.007233802694827318, 0.039952173829078674, -0.04206623509526253, 0.05796084553003311, 0.015421158634126186, 0.03981016203761101, -0.011850845068693161, -0.08334428071975708, -0.02053091675043106, -0.046618226915597916, 0.038890816271305084, 0.008765351958572865, 0.0013253070646896958, -0.051480770111083984, -0.03770329803228378, 0.011874212883412838, -0.0184725821018219, -0.010640728287398815, -0.050522882491350174, -0.03722112253308296, 0.005240284837782383, -0.028621403500437737, -0.03534996137022972, -0.01536624226719141, -0.005863936152309179, 0.024873647838830948, 2.0130628399783745e-06, -0.03311895579099655, -0.0075663235038518906, 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782 | 782 | ['Habib Shah', 'Rozaida Ghazali', 'Nazri Mohd Nawi'] | 1112.4628v1 | Nowadays, computer scientists have shown the interest in the study of social
insect's behaviour in neural networks area for solving different combinatorial
and statistical problems. Chief among these is the Artificial Bee Colony (ABC)
algorithm. This paper investigates the use of ABC algorithm that simulates the
intelligent foraging behaviour of a honey bee swarm. Multilayer Perceptron
(MLP) trained with the standard back propagation algorithm normally utilises
computationally intensive training algorithms. One of the crucial problems with
the backpropagation (BP) algorithm is that it can sometimes yield the networks
with suboptimal weights because of the presence of many local optima in the
solution space. To overcome ABC algorithm used in this work to train MLP
learning the complex behaviour of earthquake time series data trained by BP,
the performance of MLP-ABC is benchmarked against MLP training with the
standard BP. The experimental result shows that MLP-ABC performance is better
than MLP-BP for time series data. | Using Artificial Bee Colony Algorithm for MLP Training on Earthquake
Time Series Data Prediction | 2,011 | http://arxiv.org/pdf/1112.4628v1 | Title Using Artificial Bee Colony Algorithm MLP Training Earthquake Time Series Data Prediction Summary Nowadays computer scientist shown interest study social insect behaviour neural network area solving different combinatorial statistical problem Chief among Artificial Bee Colony ABC algorithm paper investigates use ABC algorithm simulates intelligent foraging behaviour honey bee swarm Multilayer Perceptron MLP trained standard back propagation algorithm normally utilises computationally intensive training algorithm One crucial problem backpropagation BP algorithm sometimes yield network suboptimal weight presence many local optimum solution space overcome ABC algorithm used work train MLP learning complex behaviour earthquake time series data trained BP performance MLPABC benchmarked MLP training standard BP experimental result show MLPABC performance better MLPBP time series 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 | [-0.022705523297190666, 0.0008704561623744667, -0.029974015429615974, -0.005475393030792475, -0.026523591950535774, -0.024368036538362503, -0.022687533870339394, -0.02391619049012661, 0.04949353635311127, 0.0014225913910195231, 0.03828252851963043, 0.03995503857731819, -0.01710430718958378, -0.003329209517687559, 0.02797151729464531, 0.004215565975755453, 0.009939940646290779, 0.06708064675331116, 0.014071209356188774, -0.0030302507802844048, 0.0032180154230445623, -0.06402169913053513, 0.0021448396146297455, 0.04143055900931358, -0.01908131316304207, 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783 | 783 | ['Guanjiao Ren', 'Weihai Chen', 'Sakyasingha Dasgupta', 'Christoph Kolodziejski', 'Florentin Wörgötter', 'Poramate Manoonpong'] | 1407.3269v1 | An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller has difficulties dealing with leg malfunction. Specifically, in the
scenarios presented here, its movement permanently deviates from the desired
trajectory. To address this problem, we extend the single chaotic CPG to
multiple CPGs with learning. The learning mechanism is based on a simulated
annealing algorithm. In a normal situation, the CPGs synchronize and their
dynamics are identical. With leg malfunction or disability, the CPGs lose
synchronization leading to independent dynamics. In this case, the learning
mechanism is applied to automatically adjust the remaining legs' oscillation
frequencies so that the robot adapts its locomotion to deal with the
malfunction. As a consequence, the trajectory produced by the multiple chaotic
CPGs resembles the original trajectory far better than the one produced by only
a single CPG. The performance of the system is evaluated first in a physical
simulation of a quadruped as well as a hexapod robot and finally in a real
six-legged walking machine called AMOSII. The experimental results presented
here reveal that using multiple CPGs with learning is an effective approach for
adaptive locomotion generation where, for instance, different body parts have
to perform independent movements for malfunction compensation. | Multiple chaotic central pattern generators with learning for legged
locomotion and malfunction compensation | 2,014 | http://arxiv.org/pdf/1407.3269v1 | Title Multiple chaotic central pattern generator learning legged locomotion malfunction compensation Summary originally chaotic system controlled various periodic dynamic implemented legged robot locomotion control central pattern generator CPG sophisticated gait pattern arise robot perform various walking behavior However single chaotic CPG controller difficulty dealing leg malfunction Specifically scenario presented movement permanently deviate desired trajectory address problem extend single chaotic CPG multiple CPGs learning learning mechanism based simulated annealing algorithm normal situation CPGs synchronize dynamic identical leg malfunction disability CPGs lose synchronization leading independent dynamic case learning mechanism applied automatically adjust remaining leg oscillation frequency robot adapts locomotion deal malfunction consequence trajectory produced multiple chaotic CPGs resembles original trajectory far better one produced single CPG performance system evaluated first physical simulation quadruped well hexapod robot finally real sixlegged walking machine called AMOSII experimental result presented reveal using multiple CPGs learning effective approach adaptive locomotion generation instance different body part perform independent movement malfunction compensation 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 | [0.011876724660396576, -0.0229293555021286, -0.029470782727003098, -0.01031036488711834, 0.024149449542164803, 0.01428877841681242, 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784 | 784 | ['Christopher Clark', 'Amos Storkey'] | 1412.3409v2 | Mastering the game of Go has remained a long standing challenge to the field
of AI. Modern computer Go systems rely on processing millions of possible
future positions to play well, but intuitively a stronger and more 'humanlike'
way to play the game would be to rely on pattern recognition abilities rather
then brute force computation. Following this sentiment, we train deep
convolutional neural networks to play Go by training them to predict the moves
made by expert Go players. To solve this problem we introduce a number of novel
techniques, including a method of tying weights in the network to 'hard code'
symmetries that are expect to exist in the target function, and demonstrate in
an ablation study they considerably improve performance. Our final networks are
able to achieve move prediction accuracies of 41.1% and 44.4% on two different
Go datasets, surpassing previous state of the art on this task by significant
margins. Additionally, while previous move prediction programs have not yielded
strong Go playing programs, we show that the networks trained in this work
acquired high levels of skill. Our convolutional neural networks can
consistently defeat the well known Go program GNU Go, indicating it is state of
the art among programs that do not use Monte Carlo Tree Search. It is also able
to win some games against state of the art Go playing program Fuego while using
a fraction of the play time. This success at playing Go indicates high level
principles of the game were learned. | Teaching Deep Convolutional Neural Networks to Play Go | 2,014 | http://arxiv.org/pdf/1412.3409v2 | Title Teaching Deep Convolutional Neural Networks Play Go Summary Mastering game Go remained long standing challenge field AI Modern computer Go system rely processing million possible future position play well intuitively stronger humanlike way play game would rely pattern recognition ability rather brute force computation Following sentiment train deep convolutional neural network play Go training predict move made expert Go player solve problem introduce number novel technique including method tying weight network hard code symmetry expect exist target function demonstrate ablation study considerably improve performance final network able achieve move prediction accuracy 411 444 two different Go datasets surpassing previous state art task significant margin Additionally previous move prediction program yielded strong Go playing program show network trained work acquired high level skill convolutional neural network consistently defeat well known Go program GNU Go indicating state art among program use Monte Carlo Tree Search also able win game state art Go playing program Fuego using fraction play time success playing Go indicates high level principle game learned 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 | [0.006599697284400463, 0.04295464977622032, -0.040187060832977295, 0.0573764406144619, 0.023335492238402367, -0.04744700714945793, 0.028163326904177666, -0.0036823363043367863, -0.064762644469738, 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785 | 785 | ['Kratarth Goel', 'Raunaq Vohra', 'J. K. Sahoo'] | 1412.7927v1 | In this paper, we propose a generic technique to model temporal dependencies
and sequences using a combination of a recurrent neural network and a Deep
Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state
of the RNN that allows it to provide temporal information and a multi-layer DBN
that helps in high level representation of the data. This makes RNN-DBNs ideal
for sequence generation. Further, the use of a DBN in conjunction with the RNN
makes this model capable of significantly more complex data representation than
an RBM. We apply this technique to the task of polyphonic music generation. | Polyphonic Music Generation by Modeling Temporal Dependencies Using a
RNN-DBN | 2,014 | http://arxiv.org/pdf/1412.7927v1 | Title Polyphonic Music Generation Modeling Temporal Dependencies Using RNNDBN Summary paper propose generic technique model temporal dependency sequence using combination recurrent neural network Deep Belief Network technique RNNDBN amalgamation memory state RNN allows provide temporal information multilayer DBN help high level representation data make RNNDBNs ideal sequence generation use DBN conjunction RNN make model capable significantly complex data representation RBM apply technique task polyphonic music generation 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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786 | 786 | ['Arun Nair', 'Praveen Srinivasan', 'Sam Blackwell', 'Cagdas Alcicek', 'Rory Fearon', 'Alessandro De Maria', 'Vedavyas Panneershelvam', 'Mustafa Suleyman', 'Charles Beattie', 'Stig Petersen', 'Shane Legg', 'Volodymyr Mnih', 'Koray Kavukcuoglu', 'David Silver'] | 1507.04296v2 | We present the first massively distributed architecture for deep
reinforcement learning. This architecture uses four main components: parallel
actors that generate new behaviour; parallel learners that are trained from
stored experience; a distributed neural network to represent the value function
or behaviour policy; and a distributed store of experience. We used our
architecture to implement the Deep Q-Network algorithm (DQN). Our distributed
algorithm was applied to 49 games from Atari 2600 games from the Arcade
Learning Environment, using identical hyperparameters. Our performance
surpassed non-distributed DQN in 41 of the 49 games and also reduced the
wall-time required to achieve these results by an order of magnitude on most
games. | Massively Parallel Methods for Deep Reinforcement Learning | 2,015 | http://arxiv.org/pdf/1507.04296v2 | Title Massively Parallel Methods Deep Reinforcement Learning Summary present first massively distributed architecture deep reinforcement learning architecture us four main component parallel actor generate new behaviour parallel learner trained stored experience distributed neural network represent value function behaviour policy distributed store experience used architecture implement Deep QNetwork algorithm DQN distributed algorithm applied 49 game Atari 2600 game Arcade Learning Environment using identical hyperparameters performance surpassed nondistributed DQN 41 49 game also reduced walltime required achieve result order magnitude game 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 | [0.01950511336326599, 0.04397379606962204, -0.035955168306827545, 0.02042825147509575, 0.0004354389675427228, 0.025455953553318977, 0.01727382279932499, -0.02691436931490898, -0.06118806079030037, 0.022097965702414513, -0.06944891065359116, 0.0007897754549048841, -0.034655291587114334, 0.017840558663010597, -0.011427433229982853, -0.006027689669281244, 0.02561754174530506, 0.00805874913930893, -0.006303167901933193, -0.03308761864900589, 0.007491424214094877, -0.047427210956811905, -0.015077770687639713, 0.019541526213288307, -0.0065354541875422, -0.025985948741436005, -0.042754724621772766, 0.01461913250386715, 0.04427769035100937, -0.07540789246559143, 0.05003765597939491, -0.039426565170288086, 0.040484629571437836, 0.0779435932636261, 1.9832425550703192e-06, 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787 | 787 | ['Maxim Borisyak', 'Andrey Ustyuzhanin'] | 1507.07374v1 | The problem of autonomous navigation is one of the basic problems for
robotics. Although, in general, it may be challenging when an autonomous
vehicle is placed into partially observable domain. In this paper we consider
simplistic environment model and introduce a navigation algorithm based on
Learning Classifier System. | A genetic algorithm for autonomous navigation in partially observable
domain | 2,015 | http://arxiv.org/pdf/1507.07374v1 | Title genetic algorithm autonomous navigation partially observable domain Summary problem autonomous navigation one basic problem robotics Although general may challenging autonomous vehicle placed partially observable domain paper consider simplistic environment model introduce navigation algorithm based Learning Classifier System 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 | [0.04368383437395096, 0.008930462412536144, -0.019828274846076965, -0.0644092857837677, -0.02506658434867859, 0.009054241701960564, 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788 | 788 | ['Hao Yi Ong', 'Kevin Chavez', 'Augustus Hong'] | 1508.04186v2 | We propose a distributed deep learning model to successfully learn control
policies directly from high-dimensional sensory input using reinforcement
learning. The model is based on the deep Q-network, a convolutional neural
network trained with a variant of Q-learning. Its input is raw pixels and its
output is a value function estimating future rewards from taking an action
given a system state. To distribute the deep Q-network training, we adapt the
DistBelief software framework to the context of efficiently training
reinforcement learning agents. As a result, the method is completely
asynchronous and scales well with the number of machines. We demonstrate that
the deep Q-network agent, receiving only the pixels and the game score as
inputs, was able to achieve reasonable success on a simple game with minimal
parameter tuning. | Distributed Deep Q-Learning | 2,015 | http://arxiv.org/pdf/1508.04186v2 | Title Distributed Deep QLearning Summary propose distributed deep learning model successfully learn control policy directly highdimensional sensory input using reinforcement learning model based deep Qnetwork convolutional neural network trained variant Qlearning input raw pixel output value function estimating future reward taking action given system state distribute deep Qnetwork training adapt DistBelief software framework context efficiently training reinforcement learning agent result method completely asynchronous scale well number machine demonstrate deep Qnetwork agent receiving pixel game score input able achieve reasonable success simple game minimal parameter tuning 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 | [-0.016913609579205513, 0.04123321920633316, -0.0237545408308506, 0.03703076392412186, -0.009450439363718033, -0.019529834389686584, 0.03755539655685425, -0.007258306257426739, -0.03266405686736107, 0.030578289180994034, -0.04520018398761749, 0.02052122727036476, -0.03057701885700226, 0.08222495764493942, 0.02227124571800232, -0.02937994711101055, 0.01278771087527275, 0.037604089826345444, -0.041284430772066116, -0.024084394797682762, 0.0029514243360608816, -0.0625615194439888, 0.003617755137383938, 0.024097314104437828, -0.04578090086579323, -0.02412937767803669, -0.055414821952581406, 0.012418866157531738, 0.03471088409423828, -0.0720762088894844, 0.04484125226736069, -0.03667614981532097, 0.03357308357954025, 0.07694388180971146, 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789 | 789 | ['Gustav Sourek', 'Vojtech Aschenbrenner', 'Filip Zelezny', 'Ondrej Kuzelka'] | 1508.05128v2 | We propose a method combining relational-logic representations with neural
network learning. A general lifted architecture, possibly reflecting some
background domain knowledge, is described through relational rules which may be
handcrafted or learned. The relational rule-set serves as a template for
unfolding possibly deep neural networks whose structures also reflect the
structures of given training or testing relational examples. Different networks
corresponding to different examples share their weights, which co-evolve during
training by stochastic gradient descent algorithm. The framework allows for
hierarchical relational modeling constructs and learning of latent relational
concepts through shared hidden layers weights corresponding to the rules.
Discovery of notable relational concepts and experiments on 78 relational
learning benchmarks demonstrate favorable performance of the method. | Lifted Relational Neural Networks | 2,015 | http://arxiv.org/pdf/1508.05128v2 | Title Lifted Relational Neural Networks Summary propose method combining relationallogic representation neural network learning general lifted architecture possibly reflecting background domain knowledge described relational rule may handcrafted learned relational ruleset serf template unfolding possibly deep neural network whose structure also reflect structure given training testing relational example Different network corresponding different example share weight coevolve training stochastic gradient descent algorithm framework allows hierarchical relational modeling construct learning latent relational concept shared hidden layer weight corresponding rule Discovery notable relational concept experiment 78 relational learning benchmark demonstrate favorable performance 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 | [0.01719510741531849, 0.06408540159463882, -0.04534464329481125, 0.05606064200401306, -0.03824929520487785, -0.011926489882171154, -0.0183150302618742, 0.03267604112625122, -0.009359707124531269, -0.02553440071642399, -0.007110713981091976, 0.004581031855195761, -0.008772253058850765, 0.03764282166957855, -0.013838107697665691, -0.054672397673130035, -0.0015597135061398149, 0.036093007773160934, -0.024726513773202896, 0.019697608426213264, -0.01379278302192688, 0.015099011361598969, 0.023111799731850624, 0.034224528819322586, -0.04361915588378906, -0.014028211124241352, -0.007010582368820906, 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790 | 790 | ['Matthew Lai'] | 1509.01549v2 | This report presents Giraffe, a chess engine that uses self-play to discover
all its domain-specific knowledge, with minimal hand-crafted knowledge given by
the programmer. Unlike previous attempts using machine learning only to perform
parameter-tuning on hand-crafted evaluation functions, Giraffe's learning
system also performs automatic feature extraction and pattern recognition. The
trained evaluation function performs comparably to the evaluation functions of
state-of-the-art chess engines - all of which containing thousands of lines of
carefully hand-crafted pattern recognizers, tuned over many years by both
computer chess experts and human chess masters. Giraffe is the most successful
attempt thus far at using end-to-end machine learning to play chess. | Giraffe: Using Deep Reinforcement Learning to Play Chess | 2,015 | http://arxiv.org/pdf/1509.01549v2 | Title Giraffe Using Deep Reinforcement Learning Play Chess Summary report present Giraffe chess engine us selfplay discover domainspecific knowledge minimal handcrafted knowledge given programmer Unlike previous attempt using machine learning perform parametertuning handcrafted evaluation function Giraffes learning system also performs automatic feature extraction pattern recognition trained evaluation function performs comparably evaluation function stateoftheart chess engine containing thousand line carefully handcrafted pattern recognizers tuned many year computer chess expert human chess master Giraffe successful attempt thus far using endtoend machine learning play chess 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 | [0.005608752369880676, 0.017788715660572052, -0.03463148698210716, 0.036784302443265915, -0.0033812965266406536, -0.019422052428126335, -0.005180541425943375, 0.014505052007734776, -0.04271160066127777, -0.012004423886537552, -0.0019216561922803521, 0.0015307688154280186, -0.014542761258780956, 0.07205724716186523, 0.03482886031270027, -0.05348193272948265, 0.006424352061003447, -0.034491509199142456, 0.010445122607052326, 0.014032525941729546, -0.010540392249822617, -0.029945382848381996, -0.013372426852583885, 0.05964625999331474, -0.014513003639876842, 0.0018593471031636, -0.09800650924444199, -0.014035063795745373, -0.011370844207704067, -0.08565737307071686, 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791 | 791 | ['Kaisheng Yao', 'Geoffrey Zweig', 'Baolin Peng'] | 1510.08565v3 | In a conversation or a dialogue process, attention and intention play
intrinsic roles. This paper proposes a neural network based approach that
models the attention and intention processes. It essentially consists of three
recurrent networks. The encoder network is a word-level model representing
source side sentences. The intention network is a recurrent network that models
the dynamics of the intention process. The decoder network is a recurrent
network produces responses to the input from the source side. It is a language
model that is dependent on the intention and has an attention mechanism to
attend to particular source side words, when predicting a symbol in the
response. The model is trained end-to-end without labeling data. Experiments
show that this model generates natural responses to user inputs. | Attention with Intention for a Neural Network Conversation Model | 2,015 | http://arxiv.org/pdf/1510.08565v3 | Title Attention Intention Neural Network Conversation Model Summary conversation dialogue process attention intention play intrinsic role paper proposes neural network based approach model attention intention process essentially consists three recurrent network encoder network wordlevel model representing source side sentence intention network recurrent network model dynamic intention process decoder network recurrent network produce response input source side language model dependent intention attention mechanism attend particular source side word predicting symbol response model trained endtoend without labeling data Experiments show model generates natural response user input 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 | [0.07418721914291382, 0.028101658448576927, -0.016625864431262016, 0.03261515498161316, -0.005677464418113232, -0.022973161190748215, -0.031870037317276, -0.021354587748646736, -0.006044243928045034, -0.048517849296331406, -0.03193512558937073, 0.004044013097882271, 0.012984806671738625, 0.08076771348714828, 0.0012300065718591213, -0.04585843160748482, 0.011810719966888428, 0.04939262941479683, 0.03641069307923317, 0.05152434855699539, 0.017739146947860718, -0.002552787307649851, -0.0029129867907613516, 0.04987426847219467, 0.004434644244611263, -0.020148208364844322, -0.045057691633701324, 0.012311171740293503, -0.02699943259358406, -0.016066357493400574, 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792 | 792 | ['Matthew Hausknecht', 'Peter Stone'] | 1511.04143v4 | Recent work has shown that deep neural networks are capable of approximating
both value functions and policies in reinforcement learning domains featuring
continuous state and action spaces. However, to the best of our knowledge no
previous work has succeeded at using deep neural networks in structured
(parameterized) continuous action spaces. To fill this gap, this paper focuses
on learning within the domain of simulated RoboCup soccer, which features a
small set of discrete action types, each of which is parameterized with
continuous variables. The best learned agent can score goals more reliably than
the 2012 RoboCup champion agent. As such, this paper represents a successful
extension of deep reinforcement learning to the class of parameterized action
space MDPs. | Deep Reinforcement Learning in Parameterized Action Space | 2,015 | http://arxiv.org/pdf/1511.04143v4 | Title Deep Reinforcement Learning Parameterized Action Space Summary Recent work shown deep neural network capable approximating value function policy reinforcement learning domain featuring continuous state action space However best knowledge previous work succeeded using deep neural network structured parameterized continuous action space fill gap paper focus learning within domain simulated RoboCup soccer feature small set discrete action type parameterized continuous variable best learned agent score goal reliably 2012 RoboCup champion agent paper represents successful extension deep reinforcement learning class parameterized action space MDPs 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 | [-0.012675650417804718, 0.007990937680006027, -0.026146497577428818, -0.019109152257442474, 0.046831220388412476, -0.029667936265468597, 0.0076109240762889385, -0.06154318153858185, -0.06295673549175262, 0.019057054072618484, -0.030551210045814514, 0.007014790084213018, -0.048812828958034515, 0.08638209104537964, 0.028054503723978996, -0.08202274143695831, 0.03467436879873276, -0.039838921278715134, 0.02028568461537361, -0.012499618344008923, 0.03060167282819748, -0.07512596994638443, -0.028337478637695312, 0.06968136131763458, 0.003177777398377657, -0.015250744298100471, -0.05934276059269905, -0.04135648533701897, 0.04521014913916588, -0.037402376532554626, 0.07921021431684494, -0.029866591095924377, 0.07265022397041321, 0.02629004791378975, 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793 | 793 | ['Sainbayar Sukhbaatar', 'Arthur Szlam', 'Gabriel Synnaeve', 'Soumith Chintala', 'Rob Fergus'] | 1511.07401v2 | This paper introduces MazeBase: an environment for simple 2D games, designed
as a sandbox for machine learning approaches to reasoning and planning. Within
it, we create 10 simple games embodying a range of algorithmic tasks (e.g.
if-then statements or set negation). A variety of neural models (fully
connected, convolutional network, memory network) are deployed via
reinforcement learning on these games, with and without a procedurally
generated curriculum. Despite the tasks' simplicity, the performance of the
models is far from optimal, suggesting directions for future development. We
also demonstrate the versatility of MazeBase by using it to emulate small
combat scenarios from StarCraft. Models trained on the MazeBase version can be
directly applied to StarCraft, where they consistently beat the in-game AI. | MazeBase: A Sandbox for Learning from Games | 2,015 | http://arxiv.org/pdf/1511.07401v2 | Title MazeBase Sandbox Learning Games Summary paper introduces MazeBase environment simple 2D game designed sandbox machine learning approach reasoning planning Within create 10 simple game embodying range algorithmic task eg ifthen statement set negation variety neural model fully connected convolutional network memory network deployed via reinforcement learning game without procedurally generated curriculum Despite task simplicity performance model far optimal suggesting direction future development also demonstrate versatility MazeBase using emulate small combat scenario StarCraft Models trained MazeBase version directly applied StarCraft consistently beat ingame AI 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 | [0.01864539086818695, -0.02072683721780777, -0.03355177491903305, -0.014497590251266956, -0.017775719985365868, -0.008028334937989712, -0.004133883398026228, -0.010758710093796253, 0.022005965933203697, 0.02145436592400074, -0.01544943731278181, 0.06511269509792328, -0.057799044996500015, 0.05978481471538544, 0.018539389595389366, -0.006673093885183334, 0.022238120436668396, -0.05177796259522438, -0.03601301088929176, 0.0267346128821373, -0.028094301000237465, -0.03426443785429001, -0.04856196790933609, 0.017864761874079704, 0.0016071256250143051, -0.02187705598771572, -0.07545392960309982, 0.01508373860269785, 0.02258448675274849, -0.12286355346441269, 0.05515597388148308, 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794 | 794 | ['Juergen Schmidhuber'] | 1511.09249v1 | This paper addresses the general problem of reinforcement learning (RL) in
partially observable environments. In 2013, our large RL recurrent neural
networks (RNNs) learned from scratch to drive simulated cars from
high-dimensional video input. However, real brains are more powerful in many
ways. In particular, they learn a predictive model of their initially unknown
environment, and somehow use it for abstract (e.g., hierarchical) planning and
reasoning. Guided by algorithmic information theory, we describe RNN-based AIs
(RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending
sequences of tasks, some of them provided by the user, others invented by the
RNNAI itself in a curious, playful fashion, to improve its RNN-based world
model. Unlike our previous model-building RNN-based RL machines dating back to
1990, the RNNAI learns to actively query its model for abstract reasoning and
planning and decision making, essentially "learning to think." The basic ideas
of this report can be applied to many other cases where one RNN-like system
exploits the algorithmic information content of another. They are taken from a
grant proposal submitted in Fall 2014, and also explain concepts such as
"mirror neurons." Experimental results will be described in separate papers. | On Learning to Think: Algorithmic Information Theory for Novel
Combinations of Reinforcement Learning Controllers and Recurrent Neural World
Models | 2,015 | http://arxiv.org/pdf/1511.09249v1 | Title Learning Think Algorithmic Information Theory Novel Combinations Reinforcement Learning Controllers Recurrent Neural World Models Summary paper address general problem reinforcement learning RL partially observable environment 2013 large RL recurrent neural network RNNs learned scratch drive simulated car highdimensional video input However real brain powerful many way particular learn predictive model initially unknown environment somehow use abstract eg hierarchical planning reasoning Guided algorithmic information theory describe RNNbased AIs RNNAIs designed RNNAI trained neverending sequence task provided user others invented RNNAI curious playful fashion improve RNNbased world model Unlike previous modelbuilding RNNbased RL machine dating back 1990 RNNAI learns actively query model abstract reasoning planning decision making essentially learning think basic idea report applied many case one RNNlike system exploit algorithmic information content another taken grant proposal submitted Fall 2014 also explain concept mirror neuron Experimental result described separate paper 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 | [0.040364764630794525, 0.039489295333623886, -0.017605502158403397, 0.01730210706591606, -0.053249262273311615, -0.0025828455109149218, -0.0014882119139656425, -0.010194088332355022, -0.05190899223089218, -0.013389719650149345, -0.0011533999349921942, 0.008059129118919373, -0.007386452052742243, 0.0474996343255043, 0.04171926900744438, -0.07075284421443939, 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795 | 795 | ['Denis Steckelmacher', 'Peter Vrancx'] | 1512.05509v1 | This paper explores the performance of fitted neural Q iteration for
reinforcement learning in several partially observable environments, using
three recurrent neural network architectures: Long Short-Term Memory, Gated
Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of
several thousands candidate architectures. A variant of fitted Q iteration,
based on Advantage values instead of Q values, is also explored. The results
show that GRU performs significantly better than LSTM and MUT1 for most of the
problems considered, requiring less training episodes and less CPU time before
learning a very good policy. Advantage learning also tends to produce better
results. | An Empirical Comparison of Neural Architectures for Reinforcement
Learning in Partially Observable Environments | 2,015 | http://arxiv.org/pdf/1512.05509v1 | Title Empirical Comparison Neural Architectures Reinforcement Learning Partially Observable Environments Summary paper explores performance fitted neural Q iteration reinforcement learning several partially observable environment using three recurrent neural network architecture Long ShortTerm Memory Gated Recurrent Unit MUT1 recurrent neural architecture evolved pool several thousand candidate architecture variant fitted Q iteration based Advantage value instead Q value also explored result show GRU performs significantly better LSTM MUT1 problem considered requiring le training episode le CPU time learning good policy Advantage learning also tends produce better result 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 | [0.031508006155490875, 0.000662361504510045, -0.008813418447971344, -0.013668354600667953, -0.0028510328847914934, -0.019623108208179474, 0.0009291676105931401, -0.005711337551474571, -0.025212733075022697, 0.019281769171357155, -0.040371086448431015, -0.012081305496394634, -0.04073917865753174, 0.053953949362039566, 0.016616396605968475, -0.03382929787039757, 0.004361906088888645, -0.02232423797249794, -0.00903975497931242, -0.01167701743543148, -0.00588420033454895, -0.08948969095945358, -0.0008679064339958131, 0.014787020161747932, 0.031400736421346664, 0.03443542867898941, -0.03612884134054184, -0.057310160249471664, 0.02172914519906044, -0.10278860479593277, 0.033154938369989395, -0.04994381591677666, 0.04759949818253517, 0.07789114862680435, 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796 | 796 | ['Cristóbal Esteban', 'Oliver Staeck', 'Yinchong Yang', 'Volker Tresp'] | 1602.02685v2 | In clinical data sets we often find static information (e.g. patient gender,
blood type, etc.) combined with sequences of data that are recorded during
multiple hospital visits (e.g. medications prescribed, tests performed, etc.).
Recurrent Neural Networks (RNNs) have proven to be very successful for
modelling sequences of data in many areas of Machine Learning. In this work we
present an approach based on RNNs, specifically designed for the clinical
domain, that combines static and dynamic information in order to predict future
events. We work with a database collected in the Charit\'{e} Hospital in Berlin
that contains complete information concerning patients that underwent a kidney
transplantation. After the transplantation three main endpoints can occur:
rejection of the kidney, loss of the kidney and death of the patient. Our goal
is to predict, based on information recorded in the Electronic Health Record of
each patient, whether any of those endpoints will occur within the next six or
twelve months after each visit to the clinic. We compared different types of
RNNs that we developed for this work, with a model based on a Feedforward
Neural Network and a Logistic Regression model. We found that the RNN that we
developed based on Gated Recurrent Units provides the best performance for this
task. We also used the same models for a second task, i.e., next event
prediction, and found that here the model based on a Feedforward Neural Network
outperformed the other models. Our hypothesis is that long-term dependencies
are not as relevant in this task. | Predicting Clinical Events by Combining Static and Dynamic Information
Using Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1602.02685v2 | Title Predicting Clinical Events Combining Static Dynamic Information Using Recurrent Neural Networks Summary clinical data set often find static information eg patient gender blood type etc combined sequence data recorded multiple hospital visit eg medication prescribed test performed etc Recurrent Neural Networks RNNs proven successful modelling sequence data many area Machine Learning work present approach based RNNs specifically designed clinical domain combine static dynamic information order predict future event work database collected Charite Hospital Berlin contains complete information concerning patient underwent kidney transplantation transplantation three main endpoint occur rejection kidney loss kidney death patient goal predict based information recorded Electronic Health Record patient whether endpoint occur within next six twelve month visit clinic compared different type RNNs developed work model based Feedforward Neural Network Logistic Regression model found RNN developed based Gated Recurrent Units provides best performance task also used model second task ie next event prediction found model based Feedforward Neural Network outperformed model hypothesis longterm dependency relevant 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 | [0.013261804357171059, 0.04503307119011879, -0.008894164115190506, -0.035063933581113815, -0.015218491666018963, 0.01147483941167593, -0.010656564496457577, 0.016265563666820526, -0.028623173013329506, 0.0058285086415708065, 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797 | 797 | ['Tim Salimans', 'Diederik P. Kingma'] | 1602.07868v3 | We present weight normalization: a reparameterization of the weight vectors
in a neural network that decouples the length of those weight vectors from
their direction. By reparameterizing the weights in this way we improve the
conditioning of the optimization problem and we speed up convergence of
stochastic gradient descent. Our reparameterization is inspired by batch
normalization but does not introduce any dependencies between the examples in a
minibatch. This means that our method can also be applied successfully to
recurrent models such as LSTMs and to noise-sensitive applications such as deep
reinforcement learning or generative models, for which batch normalization is
less well suited. Although our method is much simpler, it still provides much
of the speed-up of full batch normalization. In addition, the computational
overhead of our method is lower, permitting more optimization steps to be taken
in the same amount of time. We demonstrate the usefulness of our method on
applications in supervised image recognition, generative modelling, and deep
reinforcement learning. | Weight Normalization: A Simple Reparameterization to Accelerate Training
of Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1602.07868v3 | Title Weight Normalization Simple Reparameterization Accelerate Training Deep Neural Networks Summary present weight normalization reparameterization weight vector neural network decouples length weight vector direction reparameterizing weight way improve conditioning optimization problem speed convergence stochastic gradient descent reparameterization inspired batch normalization introduce dependency example minibatch mean method also applied successfully recurrent model LSTMs noisesensitive application deep reinforcement learning generative model batch normalization le well suited Although method much simpler still provides much speedup full batch normalization addition computational overhead method lower permitting optimization step taken amount time demonstrate usefulness method application supervised image recognition generative modelling deep reinforcement 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 | [-0.020008239895105362, 0.054349448531866074, -0.012255227193236351, 0.04392442852258682, 0.02404322847723961, -0.004250003956258297, 0.035763900727033615, -0.007881603203713894, -0.043586261570453644, 0.043948639184236526, -0.025443583726882935, 0.025280063971877098, 0.0058187455870211124, 0.03038506582379341, -0.00041417303145863116, -0.09649214148521423, -0.020018907263875008, 0.0008712346316315234, -0.03253829851746559, -0.024077806621789932, 0.0028251809999346733, -0.0019087438704445958, 0.021524697542190552, 0.05256083235144615, -0.04041428491473198, 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798 | 798 | ['Felix Leibfried', 'Daniel Alexander Braun'] | 1602.08332v2 | Bounded rational decision-makers transform sensory input into motor output
under limited computational resources. Mathematically, such decision-makers can
be modeled as information-theoretic channels with limited transmission rate.
Here, we apply this formalism for the first time to multilayer feedforward
neural networks. We derive synaptic weight update rules for two scenarios,
where either each neuron is considered as a bounded rational decision-maker or
the network as a whole. In the update rules, bounded rationality translates
into information-theoretically motivated types of regularization in weight
space. In experiments on the MNIST benchmark classification task for
handwritten digits, we show that such information-theoretic regularization
successfully prevents overfitting across different architectures and attains
results that are competitive with other recent techniques like dropout,
dropconnect and Bayes by backprop, for both ordinary and convolutional neural
networks. | Bounded Rational Decision-Making in Feedforward Neural Networks | 2,016 | http://arxiv.org/pdf/1602.08332v2 | Title Bounded Rational DecisionMaking Feedforward Neural Networks Summary Bounded rational decisionmakers transform sensory input motor output limited computational resource Mathematically decisionmakers modeled informationtheoretic channel limited transmission rate apply formalism first time multilayer feedforward neural network derive synaptic weight update rule two scenario either neuron considered bounded rational decisionmaker network whole update rule bounded rationality translates informationtheoretically motivated type regularization weight space experiment MNIST benchmark classification task handwritten digit show informationtheoretic regularization successfully prevents overfitting across different architecture attains result competitive recent technique like dropout dropconnect Bayes backprop ordinary convolutional neural 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 | [-0.014995863661170006, 0.05422063544392586, -0.01628904789686203, -0.010860799811780453, 0.011774145998060703, -0.008793439716100693, 0.008602979592978954, 0.04339129850268364, 5.646177669405006e-05, 0.008354364894330502, -0.03066168911755085, 0.03283068910241127, 0.016480116173624992, 0.06421598047018051, 0.051957350224256516, 0.0034342051949352026, 0.022322263568639755, 0.010791029781103134, 0.016468653455376625, -0.03836657851934433, -0.01996166631579399, -0.05147719755768776, -0.0006706560379825532, 0.04738183692097664, 0.020462462678551674, 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799 | 799 | ['Greg Yang'] | 1602.08671v3 | Following the recent trend in explicit neural memory structures, we present a
new design of an external memory, wherein memories are stored in an Euclidean
key space $\mathbb R^n$. An LSTM controller performs read and write via
specialized read and write heads. It can move a head by either providing a new
address in the key space (aka random access) or moving from its previous
position via a Lie group action (aka Lie access). In this way, the "L" and "R"
instructions of a traditional Turing Machine are generalized to arbitrary
elements of a fixed Lie group action. For this reason, we name this new model
the Lie Access Neural Turing Machine, or LANTM.
We tested two different configurations of LANTM against an LSTM baseline in
several basic experiments. We found the right configuration of LANTM to
outperform the baseline in all of our experiments. In particular, we trained
LANTM on addition of $k$-digit numbers for $2 \le k \le 16$, but it was able to
generalize almost perfectly to $17 \le k \le 32$, all with the number of
parameters 2 orders of magnitude below the LSTM baseline. | Lie Access Neural Turing Machine | 2,016 | http://arxiv.org/pdf/1602.08671v3 | Title Lie Access Neural Turing Machine Summary Following recent trend explicit neural memory structure present new design external memory wherein memory stored Euclidean key space mathbb Rn LSTM controller performs read write via specialized read write head move head either providing new address key space aka random access moving previous position via Lie group action aka Lie access way L R instruction traditional Turing Machine generalized arbitrary element fixed Lie group action reason name new model Lie Access Neural Turing Machine LANTM tested two different configuration LANTM LSTM baseline several basic experiment found right configuration LANTM outperform baseline experiment particular trained LANTM addition kdigit number 2 le k le 16 able generalize almost perfectly 17 le k le 32 number parameter 2 order magnitude LSTM baseline 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 | [0.023067500442266464, -0.03536568954586983, -0.0033056761603802443, 0.035869795829057693, -0.009078841656446457, -0.01579129882156849, 0.026542559266090393, -0.016675584018230438, 0.023853767663240433, -0.026093723252415657, -0.04919576272368431, -0.05345131456851959, -0.006935525219887495, 0.04885188490152359, 0.05833158269524574, -0.028270315378904343, 0.04939347878098488, 0.016789443790912628, -0.02306159771978855, -0.013491655699908733, -0.03430594131350517, -0.0398147851228714, -0.012418467551469803, -0.03706774115562439, 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Subsets and Splits