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900 | 900 | ['Wei Wen', 'Chunpeng Wu', 'Yandan Wang', 'Yiran Chen', 'Hai Li'] | 1608.03665v4 | High demand for computation resources severely hinders deployment of
large-scale Deep Neural Networks (DNN) in resource constrained devices. In this
work, we propose a Structured Sparsity Learning (SSL) method to regularize the
structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs.
SSL can: (1) learn a compact structure from a bigger DNN to reduce computation
cost; (2) obtain a hardware-friendly structured sparsity of DNN to efficiently
accelerate the DNNs evaluation. Experimental results show that SSL achieves on
average 5.1x and 3.1x speedups of convolutional layer computation of AlexNet
against CPU and GPU, respectively, with off-the-shelf libraries. These speedups
are about twice speedups of non-structured sparsity; (3) regularize the DNN
structure to improve classification accuracy. The results show that for
CIFAR-10, regularization on layer depth can reduce 20 layers of a Deep Residual
Network (ResNet) to 18 layers while improve the accuracy from 91.25% to 92.60%,
which is still slightly higher than that of original ResNet with 32 layers. For
AlexNet, structure regularization by SSL also reduces the error by around ~1%.
Open source code is in https://github.com/wenwei202/caffe/tree/scnn | Learning Structured Sparsity in Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1608.03665v4 | Title Learning Structured Sparsity Deep Neural Networks Summary High demand computation resource severely hinders deployment largescale Deep Neural Networks DNN resource constrained device work propose Structured Sparsity Learning SSL method regularize structure ie filter channel filter shape layer depth DNNs SSL 1 learn compact structure bigger DNN reduce computation cost 2 obtain hardwarefriendly structured sparsity DNN efficiently accelerate DNNs evaluation Experimental result show SSL achieves average 51x 31x speedup convolutional layer computation AlexNet CPU GPU respectively offtheshelf library speedup twice speedup nonstructured sparsity 3 regularize DNN structure improve classification accuracy result show CIFAR10 regularization layer depth reduce 20 layer Deep Residual Network ResNet 18 layer improve accuracy 9125 9260 still slightly higher original ResNet 32 layer AlexNet structure regularization SSL also reduces error around 1 Open source code httpsgithubcomwenwei202caffetreescnn 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 | [-0.01659630611538887, 0.0748564675450325, -0.005367153324186802, 0.04604210704565048, 0.019997602328658104, -0.05583498626947403, 0.05846254155039787, 0.003320320276543498, -0.024876467883586884, 0.029903799295425415, 0.008502352982759476, 0.019730141386389732, 0.01199910044670105, 0.04032714664936066, -0.01680508814752102, -0.03992731496691704, 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901 | 901 | ['Itay Safran', 'Ohad Shamir'] | 1610.09887v2 | We provide several new depth-based separation results for feed-forward neural
networks, proving that various types of simple and natural functions can be
better approximated using deeper networks than shallower ones, even if the
shallower networks are much larger. This includes indicators of balls and
ellipses; non-linear functions which are radial with respect to the $L_1$ norm;
and smooth non-linear functions. We also show that these gaps can be observed
experimentally: Increasing the depth indeed allows better learning than
increasing width, when training neural networks to learn an indicator of a unit
ball. | Depth-Width Tradeoffs in Approximating Natural Functions with Neural
Networks | 2,016 | http://arxiv.org/pdf/1610.09887v2 | Title DepthWidth Tradeoffs Approximating Natural Functions Neural Networks Summary provide several new depthbased separation result feedforward neural network proving various type simple natural function better approximated using deeper network shallower one even shallower network much larger includes indicator ball ellipsis nonlinear function radial respect L1 norm smooth nonlinear function also show gap observed experimentally Increasing depth indeed allows better learning increasing width training neural network learn indicator unit ball 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 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902 | 902 | ['Chuan-Yung Tsai', 'Andrew Saxe', 'David Cox'] | 1610.10087v1 | We present a novel neural network algorithm, the Tensor Switching (TS)
network, which generalizes the Rectified Linear Unit (ReLU) nonlinearity to
tensor-valued hidden units. The TS network copies its entire input vector to
different locations in an expanded representation, with the location determined
by its hidden unit activity. In this way, even a simple linear readout from the
TS representation can implement a highly expressive deep-network-like function.
The TS network hence avoids the vanishing gradient problem by construction, at
the cost of larger representation size. We develop several methods to train the
TS network, including equivalent kernels for infinitely wide and deep TS
networks, a one-pass linear learning algorithm, and two
backpropagation-inspired representation learning algorithms. Our experimental
results demonstrate that the TS network is indeed more expressive and
consistently learns faster than standard ReLU networks. | Tensor Switching Networks | 2,016 | http://arxiv.org/pdf/1610.10087v1 | Title Tensor Switching Networks Summary present novel neural network algorithm Tensor Switching TS network generalizes Rectified Linear Unit ReLU nonlinearity tensorvalued hidden unit TS network copy entire input vector different location expanded representation location determined hidden unit activity way even simple linear readout TS representation implement highly expressive deepnetworklike function TS network hence avoids vanishing gradient problem construction cost larger representation size develop several method train TS network including equivalent kernel infinitely wide deep TS network onepass linear learning algorithm two backpropagationinspired representation learning algorithm experimental result demonstrate TS network indeed expressive consistently learns faster standard ReLU 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 | [-0.038573481142520905, -0.017879076302051544, -0.022742094472050667, 0.04812457785010338, -0.006141091696918011, 0.012086226604878902, 0.06394513696432114, 0.01881677284836769, -0.007223112974315882, -0.029840851202607155, -0.024261638522148132, 0.0003172199649270624, -0.013522958382964134, 0.04881155118346214, 0.008892828598618507, -0.025281991809606552, -0.0019080990459769964, 0.01199586596339941, 0.010592011734843254, -0.014808635227382183, -0.00080184789840132, -0.02775203250348568, -0.017552506178617477, -0.0187941025942564, -0.03109869919717312, 0.01329815573990345, 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903 | 903 | ['Maithra Raghu', 'Ben Poole', 'Jon Kleinberg', 'Surya Ganguli', 'Jascha Sohl-Dickstein'] | 1611.08083v1 | We survey results on neural network expressivity described in "On the
Expressive Power of Deep Neural Networks". The paper motivates and develops
three natural measures of expressiveness, which all display an exponential
dependence on the depth of the network. In fact, all of these measures are
related to a fourth quantity, trajectory length. This quantity grows
exponentially in the depth of the network, and is responsible for the depth
sensitivity observed. These results translate to consequences for networks
during and after training. They suggest that parameters earlier in a network
have greater influence on its expressive power -- in particular, given a layer,
its influence on expressivity is determined by the remaining depth of the
network after that layer. This is verified with experiments on MNIST and
CIFAR-10. We also explore the effect of training on the input-output map, and
find that it trades off between the stability and expressivity. | Survey of Expressivity in Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1611.08083v1 | Title Survey Expressivity Deep Neural Networks Summary survey result neural network expressivity described Expressive Power Deep Neural Networks paper motivates develops three natural measure expressiveness display exponential dependence depth network fact measure related fourth quantity trajectory length quantity grows exponentially depth network responsible depth sensitivity observed result translate consequence network training suggest parameter earlier network greater influence expressive power particular given layer influence expressivity determined remaining depth network layer verified experiment MNIST CIFAR10 also explore effect training inputoutput map find trade stability expressivity 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 | [-0.040803201496601105, 0.0421065017580986, -0.038796842098236084, 0.017388811334967613, -0.01189966220408678, -0.061782948672771454, 0.018624713644385338, 0.013946075923740864, -0.07145845144987106, 0.03328273817896843, 0.00952074769884348, 0.008740254677832127, 0.01378615852445364, 0.08721750974655151, 0.02509308233857155, -0.055375438183546066, 0.02963067591190338, 0.011296109296381474, -0.03609783202409744, -0.002497459761798382, -0.003192686475813389, -0.051345668733119965, 0.016027826815843582, 0.04902287945151329, -0.0554627999663353, 0.004778401926159859, -0.015324248000979424, -0.019496090710163116, 0.023920871317386627, -0.04098639637231827, 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904 | 904 | ['Zachary C. Lipton', 'Subarna Tripathi'] | 1702.04782v2 | Generative adversarial networks (GANs) transform latent vectors into visually
plausible images. It is generally thought that the original GAN formulation
gives no out-of-the-box method to reverse the mapping, projecting images back
into latent space. We introduce a simple, gradient-based technique called
stochastic clipping. In experiments, for images generated by the GAN, we
precisely recover their latent vector pre-images 100% of the time. Additional
experiments demonstrate that this method is robust to noise. Finally, we show
that even for unseen images, our method appears to recover unique encodings. | Precise Recovery of Latent Vectors from Generative Adversarial Networks | 2,017 | http://arxiv.org/pdf/1702.04782v2 | Title Precise Recovery Latent Vectors Generative Adversarial Networks Summary Generative adversarial network GANs transform latent vector visually plausible image generally thought original GAN formulation give outofthebox method reverse mapping projecting image back latent space introduce simple gradientbased technique called stochastic clipping experiment image generated GAN precisely recover latent vector preimages 100 time Additional experiment demonstrate method robust noise Finally show even unseen image method appears recover unique encoding 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 | [0.026400696486234665, 0.07534674555063248, -0.01070816908031702, 0.05447891727089882, -0.026032110676169395, -0.03902760148048401, -0.007082941010594368, 0.019612129777669907, -0.09977174550294876, 0.03497481718659401, 0.01035909540951252, 0.014261354692280293, -0.007608470041304827, 0.02977430634200573, 0.07887084782123566, -0.09362761676311493, -0.021066060289740562, 0.03089197352528572, -0.04472113773226738, -0.04326751455664635, 0.0160140972584486, -0.019055208191275597, 0.0010448492830619216, 0.03513049706816673, -0.06450794637203217, -0.007206528447568417, -0.0001912178995553404, -0.0010388628579676151, 0.02795008011162281, -0.009311086498200893, 0.017948998138308525, -0.008568471297621727, 0.0021595514845103025, -0.007820543833076954, 2.560815573815489e-06, 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905 | 905 | ['Nathan Ng', 'Rodney A Gabriel', 'Julian McAuley', 'Charles Elkan', 'Zachary C Lipton'] | 1702.05386v3 | Scheduling surgeries is a challenging task due to the fundamental uncertainty
of the clinical environment, as well as the risks and costs associated with
under- and over-booking. We investigate neural regression algorithms to
estimate the parameters of surgery case durations, focusing on the issue of
heteroscedasticity. We seek to simultaneously estimate the duration of each
surgery, as well as a surgery-specific notion of our uncertainty about its
duration. Estimating this uncertainty can lead to more nuanced and effective
scheduling strategies, as we are able to schedule surgeries more efficiently
while allowing an informed and case-specific margin of error. Using surgery
records %from the UC San Diego Health System, from a large United States health
system we demonstrate potential improvements on the order of 20% (in terms of
minutes overbooked) compared to current scheduling techniques. Moreover, we
demonstrate that surgery durations are indeed heteroscedastic. We show that
models that estimate case-specific uncertainty better fit the data (log
likelihood). Additionally, we show that the heteroscedastic predictions can
more optimally trade off between over and under-booking minutes, especially
when idle minutes and scheduling collisions confer disparate costs. | Predicting Surgery Duration with Neural Heteroscedastic Regression | 2,017 | http://arxiv.org/pdf/1702.05386v3 | Title Predicting Surgery Duration Neural Heteroscedastic Regression Summary Scheduling surgery challenging task due fundamental uncertainty clinical environment well risk cost associated overbooking investigate neural regression algorithm estimate parameter surgery case duration focusing issue heteroscedasticity seek simultaneously estimate duration surgery well surgeryspecific notion uncertainty duration Estimating uncertainty lead nuanced effective scheduling strategy able schedule surgery efficiently allowing informed casespecific margin error Using surgery record UC San Diego Health System large United States health system demonstrate potential improvement order 20 term minute overbooked compared current scheduling technique Moreover demonstrate surgery duration indeed heteroscedastic show model estimate casespecific uncertainty better fit data log likelihood Additionally show heteroscedastic prediction optimally trade underbooking minute especially idle minute scheduling collision confer disparate 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 | [0.0035067747812718153, 0.05280238389968872, -0.05167202278971672, -0.06231819838285446, 0.026894744485616684, 0.014329604804515839, 0.04932184889912605, 0.020540937781333923, -0.057872798293828964, 0.02975635416805744, 0.020651469007134438, -0.047121625393629074, 0.024571049958467484, 0.08178380876779556, 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906 | 906 | ['Haihao Lu', 'Kenji Kawaguchi'] | 1702.08580v2 | In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create
non-convex loss surfaces. Then, does depth alone create bad local minima? In
this paper, we prove that without nonlinearity, depth alone does not create bad
local minima, although it induces non-convex loss surface. Using this insight,
we greatly simplify a recently proposed proof to show that all of the local
minima of feedforward deep linear neural networks are global minima. Our
theoretical results generalize previous results with fewer assumptions, and
this analysis provides a method to show similar results beyond square loss in
deep linear models. | Depth Creates No Bad Local Minima | 2,017 | http://arxiv.org/pdf/1702.08580v2 | Title Depth Creates Bad Local Minima Summary deep learning textitdepth well textitnonlinearity create nonconvex loss surface depth alone create bad local minimum paper prove without nonlinearity depth alone create bad local minimum although induces nonconvex loss surface Using insight greatly simplify recently proposed proof show local minimum feedforward deep linear neural network global minimum theoretical result generalize previous result fewer assumption analysis provides method show similar result beyond square loss deep linear 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 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907 | 907 | ['Kenji Kawaguchi', 'Bo Xie', 'Vikas Verma', 'Le Song'] | 1702.08882v7 | We propose semi-random features for nonlinear function approximation. The
flexibility of semi-random feature lies between the fully adjustable units in
deep learning and the random features used in kernel methods. For one hidden
layer models with semi-random features, we prove with no unrealistic
assumptions that the model classes contain an arbitrarily good function as the
width increases (universality), and despite non-convexity, we can find such a
good function (optimization theory) that generalizes to unseen new data
(generalization bound). For deep models, with no unrealistic assumptions, we
prove universal approximation ability, a lower bound on approximation error, a
partial optimization guarantee, and a generalization bound. Depending on the
problems, the generalization bound of deep semi-random features can be
exponentially better than the known bounds of deep ReLU nets; our
generalization error bound can be independent of the depth, the number of
trainable weights as well as the input dimensionality. In experiments, we show
that semi-random features can match the performance of neural networks by using
slightly more units, and it outperforms random features by using significantly
fewer units. Moreover, we introduce a new implicit ensemble method by using
semi-random features. | Deep Semi-Random Features for Nonlinear Function Approximation | 2,017 | http://arxiv.org/pdf/1702.08882v7 | Title Deep SemiRandom Features Nonlinear Function Approximation Summary propose semirandom feature nonlinear function approximation flexibility semirandom feature lie fully adjustable unit deep learning random feature used kernel method one hidden layer model semirandom feature prove unrealistic assumption model class contain arbitrarily good function width increase universality despite nonconvexity find good function optimization theory generalizes unseen new data generalization bound deep model unrealistic assumption prove universal approximation ability lower bound approximation error partial optimization guarantee generalization bound Depending problem generalization bound deep semirandom feature exponentially better known bound deep ReLU net generalization error bound independent depth number trainable weight well input dimensionality experiment show semirandom feature match performance neural network using slightly unit outperforms random feature using significantly fewer unit Moreover introduce new implicit ensemble method using semirandom feature 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 | [-0.03810715675354004, 0.042253658175468445, -0.016475047916173935, 0.03775469586253166, 0.02621050551533699, -0.038027409464120865, 0.054141078144311905, -0.0273752398788929, -0.04127400740981102, 0.000567191222216934, -0.010257388465106487, -0.03006933629512787, -0.03344865143299103, 0.07191070169210434, 0.01838579773902893, 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908 | 908 | ['Pietro Morerio', 'Jacopo Cavazza', 'Riccardo Volpi', 'Rene Vidal', 'Vittorio Murino'] | 1703.06229v2 | Dropout is a very effective way of regularizing neural networks.
Stochastically "dropping out" units with a certain probability discourages
over-specific co-adaptations of feature detectors, preventing overfitting and
improving network generalization. Besides, Dropout can be interpreted as an
approximate model aggregation technique, where an exponential number of smaller
networks are averaged in order to get a more powerful ensemble. In this paper,
we show that using a fixed dropout probability during training is a suboptimal
choice. We thus propose a time scheduling for the probability of retaining
neurons in the network. This induces an adaptive regularization scheme that
smoothly increases the difficulty of the optimization problem. This idea of
"starting easy" and adaptively increasing the difficulty of the learning
problem has its roots in curriculum learning and allows one to train better
models. Indeed, we prove that our optimization strategy implements a very
general curriculum scheme, by gradually adding noise to both the input and
intermediate feature representations within the network architecture.
Experiments on seven image classification datasets and different network
architectures show that our method, named Curriculum Dropout, frequently yields
to better generalization and, at worst, performs just as well as the standard
Dropout method. | Curriculum Dropout | 2,017 | http://arxiv.org/pdf/1703.06229v2 | Title Curriculum Dropout Summary Dropout effective way regularizing neural network Stochastically dropping unit certain probability discourages overspecific coadaptations feature detector preventing overfitting improving network generalization Besides Dropout interpreted approximate model aggregation technique exponential number smaller network averaged order get powerful ensemble paper show using fixed dropout probability training suboptimal choice thus propose time scheduling probability retaining neuron network induces adaptive regularization scheme smoothly increase difficulty optimization problem idea starting easy adaptively increasing difficulty learning problem root curriculum learning allows one train better model Indeed prove optimization strategy implement general curriculum scheme gradually adding noise input intermediate feature representation within network architecture Experiments seven image classification datasets different network architecture show method named Curriculum Dropout frequently yield better generalization worst performs well standard Dropout 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 | [0.021196754649281502, 0.025057265534996986, -0.04824516177177429, 0.02435699664056301, 0.0032089315354824066, 0.01290759164839983, 0.05139574781060219, -0.03810380399227142, -0.018367275595664978, 0.004343180451542139, -0.004008772782981396, 0.051786135882139206, 0.005155432969331741, 0.04700867086648941, 0.034360967576503754, -0.07485149055719376, 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909 | 909 | ['David Rolnick', 'Max Tegmark'] | 1705.05502v1 | It is well-known that neural networks are universal approximators, but that
deeper networks tend to be much more efficient than shallow ones. We shed light
on this by proving that the total number of neurons $m$ required to approximate
natural classes of multivariate polynomials of $n$ variables grows only
linearly with $n$ for deep neural networks, but grows exponentially when merely
a single hidden layer is allowed. We also provide evidence that when the number
of hidden layers is increased from $1$ to $k$, the neuron requirement grows
exponentially not with $n$ but with $n^{1/k}$, suggesting that the minimum
number of layers required for computational tractability grows only
logarithmically with $n$. | The power of deeper networks for expressing natural functions | 2,017 | http://arxiv.org/pdf/1705.05502v1 | Title power deeper network expressing natural function Summary wellknown neural network universal approximators deeper network tend much efficient shallow one shed light proving total number neuron required approximate natural class multivariate polynomial n variable grows linearly n deep neural network grows exponentially merely single hidden layer allowed also provide evidence number hidden layer increased 1 k neuron requirement grows exponentially n n1k suggesting minimum number layer required computational tractability grows logarithmically n 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 | [-0.06329913437366486, 0.08246143907308578, -0.015012403950095177, 0.019937817007303238, -0.01387228537350893, -0.04648955538868904, 0.02122058905661106, -0.03758176788687706, -0.010652941651642323, 0.02578652650117874, 0.04564613103866577, 0.02698303945362568, -0.020929984748363495, 0.042022790759801865, 0.07929632812738419, -0.02574647217988968, -0.013168825767934322, -0.012646101415157318, -0.039347052574157715, -0.025954391807317734, -0.018668361008167267, -0.06287132948637009, 0.008054640144109726, 0.027151521295309067, -0.06764641404151917, 0.005152038764208555, 0.007999015972018242, -0.0388503260910511, 0.0004099470970686525, 0.020301904529333115, 0.015311836265027523, -0.027308328077197075, 0.0020898422226309776, 0.06779758632183075, 2.267556965307449e-06, -0.027174295857548714, 0.003702176734805107, 0.019063951447606087, 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910 | 910 | ['Dongsung Huh', 'Terrence J. Sejnowski'] | 1706.04698v2 | Much of studies on neural computation are based on network models of static
neurons that produce analog output, despite the fact that information
processing in the brain is predominantly carried out by dynamic neurons that
produce discrete pulses called spikes. Research in spike-based computation has
been impeded by the lack of efficient supervised learning algorithm for spiking
networks. Here, we present a gradient descent method for optimizing spiking
network models by introducing a differentiable formulation of spiking networks
and deriving the exact gradient calculation. For demonstration, we trained
recurrent spiking networks on two dynamic tasks: one that requires optimizing
fast (~millisecond) spike-based interactions for efficient encoding of
information, and a delayed memory XOR task over extended duration (~second).
The results show that our method indeed optimizes the spiking network dynamics
on the time scale of individual spikes as well as behavioral time scales. In
conclusion, our result offers a general purpose supervised learning algorithm
for spiking neural networks, thus advancing further investigations on
spike-based computation. | Gradient Descent for Spiking Neural Networks | 2,017 | http://arxiv.org/pdf/1706.04698v2 | Title Gradient Descent Spiking Neural Networks Summary Much study neural computation based network model static neuron produce analog output despite fact information processing brain predominantly carried dynamic neuron produce discrete pulse called spike Research spikebased computation impeded lack efficient supervised learning algorithm spiking network present gradient descent method optimizing spiking network model introducing differentiable formulation spiking network deriving exact gradient calculation demonstration trained recurrent spiking network two dynamic task one requires optimizing fast millisecond spikebased interaction efficient encoding information delayed memory XOR task extended duration second result show method indeed optimizes spiking network dynamic time scale individual spike well behavioral time scale conclusion result offer general purpose supervised learning algorithm spiking neural network thus advancing investigation spikebased computation 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 | [-0.030215846374630928, -0.061905812472105026, -0.020115753635764122, 0.03791351616382599, 0.030119411647319794, -0.001504110754467547, 0.002473750151693821, -0.004003675188869238, 0.02099120244383812, 0.017014838755130768, -0.024780504405498505, 0.03629687428474426, -0.00887641217559576, 0.061002928763628006, 0.011684088036417961, -0.022914500907063484, 0.060947198420763016, 0.03995531052350998, -0.0031483254861086607, 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911 | 911 | ['Ivo Gonçalves', 'Sara Silva', 'Carlos M. Fonseca', 'Mauro Castelli'] | 1706.06195v1 | In iterative supervised learning algorithms it is common to reach a point in
the search where no further induction seems to be possible with the available
data. If the search is continued beyond this point, the risk of overfitting
increases significantly. Following the recent developments in inductive
semantic stochastic methods, this paper studies the feasibility of using
information gathered from the semantic neighborhood to decide when to stop the
search. Two semantic stopping criteria are proposed and experimentally assessed
in Geometric Semantic Genetic Programming (GSGP) and in the Semantic Learning
Machine (SLM) algorithm (the equivalent algorithm for neural networks). The
experiments are performed on real-world high-dimensional regression datasets.
The results show that the proposed semantic stopping criteria are able to
detect stopping points that result in a competitive generalization for both
GSGP and SLM. This approach also yields computationally efficient algorithms as
it allows the evolution of neural networks in less than 3 seconds on average,
and of GP trees in at most 10 seconds. The usage of the proposed semantic
stopping criteria in conjunction with the computation of optimal
mutation/learning steps also results in small trees and neural networks. | Unsure When to Stop? Ask Your Semantic Neighbors | 2,017 | http://arxiv.org/pdf/1706.06195v1 | Title Unsure Stop Ask Semantic Neighbors Summary iterative supervised learning algorithm common reach point search induction seems possible available data search continued beyond point risk overfitting increase significantly Following recent development inductive semantic stochastic method paper study feasibility using information gathered semantic neighborhood decide stop search Two semantic stopping criterion proposed experimentally assessed Geometric Semantic Genetic Programming GSGP Semantic Learning Machine SLM algorithm equivalent algorithm neural network experiment performed realworld highdimensional regression datasets result show proposed semantic stopping criterion able detect stopping point result competitive generalization GSGP SLM approach also yield computationally efficient algorithm allows evolution neural network le 3 second average GP tree 10 second usage proposed semantic stopping criterion conjunction computation optimal mutationlearning step also result small tree 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 | [0.0018006833270192146, 0.025668496266007423, -0.01144926343113184, 0.013653518632054329, -0.029787158593535423, 0.004409995395690203, -0.01718537136912346, 0.00735758850350976, -0.022570738568902016, 0.009048466570675373, 0.04725297540426254, 0.0631190687417984, -0.00830131582915783, -0.013563445769250393, -0.0009496856946498156, -0.00901925191283226, 0.035479746758937836, 0.035321250557899475, -0.026106517761945724, -0.03972679376602173, 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912 | 912 | ['Daniel Hsu'] | 1708.02975v2 | In this paper, we use variational recurrent neural network to investigate the
anomaly detection problem on graph time series. The temporal correlation is
modeled by the combination of recurrent neural network (RNN) and variational
inference (VI), while the spatial information is captured by the graph
convolutional network. In order to incorporate external factors, we use feature
extractor to augment the transition of latent variables, which can learn the
influence of external factors. With the target function as accumulative ELBO,
it is easy to extend this model to on-line method. The experimental study on
traffic flow data shows the detection capability of the proposed method. | Anomaly Detection on Graph Time Series | 2,017 | http://arxiv.org/pdf/1708.02975v2 | Title Anomaly Detection Graph Time Series Summary paper use variational recurrent neural network investigate anomaly detection problem graph time series temporal correlation modeled combination recurrent neural network RNN variational inference VI spatial information captured graph convolutional network order incorporate external factor use feature extractor augment transition latent variable learn influence external factor target function accumulative ELBO easy extend model online method experimental study traffic flow data show detection capability proposed 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 | [0.0027484481688588858, 0.03369508311152458, -0.02338779717683792, 0.02868322655558586, -0.08980365097522736, -0.015192878432571888, 0.010118133388459682, -0.014228387735784054, -0.00973463337868452, 0.0182920191437006, 0.10572895407676697, 0.020029330626130104, -0.012014509178698063, 0.10851632058620453, 0.0016781978774815798, -0.014766805805265903, 0.010808509774506092, -0.028826149180531502, 0.01485917717218399, 0.026405679062008858, 0.03537343814969063, -0.025955911725759506, 0.03973963484168053, -0.010146860033273697, -0.030609337612986565, 0.015780074521899223, -0.04256397858262062, -0.01603270135819912, 0.05128259211778641, -0.07546096295118332, 0.027197884395718575, -0.0066036502830684185, 0.02479838766157627, 0.05282596871256828, 2.014414349105209e-06, 0.01355159841477871, 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913 | 913 | ['Abien Fred Agarap'] | 1709.03082v7 | Gated Recurrent Unit (GRU) is a recently-developed variation of the long
short-term memory (LSTM) unit, both of which are types of recurrent neural
network (RNN). Through empirical evidence, both models have been proven to be
effective in a wide variety of machine learning tasks such as natural language
processing (Wen et al., 2015), speech recognition (Chorowski et al., 2015), and
text classification (Yang et al., 2016). Conventionally, like most neural
networks, both of the aforementioned RNN variants employ the Softmax function
as its final output layer for its prediction, and the cross-entropy function
for computing its loss. In this paper, we present an amendment to this norm by
introducing linear support vector machine (SVM) as the replacement for Softmax
in the final output layer of a GRU model. Furthermore, the cross-entropy
function shall be replaced with a margin-based function. While there have been
similar studies (Alalshekmubarak & Smith, 2013; Tang, 2013), this proposal is
primarily intended for binary classification on intrusion detection using the
2013 network traffic data from the honeypot systems of Kyoto University.
Results show that the GRU-SVM model performs relatively higher than the
conventional GRU-Softmax model. The proposed model reached a training accuracy
of ~81.54% and a testing accuracy of ~84.15%, while the latter was able to
reach a training accuracy of ~63.07% and a testing accuracy of ~70.75%. In
addition, the juxtaposition of these two final output layers indicate that the
SVM would outperform Softmax in prediction time - a theoretical implication
which was supported by the actual training and testing time in the study. | A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and
Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data | 2,017 | http://arxiv.org/pdf/1709.03082v7 | Title Neural Network Architecture Combining Gated Recurrent Unit GRU Support Vector Machine SVM Intrusion Detection Network Traffic Data Summary Gated Recurrent Unit GRU recentlydeveloped variation long shortterm memory LSTM unit type recurrent neural network RNN empirical evidence model proven effective wide variety machine learning task natural language processing Wen et al 2015 speech recognition Chorowski et al 2015 text classification Yang et al 2016 Conventionally like neural network aforementioned RNN variant employ Softmax function final output layer prediction crossentropy function computing loss paper present amendment norm introducing linear support vector machine SVM replacement Softmax final output layer GRU model Furthermore crossentropy function shall replaced marginbased function similar study Alalshekmubarak Smith 2013 Tang 2013 proposal primarily intended binary classification intrusion detection using 2013 network traffic data honeypot system Kyoto University Results show GRUSVM model performs relatively higher conventional GRUSoftmax model proposed model reached training accuracy 8154 testing accuracy 8415 latter able reach training accuracy 6307 testing accuracy 7075 addition juxtaposition two final output layer indicate SVM would outperform Softmax prediction time theoretical implication supported actual training testing time study 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 | [0.038477201014757156, 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914 | 914 | ['Divya Gopinath', 'Guy Katz', 'Corina S. Pasareanu', 'Clark Barrett'] | 1710.00486v1 | Deep neural networks have become widely used, obtaining remarkable results in
domains such as computer vision, speech recognition, natural language
processing, audio recognition, social network filtering, machine translation,
and bio-informatics, where they have produced results comparable to human
experts. However, these networks can be easily fooled by adversarial
perturbations: minimal changes to correctly-classified inputs, that cause the
network to mis-classify them. This phenomenon represents a concern for both
safety and security, but it is currently unclear how to measure a network's
robustness against such perturbations. Existing techniques are limited to
checking robustness around a few individual input points, providing only very
limited guarantees. We propose a novel approach for automatically identifying
safe regions of the input space, within which the network is robust against
adversarial perturbations. The approach is data-guided, relying on clustering
to identify well-defined geometric regions as candidate safe regions. We then
utilize verification techniques to confirm that these regions are safe or to
provide counter-examples showing that they are not safe. We also introduce the
notion of targeted robustness which, for a given target label and region,
ensures that a NN does not map any input in the region to the target label. We
evaluated our technique on the MNIST dataset and on a neural network
implementation of a controller for the next-generation Airborne Collision
Avoidance System for unmanned aircraft (ACAS Xu). For these networks, our
approach identified multiple regions which were completely safe as well as some
which were only safe for specific labels. It also discovered several
adversarial perturbations of interest. | DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in
Neural Networks | 2,017 | http://arxiv.org/pdf/1710.00486v1 | Title DeepSafe Datadriven Approach Checking Adversarial Robustness Neural Networks Summary Deep neural network become widely used obtaining remarkable result domain computer vision speech recognition natural language processing audio recognition social network filtering machine translation bioinformatics produced result comparable human expert However network easily fooled adversarial perturbation minimal change correctlyclassified input cause network misclassify phenomenon represents concern safety security currently unclear measure network robustness perturbation Existing technique limited checking robustness around individual input point providing limited guarantee propose novel approach automatically identifying safe region input space within network robust adversarial perturbation approach dataguided relying clustering identify welldefined geometric region candidate safe region utilize verification technique confirm region safe provide counterexample showing safe also introduce notion targeted robustness given target label region ensures NN map input region target label evaluated technique MNIST dataset neural network implementation controller nextgeneration Airborne Collision Avoidance System unmanned aircraft ACAS Xu network approach identified multiple region completely safe well safe specific label also discovered several adversarial perturbation interest 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 | [-0.01810164377093315, 0.044370271265506744, -0.03122018091380596, 0.03759416937828064, 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915 | 915 | ['Grzegorz Dudek'] | 1710.04874v1 | Neural networks with random hidden nodes have gained increasing interest from
researchers and practical applications. This is due to their unique features
such as very fast training and universal approximation property. In these
networks the weights and biases of hidden nodes determining the nonlinear
feature mapping are set randomly and are not learned. Appropriate selection of
the intervals from which weights and biases are selected is extremely
important. This topic has not yet been sufficiently explored in the literature.
In this work a method of generating random weights and biases is proposed. This
method generates the parameters of the hidden nodes in such a way that
nonlinear fragments of the activation functions are located in the input space
regions with data and can be used to construct the surface approximating a
nonlinear target function. The weights and biases are dependent on the input
data range and activation function type. The proposed methods allows us to
control the generalization degree of the model. These all lead to improvement
in approximation performance of the network. Several experiments show very
promising results. | A Method of Generating Random Weights and Biases in Feedforward Neural
Networks with Random Hidden Nodes | 2,017 | http://arxiv.org/pdf/1710.04874v1 | Title Method Generating Random Weights Biases Feedforward Neural Networks Random Hidden Nodes Summary Neural network random hidden node gained increasing interest researcher practical application due unique feature fast training universal approximation property network weight bias hidden node determining nonlinear feature mapping set randomly learned Appropriate selection interval weight bias selected extremely important topic yet sufficiently explored literature work method generating random weight bias proposed method generates parameter hidden node way nonlinear fragment activation function located input space region data used construct surface approximating nonlinear target function weight bias dependent input data range activation function type proposed method allows u control generalization degree model lead improvement approximation performance network Several experiment show promising 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 | [-0.0025384598411619663, -0.0127226822078228, -0.03115057945251465, 0.00018343626288697124, -0.008374815806746483, -0.04033152014017105, 0.04117656871676445, -0.032881516963243484, 0.04597532004117966, 0.014851347543299198, 0.0009996519656851888, 0.0401940755546093, 0.011621665209531784, 0.03633009269833565, 0.03180719166994095, -0.019339242950081825, -0.01312561146914959, 0.003990543074905872, -0.00895534548908472, -0.03849358111619949, -0.004873710684478283, -0.0073939478024840355, 0.03222924843430519, 0.026902124285697937, 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916 | 916 | ['Rumen Dangovski', 'Li Jing', 'Marin Soljacic'] | 1710.09537v1 | The concepts of unitary evolution matrices and associative memory have
boosted the field of Recurrent Neural Networks (RNN) to state-of-the-art
performance in a variety of sequential tasks. However, RNN still have a limited
capacity to manipulate long-term memory. To bypass this weakness the most
successful applications of RNN use external techniques such as attention
mechanisms. In this paper we propose a novel RNN model that unifies the
state-of-the-art approaches: Rotational Unit of Memory (RUM). The core of RUM
is its rotational operation, which is, naturally, a unitary matrix, providing
architectures with the power to learn long-term dependencies by overcoming the
vanishing and exploding gradients problem. Moreover, the rotational unit also
serves as associative memory. We evaluate our model on synthetic memorization,
question answering and language modeling tasks. RUM learns the Copying Memory
task completely and improves the state-of-the-art result in the Recall task.
RUM's performance in the bAbI Question Answering task is comparable to that of
models with attention mechanism. We also improve the state-of-the-art result to
1.189 bits-per-character (BPC) loss in the Character Level Penn Treebank (PTB)
task, which is to signify the applications of RUM to real-world sequential
data. The universality of our construction, at the core of RNN, establishes RUM
as a promising approach to language modeling, speech recognition and machine
translation. | Rotational Unit of Memory | 2,017 | http://arxiv.org/pdf/1710.09537v1 | Title Rotational Unit Memory Summary concept unitary evolution matrix associative memory boosted field Recurrent Neural Networks RNN stateoftheart performance variety sequential task However RNN still limited capacity manipulate longterm memory bypass weakness successful application RNN use external technique attention mechanism paper propose novel RNN model unifies stateoftheart approach Rotational Unit Memory RUM core RUM rotational operation naturally unitary matrix providing architecture power learn longterm dependency overcoming vanishing exploding gradient problem Moreover rotational unit also serf associative memory evaluate model synthetic memorization question answering language modeling task RUM learns Copying Memory task completely improves stateoftheart result Recall task RUMs performance bAbI Question Answering task comparable model attention mechanism also improve stateoftheart result 1189 bitspercharacter BPC loss Character Level Penn Treebank PTB task signify application RUM realworld sequential data universality construction core RNN establishes RUM promising approach language modeling speech recognition machine translation 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 | [0.03185699135065079, 0.025411365553736687, -0.014463833533227444, 0.04377507418394089, -0.008871356956660748, 0.031174534931778908, 0.005433708429336548, 0.0005734682781621814, -0.06515191495418549, 0.0006912930984981358, -0.03514276072382927, -0.06120593100786209, 0.011213659308850765, 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917 | 917 | ['Tero Karras', 'Timo Aila', 'Samuli Laine', 'Jaakko Lehtinen'] | 1710.10196v3 | We describe a new training methodology for generative adversarial networks.
The key idea is to grow both the generator and discriminator progressively:
starting from a low resolution, we add new layers that model increasingly fine
details as training progresses. This both speeds the training up and greatly
stabilizes it, allowing us to produce images of unprecedented quality, e.g.,
CelebA images at 1024^2. We also propose a simple way to increase the variation
in generated images, and achieve a record inception score of 8.80 in
unsupervised CIFAR10. Additionally, we describe several implementation details
that are important for discouraging unhealthy competition between the generator
and discriminator. Finally, we suggest a new metric for evaluating GAN results,
both in terms of image quality and variation. As an additional contribution, we
construct a higher-quality version of the CelebA dataset. | Progressive Growing of GANs for Improved Quality, Stability, and
Variation | 2,017 | http://arxiv.org/pdf/1710.10196v3 | Title Progressive Growing GANs Improved Quality Stability Variation Summary describe new training methodology generative adversarial network key idea grow generator discriminator progressively starting low resolution add new layer model increasingly fine detail training progress speed training greatly stabilizes allowing u produce image unprecedented quality eg CelebA image 10242 also propose simple way increase variation generated image achieve record inception score 880 unsupervised CIFAR10 Additionally describe several implementation detail important discouraging unhealthy competition generator discriminator Finally suggest new metric evaluating GAN result term image quality variation additional contribution construct higherquality version CelebA 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 | [0.01871155947446823, 0.11053736507892609, -0.011309931986033916, 0.024971190840005875, 0.017558861523866653, -0.028626861050724983, 0.015793029218912125, 0.004993888549506664, -0.03921636939048767, 0.04815524071455002, 0.029475171118974686, 0.0026604984886944294, -0.02696581743657589, 0.022566430270671844, 0.05407110974192619, -0.0689169317483902, -0.018480265513062477, 0.05168270692229271, -0.018493682146072388, -0.026327023282647133, 0.03238179162144661, 0.0012795018265023828, 0.009063071571290493, 0.002366574015468359, -0.08418120443820953, -0.014852040447294712, -0.009469193406403065, -0.005358773283660412, 0.041365545243024826, -0.03782596066594124, -0.0009374485234729946, 0.004224509932100773, 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918 | 918 | ['Cem Subakan', 'Paris Smaragdis'] | 1710.10779v1 | Generative source separation methods such as non-negative matrix
factorization (NMF) or auto-encoders, rely on the assumption of an output
probability density. Generative Adversarial Networks (GANs) can learn data
distributions without needing a parametric assumption on the output density. We
show on a speech source separation experiment that, a multi-layer perceptron
trained with a Wasserstein-GAN formulation outperforms NMF, auto-encoders
trained with maximum likelihood, and variational auto-encoders in terms of
source to distortion ratio. | Generative Adversarial Source Separation | 2,017 | http://arxiv.org/pdf/1710.10779v1 | Title Generative Adversarial Source Separation Summary Generative source separation method nonnegative matrix factorization NMF autoencoders rely assumption output probability density Generative Adversarial Networks GANs learn data distribution without needing parametric assumption output density show speech source separation experiment multilayer perceptron trained WassersteinGAN formulation outperforms NMF autoencoders trained maximum likelihood variational autoencoders term source distortion ratio 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 | [-0.00513956043869257, 0.08517022430896759, 0.006246505305171013, 0.030120765790343285, 0.0018776756478473544, -0.02955840528011322, 0.027502423152327538, -0.0003878129937220365, -0.09014207869768143, -0.00039256407762877643, -0.0823880136013031, -0.006068127229809761, -0.013439353555440903, 0.0016103184316307306, 0.06918319314718246, -0.04208170995116234, 0.005081041716039181, -0.02075408585369587, -0.038111891597509384, -0.010879455134272575, -0.006897368468344212, -0.026556042954325676, -0.024455934762954712, -0.00332547165453434, -0.02826683595776558, -0.00491099338978529, -0.012975763529539108, -0.024917814880609512, 0.03379470109939575, 0.0203222818672657, -0.03477222099900246, -0.011427739635109901, -0.0020935267675668, 0.050318535417318344, 2.2910696770850336e-06, 0.034173861145973206, 0.008265495300292969, -0.006771268788725138, 0.009841947816312313, 0.023359389975667, 0.008488349616527557, 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919 | 919 | ['Yuan Zeng', 'Kevin Devincentis', 'Yao Xiao', 'Zubayer Ibne Ferdous', 'Xiaochen Guo', 'Zhiyuan Yan', 'Yevgeny Berdichevsky'] | 1710.10944v3 | Neural networks have shown great potential in many applications like speech
recognition, drug discovery, image classification, and object detection. Neural
network models are inspired by biological neural networks, but they are
optimized to perform machine learning tasks on digital computers. The proposed
work explores the possibilities of using living neural networks in vitro as
basic computational elements for machine learning applications. A new
supervised STDP-based learning algorithm is proposed in this work, which
considers neuron engineering constrains. A 74.7% accuracy is achieved on the
MNIST benchmark for handwritten digit recognition. | A Supervised STDP-based Training Algorithm for Living Neural Networks | 2,017 | http://arxiv.org/pdf/1710.10944v3 | Title Supervised STDPbased Training Algorithm Living Neural Networks Summary Neural network shown great potential many application like speech recognition drug discovery image classification object detection Neural network model inspired biological neural network optimized perform machine learning task digital computer proposed work explores possibility using living neural network vitro basic computational element machine learning application new supervised STDPbased learning algorithm proposed work considers neuron engineering constrains 747 accuracy achieved MNIST benchmark handwritten digit 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 | [-0.012166383676230907, -0.008391223847866058, -0.018980277702212334, 0.04643481224775314, 0.027766350656747818, 3.060038943658583e-05, 0.059188112616539, -0.008656064979732037, 0.01536852028220892, 0.006702076178044081, -0.016468089073896408, 0.018291722983121872, 0.021369101479649544, 0.04788945987820625, 0.04378671199083328, 0.02173311822116375, 0.007040274329483509, 0.056248776614665985, -0.025057164952158928, -0.034651368856430054, -0.0012829072074964643, -0.04214468225836754, -0.024662982672452927, 0.04474843665957451, -0.011151665821671486, 0.03851737827062607, 0.009783967398107052, -0.056897588074207306, 0.04009445011615753, -0.04252440482378006, 0.03198009356856346, -0.03957834467291832, 0.03053857386112213, 0.0606289841234684, 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920 | 920 | ['John Alberg', 'Zachary C. Lipton'] | 1711.04837v1 | On a periodic basis, publicly traded companies are required to report
fundamentals: financial data such as revenue, operating income, debt, among
others. These data points provide some insight into the financial health of a
company. Academic research has identified some factors, i.e. computed features
of the reported data, that are known through retrospective analysis to
outperform the market average. Two popular factors are the book value
normalized by market capitalization (book-to-market) and the operating income
normalized by the enterprise value (EBIT/EV). In this paper: we first show
through simulation that if we could (clairvoyantly) select stocks using factors
calculated on future fundamentals (via oracle), then our portfolios would far
outperform a standard factor approach. Motivated by this analysis, we train
deep neural networks to forecast future fundamentals based on a trailing
5-years window. Quantitative analysis demonstrates a significant improvement in
MSE over a naive strategy. Moreover, in retrospective analysis using an
industry-grade stock portfolio simulator (backtester), we show an improvement
in compounded annual return to 17.1% (MLP) vs 14.4% for a standard factor
model. | Improving Factor-Based Quantitative Investing by Forecasting Company
Fundamentals | 2,017 | http://arxiv.org/pdf/1711.04837v1 | Title Improving FactorBased Quantitative Investing Forecasting Company Fundamentals Summary periodic basis publicly traded company required report fundamental financial data revenue operating income debt among others data point provide insight financial health company Academic research identified factor ie computed feature reported data known retrospective analysis outperform market average Two popular factor book value normalized market capitalization booktomarket operating income normalized enterprise value EBITEV paper first show simulation could clairvoyantly select stock using factor calculated future fundamental via oracle portfolio would far outperform standard factor approach Motivated analysis train deep neural network forecast future fundamental based trailing 5years window Quantitative analysis demonstrates significant improvement MSE naive strategy Moreover retrospective analysis using industrygrade stock portfolio simulator backtester show improvement compounded annual return 171 MLP v 144 standard 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 | [-0.02115362137556076, 0.06896302849054337, -0.037331871688365936, -0.028223684057593346, 0.021599896252155304, -0.04645337536931038, 0.004053597804158926, 0.03677145391702652, -0.06326203048229218, -0.024846667423844337, 0.015410063788294792, 0.04602045193314552, 0.0012376694940030575, 0.14129263162612915, 0.012704470194876194, 0.023898381739854813, 0.02402772568166256, -0.02060450240969658, 0.003679730696603656, 0.008736394345760345, 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921 | 921 | ['Eli David', 'Moshe Koppel', 'Nathan S. Netanyahu'] | 1711.06839v1 | In this paper we demonstrate how genetic algorithms can be used to reverse
engineer an evaluation function's parameters for computer chess. Our results
show that using an appropriate mentor, we can evolve a program that is on par
with top tournament-playing chess programs, outperforming a two-time World
Computer Chess Champion. This performance gain is achieved by evolving a
program with a smaller number of parameters in its evaluation function to mimic
the behavior of a superior mentor which uses a more extensive evaluation
function. In principle, our mentor-assisted approach could be used in a wide
range of problems for which appropriate mentors are available. | Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization | 2,017 | http://arxiv.org/pdf/1711.06839v1 | Title Genetic Algorithms MentorAssisted Evaluation Function Optimization Summary paper demonstrate genetic algorithm used reverse engineer evaluation function parameter computer chess result show using appropriate mentor evolve program par top tournamentplaying chess program outperforming twotime World Computer Chess Champion performance gain achieved evolving program smaller number parameter evaluation function mimic behavior superior mentor us extensive evaluation function principle mentorassisted approach could used wide range problem appropriate mentor available 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 | [0.027218738570809364, 0.04186451435089111, -0.029778560623526573, 0.042456574738025665, -0.016284598037600517, -0.03932826593518257, -0.05600321292877197, 0.030629165470600128, 0.008055981248617172, -0.024177247658371925, 0.03700937330722809, -0.008535700850188732, 0.0029371825512498617, 0.02641790732741356, 0.02185426652431488, 0.004576041363179684, -0.0004206383309792727, -0.020796598866581917, -0.013194660656154156, -0.0017836075276136398, -0.004766510799527168, -0.03732072189450264, 0.01377724390476942, 0.05474495142698288, 0.02838623709976673, 0.031265463680028915, -0.04480505362153053, -0.02121683396399021, 0.0030682997312396765, -0.024928823113441467, -0.03253333270549774, -0.049189720302820206, -0.03554409369826317, 0.016691848635673523, 2.152663682863931e-06, 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922 | 922 | ['Guglielmo Montone', "J. Kevin O'Regan", 'Alexander V. Terekhov'] | 1711.10204v1 | In the present work we propose a Deep Feed Forward network architecture which
can be trained according to a sequential learning paradigm, where tasks of
increasing difficulty are learned sequentially, yet avoiding catastrophic
forgetting. The proposed architecture can re-use the features learned on
previous tasks in a new task when the old tasks and the new one are related.
The architecture needs fewer computational resources (neurons and connections)
and less data for learning the new task than a network trained from scratch | Block Neural Network Avoids Catastrophic Forgetting When Learning
Multiple Task | 2,017 | http://arxiv.org/pdf/1711.10204v1 | Title Block Neural Network Avoids Catastrophic Forgetting Learning Multiple Task Summary present work propose Deep Feed Forward network architecture trained according sequential learning paradigm task increasing difficulty learned sequentially yet avoiding catastrophic forgetting proposed architecture reuse feature learned previous task new task old task new one related architecture need fewer computational resource neuron connection le data learning new task network trained scratch Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype 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923 | 923 | ['Kyeong Soo Kim', 'Sanghyuk Lee', 'Kaizhu Huang'] | 1712.01990v1 | One of the key technologies for future large-scale location-aware services
covering a complex of multi-story buildings --- e.g., a big shopping mall and a
university campus --- is a scalable indoor localization technique. In this
paper, we report the current status of our investigation on the use of deep
neural networks (DNNs) for scalable building/floor classification and
floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the
hierarchical nature of the building/floor estimation and floor-level
coordinates estimation of a location, we propose a new DNN architecture
consisting of a stacked autoencoder for the reduction of feature space
dimension and a feed-forward classifier for multi-label classification of
building/floor/location, on which the multi-building and multi-floor indoor
localization system based on Wi-Fi fingerprinting is built. Experimental
results for the performance of building/floor estimation and floor-level
coordinates estimation of a given location demonstrate the feasibility of the
proposed DNN-based indoor localization system, which can provide near
state-of-the-art performance using a single DNN, for the implementation with
lower complexity and energy consumption at mobile devices. | A Scalable Deep Neural Network Architecture for Multi-Building and
Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting | 2,017 | http://arxiv.org/pdf/1712.01990v1 | Title Scalable Deep Neural Network Architecture MultiBuilding MultiFloor Indoor Localization Based WiFi Fingerprinting Summary One key technology future largescale locationaware service covering complex multistory building eg big shopping mall university campus scalable indoor localization technique paper report current status investigation use deep neural network DNNs scalable buildingfloor classification floorlevel position estimation based WiFi fingerprinting Exploiting hierarchical nature buildingfloor estimation floorlevel coordinate estimation location propose new DNN architecture consisting stacked autoencoder reduction feature space dimension feedforward classifier multilabel classification buildingfloorlocation multibuilding multifloor indoor localization system based WiFi fingerprinting built Experimental result performance buildingfloor estimation floorlevel coordinate estimation given location demonstrate feasibility proposed DNNbased indoor localization system provide near stateoftheart performance using single DNN implementation lower complexity energy consumption mobile device 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 | [-0.04584827646613121, 0.0041849929839372635, -0.018235113471746445, -0.02328268066048622, -0.027477629482746124, 0.007399750407785177, 0.06161508336663246, -0.06816114485263824, -0.027744336053729057, -0.017726950347423553, -0.046600449830293655, 0.032152097672224045, 0.026632782071828842, 0.028290538117289543, 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924 | 924 | ['Rudy Raymond', 'Takayuki Osogami', 'Sakyasingha Dasgupta'] | 1712.06132v1 | Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict
time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is
generated by a Gaussian distribution whose first-order moment (mean)
dynamically changes over time but its second-order moment (variance) is fixed.
However, in many financial applications, the assumption is quite limiting in
two aspects. First, even when the data follows a Gaussian distribution, its
variance may change over time. Such variance is also related to important
temporal economic indicators such as the market volatility. Second, financial
time-series data often requires learning datasets generated by the generalized
Gaussian distribution with an additional shape parameter that is important to
approximate heavy-tailed distributions. Addressing those aspects, we show how
to extend DyBM that results in significant performance improvement in
predicting financial time-series data. | Dynamic Boltzmann Machines for Second Order Moments and Generalized
Gaussian Distributions | 2,017 | http://arxiv.org/pdf/1712.06132v1 | Title Dynamic Boltzmann Machines Second Order Moments Generalized Gaussian Distributions Summary Dynamic Boltzmann Machine DyBM shown highly efficient predict timeseries data Gaussian DyBM DyBM assumes predicted data generated Gaussian distribution whose firstorder moment mean dynamically change time secondorder moment variance fixed However many financial application assumption quite limiting two aspect First even data follows Gaussian distribution variance may change time variance also related important temporal economic indicator market volatility Second financial timeseries data often requires learning datasets generated generalized Gaussian distribution additional shape parameter important approximate heavytailed distribution Addressing aspect show extend DyBM result significant performance improvement predicting financial timeseries 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 | [-0.005392593331634998, -0.030938461422920227, -0.056202471256256104, -0.04896299168467522, 0.027759017422795296, 0.006648993585258722, 0.010414808988571167, -0.0027628049720078707, -0.05353260412812233, 0.05078194662928581, 0.012549011036753654, 0.005730956327170134, 0.0038401647470891476, 0.07450827211141586, 0.024848133325576782, -0.04846253618597984, -0.009471450001001358, -0.031048614531755447, 0.04724458232522011, -0.04276781529188156, 0.06441491842269897, -0.04031553491950035, 0.036965858191251755, -0.011082367971539497, -0.060192741453647614, 0.028781866654753685, -0.02179878018796444, 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925 | 925 | ['Marco Martinolli', 'Wulfram Gerstner', 'Aditya Gilra'] | 1712.10062v1 | Learning and memory are intertwined in our brain and their relationship is at
the core of several recent neural network models. In particular, the
Attention-Gated MEmory Tagging model (AuGMEnT) is a reinforcement learning
network with an emphasis on biological plausibility of memory dynamics and
learning. We find that the AuGMEnT network does not solve some hierarchical
tasks, where higher-level stimuli have to be maintained over a long time, while
lower-level stimuli need to be remembered and forgotten over a shorter
timescale. To overcome this limitation, we introduce hybrid AuGMEnT, with leaky
or short-timescale and non-leaky or long-timescale units in memory, that allow
to exchange lower-level information while maintaining higher-level one, thus
solving both hierarchical and distractor tasks. | Multi-timescale memory dynamics in a reinforcement learning network with
attention-gated memory | 2,017 | http://arxiv.org/pdf/1712.10062v1 | Title Multitimescale memory dynamic reinforcement learning network attentiongated memory Summary Learning memory intertwined brain relationship core several recent neural network model particular AttentionGated MEmory Tagging model AuGMEnT reinforcement learning network emphasis biological plausibility memory dynamic learning find AuGMEnT network solve hierarchical task higherlevel stimulus maintained long time lowerlevel stimulus need remembered forgotten shorter timescale overcome limitation introduce hybrid AuGMEnT leaky shorttimescale nonleaky longtimescale unit memory allow exchange lowerlevel information maintaining higherlevel one thus solving hierarchical distractor 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 | [0.011148829013109207, -0.04073084890842438, -0.03570118173956871, -0.023461485281586647, 0.00968702882528305, 0.028548099100589752, 0.022292574867606163, -0.031344763934612274, -0.02595648169517517, 0.0017757597379386425, -0.03176902234554291, -0.013322173617780209, 0.020687390118837357, 0.05467847362160683, 0.0242297425866127, -0.10126943141222, 0.05707624927163124, 0.0009987199446186423, 0.009144500829279423, -0.0072518023662269115, 0.014537802897393703, -0.0591365285217762, -0.01406377274543047, 0.008231987245380878, -0.013304335996508598, -0.03763509541749954, -0.0405210442841053, -0.01623477041721344, 0.03947511687874794, -0.07284731417894363, 0.016439152881503105, 0.00680364016443491, 0.027319978922605515, 0.018407775089144707, 2.574298378021922e-06, 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926 | 926 | ['Enrique Romero Merino', 'Ferran Mazzanti Castrillejo', 'Jordi Delgado Pin', 'David Buchaca Prats'] | 1801.02567v1 | Learning algorithms for energy based Boltzmann architectures that rely on
gradient descent are in general computationally prohibitive, typically due to
the exponential number of terms involved in computing the partition function.
In this way one has to resort to approximation schemes for the evaluation of
the gradient. This is the case of Restricted Boltzmann Machines (RBM) and its
learning algorithm Contrastive Divergence (CD). It is well-known that CD has a
number of shortcomings, and its approximation to the gradient has several
drawbacks. Overcoming these defects has been the basis of much research and new
algorithms have been devised, such as persistent CD. In this manuscript we
propose a new algorithm that we call Weighted CD (WCD), built from small
modifications of the negative phase in standard CD. However small these
modifications may be, experimental work reported in this paper suggest that WCD
provides a significant improvement over standard CD and persistent CD at a
small additional computational cost. | Weighted Contrastive Divergence | 2,018 | http://arxiv.org/pdf/1801.02567v1 | Title Weighted Contrastive Divergence Summary Learning algorithm energy based Boltzmann architecture rely gradient descent general computationally prohibitive typically due exponential number term involved computing partition function way one resort approximation scheme evaluation gradient case Restricted Boltzmann Machines RBM learning algorithm Contrastive Divergence CD wellknown CD number shortcoming approximation gradient several drawback Overcoming defect basis much research new algorithm devised persistent CD manuscript propose new algorithm call Weighted CD WCD built small modification negative phase standard CD However small modification may experimental work reported paper suggest WCD provides significant improvement standard CD persistent CD small additional 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 | [-4.9399870476918295e-05, -0.028885919600725174, -0.039195675402879715, 0.03501042351126671, -0.023319285362958908, 0.0015925582265481353, 0.04114723578095436, 0.0066097634844481945, -0.007172462530434132, 0.04707513377070427, 0.004055690951645374, -0.019096169620752335, 0.020037123933434486, 0.034189775586128235, 0.03234492987394333, -0.06489729881286621, -0.0107605354860425, 0.011298290453851223, -0.014989112503826618, -0.05698176100850105, 0.06458868831396103, -0.05994134023785591, 0.018598664551973343, -0.020763231441378593, -0.054747119545936584, 0.031967587769031525, -0.03102501668035984, -0.0076601458713412285, 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927 | 927 | ['Shinichi Shirakawa', 'Yasushi Iwata', 'Youhei Akimoto'] | 1801.07650v1 | Deep neural networks (DNNs) are powerful machine learning models and have
succeeded in various artificial intelligence tasks. Although various
architectures and modules for the DNNs have been proposed, selecting and
designing the appropriate network structure for a target problem is a
challenging task. In this paper, we propose a method to simultaneously optimize
the network structure and weight parameters during neural network training. We
consider a probability distribution that generates network structures, and
optimize the parameters of the distribution instead of directly optimizing the
network structure. The proposed method can apply to the various network
structure optimization problems under the same framework. We apply the proposed
method to several structure optimization problems such as selection of layers,
selection of unit types, and selection of connections using the MNIST,
CIFAR-10, and CIFAR-100 datasets. The experimental results show that the
proposed method can find the appropriate and competitive network structures. | Dynamic Optimization of Neural Network Structures Using Probabilistic
Modeling | 2,018 | http://arxiv.org/pdf/1801.07650v1 | Title Dynamic Optimization Neural Network Structures Using Probabilistic Modeling Summary Deep neural network DNNs powerful machine learning model succeeded various artificial intelligence task Although various architecture module DNNs proposed selecting designing appropriate network structure target problem challenging task paper propose method simultaneously optimize network structure weight parameter neural network training consider probability distribution generates network structure optimize parameter distribution instead directly optimizing network structure proposed method apply various network structure optimization problem framework apply proposed method several structure optimization problem selection layer selection unit type selection connection using MNIST CIFAR10 CIFAR100 datasets experimental result show proposed method find appropriate competitive network structure 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 | [-0.013948259875178337, 0.015110231004655361, -0.04892431199550629, 0.01586272194981575, -0.007155288942158222, -0.05642760545015335, 0.026939455419778824, -0.04784902185201645, -0.03239194676280022, 0.016747971996665, 0.0051847235299646854, -0.01960200071334839, 0.03455367684364319, 0.06773195415735245, 0.030500292778015137, -0.025321701541543007, -0.0024274445604532957, -0.019298506900668144, 0.01265974435955286, -0.011604352854192257, -0.008373583666980267, -0.04272117093205452, 0.009068628773093224, 0.024570317938923836, -0.0360477976500988, 0.04794897511601448, 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928 | 928 | ['Mauro Castelli', 'Ivo Gonçalves', 'Luca Manzoni', 'Leonardo Vanneschi'] | 1801.07668v1 | The objective of this paper is to define an effective strategy for building
an ensemble of Genetic Programming (GP) models. Ensemble methods are widely
used in machine learning due to their features: they average out biases, they
reduce the variance and they usually generalize better than single models.
Despite these advantages, building ensemble of GP models is not a
well-developed topic in the evolutionary computation community. To fill this
gap, we propose a strategy that blends individuals produced by standard
syntax-based GP and individuals produced by geometric semantic genetic
programming, one of the newest semantics-based method developed in GP. In fact,
recent literature showed that combining syntax and semantics could improve the
generalization ability of a GP model. Additionally, to improve the diversity of
the GP models used to build up the ensemble, we propose different pruning
criteria that are based on correlation and entropy, a commonly used measure in
information theory. Experimental results,obtained over different complex
problems, suggest that the pruning criteria based on correlation and entropy
could be effective in improving the generalization ability of the ensemble
model and in reducing the computational burden required to build it. | Pruning Techniques for Mixed Ensembles of Genetic Programming Models | 2,018 | http://arxiv.org/pdf/1801.07668v1 | Title Pruning Techniques Mixed Ensembles Genetic Programming Models Summary objective paper define effective strategy building ensemble Genetic Programming GP model Ensemble method widely used machine learning due feature average bias reduce variance usually generalize better single model Despite advantage building ensemble GP model welldeveloped topic evolutionary computation community fill gap propose strategy blend individual produced standard syntaxbased GP individual produced geometric semantic genetic programming one newest semanticsbased method developed GP fact recent literature showed combining syntax semantics could improve generalization ability GP model Additionally improve diversity GP model used build ensemble propose different pruning criterion based correlation entropy commonly used measure information theory Experimental resultsobtained different complex problem suggest pruning criterion based correlation entropy could effective improving generalization ability ensemble model reducing computational burden required build 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 | [-0.015443344600498676, 0.06426004320383072, -0.04797778278589249, 0.003623044118285179, -0.04073004424571991, 0.008288819342851639, -0.02020888403058052, 0.04328633099794388, -0.024020640179514885, -0.0010451363632455468, 0.0330679826438427, -0.014003001153469086, 0.032348960638046265, 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929 | 929 | ['Guillaume Desjardins', 'Razvan Pascanu', 'Aaron Courville', 'Yoshua Bengio'] | 1301.3545v2 | This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm for
training Boltzmann Machines. Similar in spirit to the Hessian-Free method of
Martens [8], our algorithm belongs to the family of truncated Newton methods
and exploits an efficient matrix-vector product to avoid explicitely storing
the natural gradient metric $L$. This metric is shown to be the expected second
derivative of the log-partition function (under the model distribution), or
equivalently, the variance of the vector of partial derivatives of the energy
function. We evaluate our method on the task of joint-training a 3-layer Deep
Boltzmann Machine and show that MFNG does indeed have faster per-epoch
convergence compared to Stochastic Maximum Likelihood with centering, though
wall-clock performance is currently not competitive. | Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines | 2,013 | http://arxiv.org/pdf/1301.3545v2 | Title MetricFree Natural Gradient JointTraining Boltzmann Machines Summary paper introduces MetricFree Natural Gradient MFNG algorithm training Boltzmann Machines Similar spirit HessianFree method Martens 8 algorithm belongs family truncated Newton method exploit efficient matrixvector product avoid explicitely storing natural gradient metric L metric shown expected second derivative logpartition function model distribution equivalently variance vector partial derivative energy function evaluate method task jointtraining 3layer Deep Boltzmann Machine show MFNG indeed faster perepoch convergence compared Stochastic Maximum Likelihood centering though wallclock performance currently competitive 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 | [-0.03228992596268654, -0.0158121008425951, 0.0068503329530358315, 0.05876896157860756, 0.01943853870034218, 0.019549576565623283, 0.037552040070295334, 0.00020413470338098705, -0.012966914102435112, 0.010233981534838676, -0.04960251972079277, 0.011127850972115993, -0.013932737521827221, 0.01020741555839777, 0.025607209652662277, -0.0644596666097641, -0.022719155997037888, 0.029166463762521744, -0.0326182097196579, -0.03326869383454323, 0.03753020241856575, -0.004223763942718506, -0.004116390366107225, -0.002021771389991045, -0.06462503224611282, 0.0022777176927775145, -0.03631360083818436, 0.02471543662250042, 0.05987928807735443, -0.014267796650528908, 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930 | 930 | ['Matthew D. Zeiler', 'Rob Fergus'] | 1301.3557v1 | We introduce a simple and effective method for regularizing large
convolutional neural networks. We replace the conventional deterministic
pooling operations with a stochastic procedure, randomly picking the activation
within each pooling region according to a multinomial distribution, given by
the activities within the pooling region. The approach is hyper-parameter free
and can be combined with other regularization approaches, such as dropout and
data augmentation. We achieve state-of-the-art performance on four image
datasets, relative to other approaches that do not utilize data augmentation. | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | 2,013 | http://arxiv.org/pdf/1301.3557v1 | Title Stochastic Pooling Regularization Deep Convolutional Neural Networks Summary introduce simple effective method regularizing large convolutional neural network replace conventional deterministic pooling operation stochastic procedure randomly picking activation within pooling region according multinomial distribution given activity within pooling region approach hyperparameter free combined regularization approach dropout data augmentation achieve stateoftheart performance four image datasets relative approach utilize data augmentation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen 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931 | 931 | ['Ryan Kiros'] | 1301.3641v3 | Hessian-free (HF) optimization has been successfully used for training deep
autoencoders and recurrent networks. HF uses the conjugate gradient algorithm
to construct update directions through curvature-vector products that can be
computed on the same order of time as gradients. In this paper we exploit this
property and study stochastic HF with gradient and curvature mini-batches
independent of the dataset size. We modify Martens' HF for these settings and
integrate dropout, a method for preventing co-adaptation of feature detectors,
to guard against overfitting. Stochastic Hessian-free optimization gives an
intermediary between SGD and HF that achieves competitive performance on both
classification and deep autoencoder experiments. | Training Neural Networks with Stochastic Hessian-Free Optimization | 2,013 | http://arxiv.org/pdf/1301.3641v3 | Title Training Neural Networks Stochastic HessianFree Optimization Summary Hessianfree HF optimization successfully used training deep autoencoders recurrent network HF us conjugate gradient algorithm construct update direction curvaturevector product computed order time gradient paper exploit property study stochastic HF gradient curvature minibatches independent dataset size modify Martens HF setting integrate dropout method preventing coadaptation feature detector guard overfitting Stochastic Hessianfree optimization give intermediary SGD HF achieves competitive performance classification deep autoencoder experiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02654571644961834, 0.025545494630932808, -0.00654709804803133, 0.07047495245933533, 0.027276460081338882, -0.005315929185599089, 0.02863301709294319, -0.016452424228191376, -0.022089684382081032, -0.004190881736576557, -0.018904509022831917, -0.02286473475396633, 0.022894447669386864, 0.03298916667699814, 0.010144852101802826, -0.08981166779994965, -0.0021162063349038363, -0.003242925275117159, 0.0022185540292412043, -0.04823808744549751, -0.021922139450907707, -0.009061848744750023, -0.017024409025907516, 0.02872350811958313, -0.09293743968009949, 0.03409801423549652, -0.022902019321918488, 0.0186513289809227, -0.0026375025045126677, -0.04779640585184097, 0.036736126989126205, 0.013360588811337948, 0.02257954701781273, 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932 | 932 | ['Christophe Andrieu', 'Nando de Freitas', 'Arnaud Doucet'] | 1301.3833v1 | We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated
annealing algorithm to optimize radial basis function (RBF) networks. This
algorithm enables us to maximize the joint posterior distribution of the
network parameters and the number of basis functions. It performs a global
search in the joint space of the parameters and number of parameters, thereby
surmounting the problem of local minima. We also show that by calibrating a
Bayesian model, we can obtain the classical AIC, BIC and MDL model selection
criteria within a penalized likelihood framework. Finally, we show
theoretically and empirically that the algorithm converges to the modes of the
full posterior distribution in an efficient way. | Reversible Jump MCMC Simulated Annealing for Neural Networks | 2,013 | http://arxiv.org/pdf/1301.3833v1 | Title Reversible Jump MCMC Simulated Annealing Neural Networks Summary propose novel reversible jump Markov chain Monte Carlo MCMC simulated annealing algorithm optimize radial basis function RBF network algorithm enables u maximize joint posterior distribution network parameter number basis function performs global search joint space parameter number parameter thereby surmounting problem local minimum also show calibrating Bayesian model obtain classical AIC BIC MDL model selection criterion within penalized likelihood framework Finally show theoretically empirically algorithm converges mode full posterior distribution efficient way 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 | [-0.010786466300487518, -0.009773246012628078, -0.018252236768603325, -0.04224435240030289, -0.0017850991571322083, -0.015829797834157944, -0.07046064734458923, -0.0033105129841715097, -0.03357561305165291, 0.01090709026902914, -0.04411371052265167, 0.02305375039577484, -0.00020831477013416588, 0.022349296137690544, 0.012562781572341919, 0.03228284791111946, -0.018973741680383682, -0.0335841029882431, -0.0029224869795143604, -0.02809632569551468, 0.015473533421754837, -0.010125985369086266, -0.006271610967814922, 0.010868058539927006, -0.004265624098479748, 0.03708944842219353, -0.03231572359800339, 0.009409465827047825, 0.049235906451940536, -0.052200883626937866, 0.0036111411172896624, -0.016145125031471252, 0.021749623119831085, 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933 | 933 | ['Misha Denil', 'Babak Shakibi', 'Laurent Dinh', "Marc'Aurelio Ranzato", 'Nando de Freitas'] | 1306.0543v2 | We demonstrate that there is significant redundancy in the parameterization
of several deep learning models. Given only a few weight values for each
feature it is possible to accurately predict the remaining values. Moreover, we
show that not only can the parameter values be predicted, but many of them need
not be learned at all. We train several different architectures by learning
only a small number of weights and predicting the rest. In the best case we are
able to predict more than 95% of the weights of a network without any drop in
accuracy. | Predicting Parameters in Deep Learning | 2,013 | http://arxiv.org/pdf/1306.0543v2 | Title Predicting Parameters Deep Learning Summary demonstrate significant redundancy parameterization several deep learning model Given weight value feature possible accurately predict remaining value Moreover show parameter value predicted many need learned train several different architecture learning small number weight predicting rest best case able predict 95 weight network without drop 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 | [0.0017769146943464875, 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934 | 934 | ['Guillaume Desjardins', 'Aaron Courville', 'Yoshua Bengio'] | 1210.5474v1 | Here we propose a novel model family with the objective of learning to
disentangle the factors of variation in data. Our approach is based on the
spike-and-slab restricted Boltzmann machine which we generalize to include
higher-order interactions among multiple latent variables. Seen from a
generative perspective, the multiplicative interactions emulates the entangling
of factors of variation. Inference in the model can be seen as disentangling
these generative factors. Unlike previous attempts at disentangling latent
factors, the proposed model is trained using no supervised information
regarding the latent factors. We apply our model to the task of facial
expression classification. | Disentangling Factors of Variation via Generative Entangling | 2,012 | http://arxiv.org/pdf/1210.5474v1 | Title Disentangling Factors Variation via Generative Entangling Summary propose novel model family objective learning disentangle factor variation data approach based spikeandslab restricted Boltzmann machine generalize include higherorder interaction among multiple latent variable Seen generative perspective multiplicative interaction emulates entangling factor variation Inference model seen disentangling generative factor Unlike previous attempt disentangling latent factor proposed model trained using supervised information regarding latent factor apply model task facial expression classification 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 | [0.01148148626089096, 0.044371139258146286, -0.017013508826494217, 0.0487491749227047, 0.03362919017672539, 0.058040469884872437, 0.03184833005070686, 0.03132623806595802, -0.0501619428396225, 0.04874086380004883, 0.015379191376268864, -0.008634132333099842, -0.001956221181899309, 0.09703218191862106, 0.010026331059634686, -0.08518803864717484, -0.0419931560754776, 0.02239065244793892, 0.025259602814912796, -0.027403924614191055, 0.0571800097823143, 0.007966537959873676, 0.010683698579668999, 0.008485913276672363, -0.0811401978135109, -0.029836850240826607, -0.05046450346708298, -0.0014695074642077088, 0.06338639557361603, 0.03235877305269241, -0.0066551524214446545, 0.05377838760614395, 0.04734805226325989, -0.03124319575726986, 2.4315079372172477e-06, -0.01556156575679779, 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935 | 935 | ['Marie Cottrell', 'Madalina Olteanu', 'Fabrice Rossi', 'Joseph Rynkiewicz', 'Nathalie Villa-Vialaneix'] | 1210.6511v1 | Artificial neural networks are simple and efficient machine learning tools.
Defined originally in the traditional setting of simple vector data, neural
network models have evolved to address more and more difficulties of complex
real world problems, ranging from time evolving data to sophisticated data
structures such as graphs and functions. This paper summarizes advances on
those themes from the last decade, with a focus on results obtained by members
of the SAMM team of Universit\'e Paris 1 | Neural Networks for Complex Data | 2,012 | http://arxiv.org/pdf/1210.6511v1 | Title Neural Networks Complex Data Summary Artificial neural network simple efficient machine learning tool Defined originally traditional setting simple vector data neural network model evolved address difficulty complex real world problem ranging time evolving data sophisticated data structure graph function paper summarizes advance theme last decade focus result obtained member SAMM team Universite Paris 1 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 | 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936 | 936 | ['George E. Dahl', 'Navdeep Jaitly', 'Ruslan Salakhutdinov'] | 1406.1231v1 | Although artificial neural networks have occasionally been used for
Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in
the past, the literature has of late been dominated by other machine learning
techniques such as random forests. However, a variety of new neural net
techniques along with successful applications in other domains have renewed
interest in network approaches. In this work, inspired by the winning team's
use of neural networks in a recent QSAR competition, we used an artificial
neural network to learn a function that predicts activities of compounds for
multiple assays at the same time. We conducted experiments leveraging recent
methods for dealing with overfitting in neural networks as well as other tricks
from the neural networks literature. We compared our methods to alternative
methods reported to perform well on these tasks and found that our neural net
methods provided superior performance. | Multi-task Neural Networks for QSAR Predictions | 2,014 | http://arxiv.org/pdf/1406.1231v1 | Title Multitask Neural Networks QSAR Predictions Summary Although artificial neural network occasionally used Quantitative StructureActivityProperty Relationship QSARQSPR study past literature late dominated machine learning technique random forest However variety new neural net technique along successful application domain renewed interest network approach work inspired winning team use neural network recent QSAR competition used artificial neural network learn function predicts activity compound multiple assay time conducted experiment leveraging recent method dealing overfitting neural network well trick neural network literature compared method alternative method reported perform well task found neural net method provided superior performance 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 | [-0.004167737439274788, 0.0201212540268898, -0.03157481923699379, -0.021820642054080963, 0.009284436702728271, -0.024197613820433617, 0.03925786167383194, 0.0022811272647231817, 0.06030399724841118, 0.03369792550802231, 0.013195612467825413, 0.0020212747622281313, -0.006949395872652531, 0.05171136185526848, 0.037571411579847336, 0.0617842935025692, 0.010043615475296974, 0.026673078536987305, -0.026173587888479233, -0.009911483153700829, 0.015311145223677158, -0.003602897049859166, 0.03285594657063484, 0.05120893567800522, -0.05628516525030136, -0.0017249450320377946, 0.017991093918681145, -0.02973698079586029, -0.03030944988131523, 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937 | 937 | ['Michael R. Smith', 'Tony Martinez', 'Michael Gashler'] | 1406.2235v1 | Collaborative filtering is used to recommend items to a user without
requiring a knowledge of the item itself and tends to outperform other
techniques. However, collaborative filtering suffers from the cold-start
problem, which occurs when an item has not yet been rated or a user has not
rated any items. Incorporating additional information, such as item or user
descriptions, into collaborative filtering can address the cold-start problem.
In this paper, we present a neural network model with latent input variables
(latent neural network or LNN) as a hybrid collaborative filtering technique
that addresses the cold-start problem. LNN outperforms a broad selection of
content-based filters (which make recommendations based on item descriptions)
and other hybrid approaches while maintaining the accuracy of state-of-the-art
collaborative filtering techniques. | A Hybrid Latent Variable Neural Network Model for Item Recommendation | 2,014 | http://arxiv.org/pdf/1406.2235v1 | Title Hybrid Latent Variable Neural Network Model Item Recommendation Summary Collaborative filtering used recommend item user without requiring knowledge item tends outperform technique However collaborative filtering suffers coldstart problem occurs item yet rated user rated item Incorporating additional information item user description collaborative filtering address coldstart problem paper present neural network model latent input variable latent neural network LNN hybrid collaborative filtering technique address coldstart problem LNN outperforms broad selection contentbased filter make recommendation based item description hybrid approach maintaining accuracy stateoftheart collaborative filtering technique 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 | [0.02611149288713932, -0.021026501432061195, 0.0049522374756634235, 0.005302788689732552, -0.01011070515960455, -0.0007123907562345266, 0.027500642463564873, 0.0023371907882392406, -0.03531720116734505, -0.013227098621428013, -0.01252741925418377, 0.003520858706906438, -0.00421498715877533, 0.0906575471162796, -0.0026563461869955063, -0.026773104444146156, -0.018011655658483505, 0.04233719781041145, -0.009176347404718399, -0.012993896380066872, 0.025482190772891045, 0.0207226425409317, 0.02790438011288643, 0.009138763882219791, 0.02491900697350502, 0.0116707943379879, -0.013689031824469566, -0.03643019497394562, -0.011080913245677948, 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938 | 938 | ['Tapani Raiko', 'Mathias Berglund', 'Guillaume Alain', 'Laurent Dinh'] | 1406.2989v3 | Stochastic binary hidden units in a multi-layer perceptron (MLP) network give
at least three potential benefits when compared to deterministic MLP networks.
(1) They allow to learn one-to-many type of mappings. (2) They can be used in
structured prediction problems, where modeling the internal structure of the
output is important. (3) Stochasticity has been shown to be an excellent
regularizer, which makes generalization performance potentially better in
general. However, training stochastic networks is considerably more difficult.
We study training using M samples of hidden activations per input. We show that
the case M=1 leads to a fundamentally different behavior where the network
tries to avoid stochasticity. We propose two new estimators for the training
gradient and propose benchmark tests for comparing training algorithms. Our
experiments confirm that training stochastic networks is difficult and show
that the proposed two estimators perform favorably among all the five known
estimators. | Techniques for Learning Binary Stochastic Feedforward Neural Networks | 2,014 | http://arxiv.org/pdf/1406.2989v3 | Title Techniques Learning Binary Stochastic Feedforward Neural Networks Summary Stochastic binary hidden unit multilayer perceptron MLP network give least three potential benefit compared deterministic MLP network 1 allow learn onetomany type mapping 2 used structured prediction problem modeling internal structure output important 3 Stochasticity shown excellent regularizer make generalization performance potentially better general However training stochastic network considerably difficult study training using sample hidden activation per input show case M1 lead fundamentally different behavior network try avoid stochasticity propose two new estimator training gradient propose benchmark test comparing training algorithm experiment confirm training stochastic network difficult show proposed two estimator perform favorably among five known estimator 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 | [0.001215980271808803, 0.04254048317670822, 0.00035428415867500007, 0.008588207885622978, -0.002210453851148486, -0.028770996257662773, 0.05483630299568176, 0.014176048338413239, 0.01496941689401865, 0.004779358860105276, -0.0037284898571670055, -0.003071877406910062, -0.0034792001824826, 0.07418079674243927, 0.06136280298233032, 0.027208266779780388, 0.02350589632987976, 0.00930844061076641, -0.02182118035852909, 0.0032796813175082207, -0.00036842271219938993, -0.04016788303852081, 0.003963332623243332, 0.028301304206252098, 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939 | 939 | ['Philip de Chazal', 'Jonathan Tapson', 'André van Schaik'] | 1406.3100v1 | We present an alternative to the pseudo-inverse method for determining the
hidden to output weight values for Extreme Learning Machines performing
classification tasks. The method is based on linear discriminant analysis and
provides Bayes optimal single point estimates for the weight values. | Learning ELM network weights using linear discriminant analysis | 2,014 | http://arxiv.org/pdf/1406.3100v1 | Title Learning ELM network weight using linear discriminant analysis Summary present alternative pseudoinverse method determining hidden output weight value Extreme Learning Machines performing classification task method based linear discriminant analysis provides Bayes optimal single point estimate weight value 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 | [-0.047780271619558334, -0.05507497489452362, 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940 | 940 | ['Kyunghyun Cho', 'Yoshua Bengio'] | 1406.7362v1 | Many state-of-the-art results obtained with deep networks are achieved with
the largest models that could be trained, and if more computation power was
available, we might be able to exploit much larger datasets in order to improve
generalization ability. Whereas in learning algorithms such as decision trees
the ratio of capacity (e.g., the number of parameters) to computation is very
favorable (up to exponentially more parameters than computation), the ratio is
essentially 1 for deep neural networks. Conditional computation has been
proposed as a way to increase the capacity of a deep neural network without
increasing the amount of computation required, by activating some parameters
and computation "on-demand", on a per-example basis. In this note, we propose a
novel parametrization of weight matrices in neural networks which has the
potential to increase up to exponentially the ratio of the number of parameters
to computation. The proposed approach is based on turning on some parameters
(weight matrices) when specific bit patterns of hidden unit activations are
obtained. In order to better control for the overfitting that might result, we
propose a parametrization that is tree-structured, where each node of the tree
corresponds to a prefix of a sequence of sign bits, or gating units, associated
with hidden units. | Exponentially Increasing the Capacity-to-Computation Ratio for
Conditional Computation in Deep Learning | 2,014 | http://arxiv.org/pdf/1406.7362v1 | Title Exponentially Increasing CapacitytoComputation Ratio Conditional Computation Deep Learning Summary Many stateoftheart result obtained deep network achieved largest model could trained computation power available might able exploit much larger datasets order improve generalization ability Whereas learning algorithm decision tree ratio capacity eg number parameter computation favorable exponentially parameter computation ratio essentially 1 deep neural network Conditional computation proposed way increase capacity deep neural network without increasing amount computation required activating parameter computation ondemand perexample basis note propose novel parametrization weight matrix neural network potential increase exponentially ratio number parameter computation proposed approach based turning parameter weight matrix specific bit pattern hidden unit activation obtained order better control overfitting might result propose parametrization treestructured node tree corresponds prefix sequence sign bit gating unit associated hidden unit 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 | [0.010267950594425201, 0.051133278757333755, -0.03516475856304169, 0.03024945966899395, 0.03197839483618736, 0.0034251566976308823, 0.007188150193542242, -0.004338458180427551, -0.05059635266661644, 0.00571111124008894, -0.004952612798660994, -0.012782791629433632, -0.024504205211997032, 0.03483777865767479, -0.004086826927959919, -0.026722833514213562, 0.0040726917795836926, 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941 | 941 | ['Taichi Kiwaki'] | 1505.02462v3 | We present a layered Boltzmann machine (BM) that can better exploit the
advantages of a distributed representation. It is widely believed that deep BMs
(DBMs) have far greater representational power than its shallow counterpart,
restricted Boltzmann machines (RBMs). However, this expectation on the
supremacy of DBMs over RBMs has not ever been validated in a theoretical
fashion. In this paper, we provide both theoretical and empirical evidences
that the representational power of DBMs can be actually rather limited in
taking advantages of distributed representations. We propose an approximate
measure for the representational power of a BM regarding to the efficiency of a
distributed representation. With this measure, we show a surprising fact that
DBMs can make inefficient use of distributed representations. Based on these
observations, we propose an alternative BM architecture, which we dub soft-deep
BMs (sDBMs). We show that sDBMs can more efficiently exploit the distributed
representations in terms of the measure. Experiments demonstrate that sDBMs
outperform several state-of-the-art models, including DBMs, in generative tasks
on binarized MNIST and Caltech-101 silhouettes. | Soft-Deep Boltzmann Machines | 2,015 | http://arxiv.org/pdf/1505.02462v3 | Title SoftDeep Boltzmann Machines Summary present layered Boltzmann machine BM better exploit advantage distributed representation widely believed deep BMs DBMs far greater representational power shallow counterpart restricted Boltzmann machine RBMs However expectation supremacy DBMs RBMs ever validated theoretical fashion paper provide theoretical empirical evidence representational power DBMs actually rather limited taking advantage distributed representation propose approximate measure representational power BM regarding efficiency distributed representation measure show surprising fact DBMs make inefficient use distributed representation Based observation propose alternative BM architecture dub softdeep BMs sDBMs show sDBMs efficiently exploit distributed representation term measure Experiments demonstrate sDBMs outperform several stateoftheart model including DBMs generative task binarized MNIST Caltech101 silhouette 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 | [-0.02861926145851612, -0.03383118659257889, -0.03731789067387581, 0.018691247329115868, -0.02108532004058361, 4.4967338908463717e-05, 0.09842293709516525, -0.01948707178235054, -0.058954812586307526, 0.01275632530450821, -0.029230007901787758, 0.003911531995981932, 0.00564573984593153, 0.05199882388114929, 0.032638389617204666, -0.08107413351535797, 0.008361130952835083, 0.01916617900133133, -0.012635885737836361, -0.05245571956038475, 0.034536756575107574, 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942 | 942 | ['Yaroslav Ganin', 'Evgeniya Ustinova', 'Hana Ajakan', 'Pascal Germain', 'Hugo Larochelle', 'François Laviolette', 'Mario Marchand', 'Victor Lempitsky'] | 1505.07818v4 | We introduce a new representation learning approach for domain adaptation, in
which data at training and test time come from similar but different
distributions. Our approach is directly inspired by the theory on domain
adaptation suggesting that, for effective domain transfer to be achieved,
predictions must be made based on features that cannot discriminate between the
training (source) and test (target) domains. The approach implements this idea
in the context of neural network architectures that are trained on labeled data
from the source domain and unlabeled data from the target domain (no labeled
target-domain data is necessary). As the training progresses, the approach
promotes the emergence of features that are (i) discriminative for the main
learning task on the source domain and (ii) indiscriminate with respect to the
shift between the domains. We show that this adaptation behaviour can be
achieved in almost any feed-forward model by augmenting it with few standard
layers and a new gradient reversal layer. The resulting augmented architecture
can be trained using standard backpropagation and stochastic gradient descent,
and can thus be implemented with little effort using any of the deep learning
packages. We demonstrate the success of our approach for two distinct
classification problems (document sentiment analysis and image classification),
where state-of-the-art domain adaptation performance on standard benchmarks is
achieved. We also validate the approach for descriptor learning task in the
context of person re-identification application. | Domain-Adversarial Training of Neural Networks | 2,015 | http://arxiv.org/pdf/1505.07818v4 | Title DomainAdversarial Training Neural Networks Summary introduce new representation learning approach domain adaptation data training test time come similar different distribution approach directly inspired theory domain adaptation suggesting effective domain transfer achieved prediction must made based feature cannot discriminate training source test target domain approach implement idea context neural network architecture trained labeled data source domain unlabeled data target domain labeled targetdomain data necessary training progress approach promotes emergence feature discriminative main learning task source domain ii indiscriminate respect shift domain show adaptation behaviour achieved almost feedforward model augmenting standard layer new gradient reversal layer resulting augmented architecture trained using standard backpropagation stochastic gradient descent thus implemented little effort using deep learning package demonstrate success approach two distinct classification problem document sentiment analysis image classification stateoftheart domain adaptation performance standard benchmark achieved also validate approach descriptor learning task context person reidentification 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 | [0.019201599061489105, 0.034077271819114685, -0.026657868176698685, 0.03172437846660614, 0.0033702924847602844, -0.0048766229301691055, 0.042705897241830826, 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943 | 943 | ['David Balduzzi'] | 1604.01952v1 | Methods from convex optimization are widely used as building blocks for deep
learning algorithms. However, the reasons for their empirical success are
unclear, since modern convolutional networks (convnets), incorporating
rectifier units and max-pooling, are neither smooth nor convex. Standard
guarantees therefore do not apply. This paper provides the first convergence
rates for gradient descent on rectifier convnets. The proof utilizes the
particular structure of rectifier networks which consists in binary
active/inactive gates applied on top of an underlying linear network. The
approach generalizes to max-pooling, dropout and maxout. In other words, to
precisely the neural networks that perform best empirically. The key step is to
introduce gated games, an extension of convex games with similar convergence
properties that capture the gating function of rectifiers. The main result is
that rectifier convnets converge to a critical point at a rate controlled by
the gated-regret of the units in the network. Corollaries of the main result
include: (i) a game-theoretic description of the representations learned by a
neural network; (ii) a logarithmic-regret algorithm for training neural nets;
and (iii) a formal setting for analyzing conditional computation in neural nets
that can be applied to recently developed models of attention. | Deep Online Convex Optimization with Gated Games | 2,016 | http://arxiv.org/pdf/1604.01952v1 | Title Deep Online Convex Optimization Gated Games Summary Methods convex optimization widely used building block deep learning algorithm However reason empirical success unclear since modern convolutional network convnets incorporating rectifier unit maxpooling neither smooth convex Standard guarantee therefore apply paper provides first convergence rate gradient descent rectifier convnets proof utilizes particular structure rectifier network consists binary activeinactive gate applied top underlying linear network approach generalizes maxpooling dropout maxout word precisely neural network perform best empirically key step introduce gated game extension convex game similar convergence property capture gating function rectifier main result rectifier convnets converge critical point rate controlled gatedregret unit network Corollaries main result include gametheoretic description representation learned neural network ii logarithmicregret algorithm training neural net iii formal setting analyzing conditional computation neural net applied recently developed model attention 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 | [-0.0025042917113751173, 0.028802013024687767, -0.022399017587304115, 0.042485352605581284, -0.027306577190756798, -0.037745244801044464, 0.010233091190457344, -0.01977705955505371, -0.05484988912940025, 0.023186886683106422, 0.008752051740884781, -0.029419537633657455, 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944 | 944 | ['Artit Wangperawong', 'Cyrille Brun', 'Olav Laudy', 'Rujikorn Pavasuthipaisit'] | 1604.05377v1 | Customer temporal behavioral data was represented as images in order to
perform churn prediction by leveraging deep learning architectures prominent in
image classification. Supervised learning was performed on labeled data of over
6 million customers using deep convolutional neural networks, which achieved an
AUC of 0.743 on the test dataset using no more than 12 temporal features for
each customer. Unsupervised learning was conducted using autoencoders to better
understand the reasons for customer churn. Images that maximally activate the
hidden units of an autoencoder trained with churned customers reveal ample
opportunities for action to be taken to prevent churn among strong data, no
voice users. | Churn analysis using deep convolutional neural networks and autoencoders | 2,016 | http://arxiv.org/pdf/1604.05377v1 | Title Churn analysis using deep convolutional neural network autoencoders Summary Customer temporal behavioral data represented image order perform churn prediction leveraging deep learning architecture prominent image classification Supervised learning performed labeled data 6 million customer using deep convolutional neural network achieved AUC 0743 test dataset using 12 temporal feature customer Unsupervised learning conducted using autoencoders better understand reason customer churn Images maximally activate hidden unit autoencoder trained churned customer reveal ample opportunity action taken prevent churn among strong data voice user 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 | [0.017682963982224464, 0.06825803965330124, -0.04299362748861313, -0.028144652023911476, 0.022813091054558754, 0.02680245414376259, 0.0631818100810051, -0.009304946288466454, -0.04799279198050499, 0.01680714637041092, 0.015827804803848267, 0.015125793404877186, -0.004157013259828091, 0.09472476691007614, -0.035870328545570374, 0.010017276741564274, -0.005474897567182779, 0.026808694005012512, 0.009360522963106632, 0.008196063339710236, -0.011007188819348812, -0.004717827774584293, 0.0023776963353157043, 0.044583454728126526, -0.07375650852918625, -0.012919840402901173, 0.006563018076121807, -0.003632892854511738, 0.008652733638882637, -0.03847778961062431, 0.07608717679977417, -0.025885427370667458, 0.026364343240857124, 0.04403035715222359, 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945 | 945 | ['Chee Chun Gan', 'Gerard Learmonth'] | 1604.06730v1 | ICU mortality scoring systems attempt to predict patient mortality using
predictive models with various clinical predictors. Examples of such systems
are APACHE, SAPS and MPM. However, most such scoring systems do not actively
look for and include interaction terms, despite physicians intuitively taking
such interactions into account when making a diagnosis. One barrier to
including such terms in predictive models is the difficulty of using most
variable selection methods in high-dimensional datasets. A genetic algorithm
framework for variable selection with logistic regression models is used to
search for two-way interaction terms in a clinical dataset of adult ICU
patients, with separate models being built for each category of diagnosis upon
admittance to the ICU. The models had good discrimination across all
categories, with a weighted average AUC of 0.84 (>0.90 for several categories)
and the genetic algorithm was able to find several significant interaction
terms, which may be able to provide greater insight into mortality prediction
for health practitioners. The GA selected models had improved performance
against stepwise selection and random forest models, and provides greater
flexibility in terms of variable selection by being able to optimize over any
modeler-defined model performance metric instead of a specific variable
importance metric. | Developing an ICU scoring system with interaction terms using a genetic
algorithm | 2,016 | http://arxiv.org/pdf/1604.06730v1 | Title Developing ICU scoring system interaction term using genetic algorithm Summary ICU mortality scoring system attempt predict patient mortality using predictive model various clinical predictor Examples system APACHE SAPS MPM However scoring system actively look include interaction term despite physician intuitively taking interaction account making diagnosis One barrier including term predictive model difficulty using variable selection method highdimensional datasets genetic algorithm framework variable selection logistic regression model used search twoway interaction term clinical dataset adult ICU patient separate model built category diagnosis upon admittance ICU model good discrimination across category weighted average AUC 084 090 several category genetic algorithm able find several significant interaction term may able provide greater insight mortality prediction health practitioner GA selected model improved performance stepwise selection random forest model provides greater flexibility term variable selection able optimize modelerdefined model performance metric instead specific variable importance metric 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 | [0.022869685664772987, 0.024017179384827614, -0.013866687193512917, -0.053549669682979584, 0.0076678358018398285, 0.004625585861504078, 0.04362069442868233, 0.029417937621474266, -0.006000105757266283, 0.02139606885612011, 0.04166088625788689, -0.030953554436564445, 0.06068505719304085, 0.11424346268177032, -0.02872847206890583, 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946 | 946 | ['Henry Z. Lo', 'Kevin Amaral', 'Wei Ding'] | 1604.07796v1 | One of the difficulties of training deep neural networks is caused by
improper scaling between layers. Scaling issues introduce exploding / gradient
problems, and have typically been addressed by careful scale-preserving
initialization. We investigate the value of preserving scale, or isometry,
beyond the initial weights. We propose two methods of maintaing isometry, one
exact and one stochastic. Preliminary experiments show that for both
determinant and scale-normalization effectively speeds up learning. Results
suggest that isometry is important in the beginning of learning, and
maintaining it leads to faster learning. | Scale Normalization | 2,016 | http://arxiv.org/pdf/1604.07796v1 | Title Scale Normalization Summary One difficulty training deep neural network caused improper scaling layer Scaling issue introduce exploding gradient problem typically addressed careful scalepreserving initialization investigate value preserving scale isometry beyond initial weight propose two method maintaing isometry one exact one stochastic Preliminary experiment show determinant scalenormalization effectively speed learning Results suggest isometry important beginning learning maintaining lead faster 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 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947 | 947 | ['Ludovic Arnold', 'Yann Ollivier'] | 1212.1524v2 | When using deep, multi-layered architectures to build generative models of
data, it is difficult to train all layers at once. We propose a layer-wise
training procedure admitting a performance guarantee compared to the global
optimum. It is based on an optimistic proxy of future performance, the best
latent marginal. We interpret auto-encoders in this setting as generative
models, by showing that they train a lower bound of this criterion. We test the
new learning procedure against a state of the art method (stacked RBMs), and
find it to improve performance. Both theory and experiments highlight the
importance, when training deep architectures, of using an inference model (from
data to hidden variables) richer than the generative model (from hidden
variables to data). | Layer-wise learning of deep generative models | 2,012 | http://arxiv.org/pdf/1212.1524v2 | Title Layerwise learning deep generative model Summary using deep multilayered architecture build generative model data difficult train layer propose layerwise training procedure admitting performance guarantee compared global optimum based optimistic proxy future performance best latent marginal interpret autoencoders setting generative model showing train lower bound criterion test new learning procedure state art method stacked RBMs find improve performance theory experiment highlight importance training deep architecture using inference model data hidden variable richer generative model hidden variable 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 | [-0.0313352532684803, 0.06620785593986511, -0.0353248156607151, 0.013033506460487843, 0.02999725565314293, -0.01258664671331644, 0.06161899492144585, -0.03296348825097084, -0.048237282782793045, 0.03418130800127983, 0.019652310758829117, 0.03305657207965851, -0.01949579082429409, 0.0628538578748703, 0.055762916803359985, -0.05799859017133713, -0.030896402895450592, 0.0070212422870099545, 0.013478951528668404, -0.04126232489943504, -0.004319686442613602, -0.0018347027944400907, 0.031004467979073524, 0.03323328122496605, -0.10799888521432877, 0.0003840354329440743, -0.008381620980799198, 0.0005838721990585327, -0.010344259440898895, -0.04510046914219856, 0.00277985748834908, 0.02446942776441574, 0.015765292569994926, 0.007428586017340422, 2.715013124543475e-06, -0.014269866980612278, 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948 | 948 | ['Miguel Á. Carreira-Perpiñán', 'Weiran Wang'] | 1212.5921v1 | In science and engineering, intelligent processing of complex signals such as
images, sound or language is often performed by a parameterized hierarchy of
nonlinear processing layers, sometimes biologically inspired. Hierarchical
systems (or, more generally, nested systems) offer a way to generate complex
mappings using simple stages. Each layer performs a different operation and
achieves an ever more sophisticated representation of the input, as, for
example, in an deep artificial neural network, an object recognition cascade in
computer vision or a speech front-end processing. Joint estimation of the
parameters of all the layers and selection of an optimal architecture is widely
considered to be a difficult numerical nonconvex optimization problem,
difficult to parallelize for execution in a distributed computation
environment, and requiring significant human expert effort, which leads to
suboptimal systems in practice. We describe a general mathematical strategy to
learn the parameters and, to some extent, the architecture of nested systems,
called the method of auxiliary coordinates (MAC). This replaces the original
problem involving a deeply nested function with a constrained problem involving
a different function in an augmented space without nesting. The constrained
problem may be solved with penalty-based methods using alternating optimization
over the parameters and the auxiliary coordinates. MAC has provable
convergence, is easy to implement reusing existing algorithms for single
layers, can be parallelized trivially and massively, applies even when
parameter derivatives are not available or not desirable, and is competitive
with state-of-the-art nonlinear optimizers even in the serial computation
setting, often providing reasonable models within a few iterations. | Distributed optimization of deeply nested systems | 2,012 | http://arxiv.org/pdf/1212.5921v1 | Title Distributed optimization deeply nested system Summary science engineering intelligent processing complex signal image sound language often performed parameterized hierarchy nonlinear processing layer sometimes biologically inspired Hierarchical system generally nested system offer way generate complex mapping using simple stage layer performs different operation achieves ever sophisticated representation input example deep artificial neural network object recognition cascade computer vision speech frontend processing Joint estimation parameter layer selection optimal architecture widely considered difficult numerical nonconvex optimization problem difficult parallelize execution distributed computation environment requiring significant human expert effort lead suboptimal system practice describe general mathematical strategy learn parameter extent architecture nested system called method auxiliary coordinate MAC replaces original problem involving deeply nested function constrained problem involving different function augmented space without nesting constrained problem may solved penaltybased method using alternating optimization parameter auxiliary coordinate MAC provable convergence easy implement reusing existing algorithm single layer parallelized trivially massively applies even parameter derivative available desirable competitive stateoftheart nonlinear optimizers even serial computation setting often providing reasonable model within iteration 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 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949 | 949 | ['Xiaozhao Zhao', 'Yuexian Hou', 'Qian Yu', 'Dawei Song', 'Wenjie Li'] | 1302.3931v7 | Typical dimensionality reduction methods focus on directly reducing the
number of random variables while retaining maximal variations in the data. In
this paper, we consider the dimensionality reduction in parameter spaces of
binary multivariate distributions. We propose a general
Confident-Information-First (CIF) principle to maximally preserve parameters
with confident estimates and rule out unreliable or noisy parameters. Formally,
the confidence of a parameter can be assessed by its Fisher information, which
establishes a connection with the inverse variance of any unbiased estimate for
the parameter via the Cram\'{e}r-Rao bound. We then revisit Boltzmann machines
(BM) and theoretically show that both single-layer BM without hidden units
(SBM) and restricted BM (RBM) can be solidly derived using the CIF principle.
This can not only help us uncover and formalize the essential parts of the
target density that SBM and RBM capture, but also suggest that the deep neural
network consisting of several layers of RBM can be seen as the layer-wise
application of CIF. Guided by the theoretical analysis, we develop a
sample-specific CIF-based contrastive divergence (CD-CIF) algorithm for SBM and
a CIF-based iterative projection procedure (IP) for RBM. Both CD-CIF and IP are
studied in a series of density estimation experiments. | Understanding Boltzmann Machine and Deep Learning via A Confident
Information First Principle | 2,013 | http://arxiv.org/pdf/1302.3931v7 | Title Understanding Boltzmann Machine Deep Learning via Confident Information First Principle Summary Typical dimensionality reduction method focus directly reducing number random variable retaining maximal variation data paper consider dimensionality reduction parameter space binary multivariate distribution propose general ConfidentInformationFirst CIF principle maximally preserve parameter confident estimate rule unreliable noisy parameter Formally confidence parameter assessed Fisher information establishes connection inverse variance unbiased estimate parameter via CramerRao bound revisit Boltzmann machine BM theoretically show singlelayer BM without hidden unit SBM restricted BM RBM solidly derived using CIF principle help u uncover formalize essential part target density SBM RBM capture also suggest deep neural network consisting several layer RBM seen layerwise application CIF Guided theoretical analysis develop samplespecific CIFbased contrastive divergence CDCIF algorithm SBM CIFbased iterative projection procedure IP RBM CDCIF IP studied series density estimation experiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.025464095175266266, -0.013774629682302475, -0.042159441858530045, 0.027779314666986465, -0.016197023913264275, -0.026659326627850533, 0.06105431169271469, -0.004128093831241131, -0.06401896476745605, 0.057078924030065536, 0.012049119919538498, -0.004306909162551165, 0.008740131743252277, 0.07568143308162689, 0.03475392982363701, -0.03162836655974388, 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950 | 950 | ['Vittorio Latorre', 'David Yang Gao'] | 1302.4141v1 | Radial Basis Functions Neural Networks (RBFNNs) are tools widely used in
regression problems. One of their principal drawbacks is that the formulation
corresponding to the training with the supervision of both the centers and the
weights is a highly non-convex optimization problem, which leads to some
fundamentally difficulties for traditional optimization theory and methods.
This paper presents a generalized canonical duality theory for solving this
challenging problem. We demonstrate that by sequential canonical dual
transformations, the nonconvex optimization problem of the RBFNN can be
reformulated as a canonical dual problem (without duality gap). Both global
optimal solution and local extrema can be classified. Several applications to
one of the most used Radial Basis Functions, the Gaussian function, are
illustrated. Our results show that even for one-dimensional case, the global
minimizer of the nonconvex problem may not be the best solution to the RBFNNs,
and the canonical dual theory is a promising tool for solving general neural
networks training problems. | Canonical dual solutions to nonconvex radial basis neural network
optimization problem | 2,013 | http://arxiv.org/pdf/1302.4141v1 | Title Canonical dual solution nonconvex radial basis neural network optimization problem Summary Radial Basis Functions Neural Networks RBFNNs tool widely used regression problem One principal drawback formulation corresponding training supervision center weight highly nonconvex optimization problem lead fundamentally difficulty traditional optimization theory method paper present generalized canonical duality theory solving challenging problem demonstrate sequential canonical dual transformation nonconvex optimization problem RBFNN reformulated canonical dual problem without duality gap global optimal solution local extremum classified Several application one used Radial Basis Functions Gaussian function illustrated result show even onedimensional case global minimizer nonconvex problem may best solution RBFNNs canonical dual theory promising tool solving general neural network training 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 | [-0.014607925899326801, 0.024343496188521385, -0.02844329923391342, 0.040786631405353546, 0.01724657043814659, -0.012980449013411999, -0.0007429387187585235, -0.0013303601881489158, -0.040544718503952026, 0.007509095128625631, -0.02854190394282341, -0.01185290701687336, -0.004880169872194529, 0.009530519135296345, 0.018031015992164612, -0.007347288075834513, -0.0225827656686306, -0.016424544155597687, -0.027928641065955162, -0.026085827499628067, 0.026607077568769455, -0.045429542660713196, -0.0005040611140429974, 0.004500976763665676, 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951 | 951 | ['Guido Montúfar', 'Razvan Pascanu', 'Kyunghyun Cho', 'Yoshua Bengio'] | 1402.1869v2 | We study the complexity of functions computable by deep feedforward neural
networks with piecewise linear activations in terms of the symmetries and the
number of linear regions that they have. Deep networks are able to sequentially
map portions of each layer's input-space to the same output. In this way, deep
models compute functions that react equally to complicated patterns of
different inputs. The compositional structure of these functions enables them
to re-use pieces of computation exponentially often in terms of the network's
depth. This paper investigates the complexity of such compositional maps and
contributes new theoretical results regarding the advantage of depth for neural
networks with piecewise linear activation functions. In particular, our
analysis is not specific to a single family of models, and as an example, we
employ it for rectifier and maxout networks. We improve complexity bounds from
pre-existing work and investigate the behavior of units in higher layers. | On the Number of Linear Regions of Deep Neural Networks | 2,014 | http://arxiv.org/pdf/1402.1869v2 | Title Number Linear Regions Deep Neural Networks Summary study complexity function computable deep feedforward neural network piecewise linear activation term symmetry number linear region Deep network able sequentially map portion layer inputspace output way deep model compute function react equally complicated pattern different input compositional structure function enables reuse piece computation exponentially often term network depth paper investigates complexity compositional map contributes new theoretical result regarding advantage depth neural network piecewise linear activation function particular analysis specific single family model example employ rectifier maxout network improve complexity bound preexisting work investigate behavior unit higher layer 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 | [-0.015910517424345016, 0.026597000658512115, -0.04318666458129883, 0.04936468228697777, -0.03697515279054642, -0.014711592346429825, 0.06089446693658829, -0.03369078412652016, -0.035225607454776764, 0.00869825016707182, 0.015742894262075424, -0.003113511949777603, 0.009672436863183975, 0.09304176270961761, 0.016840938478708267, -0.03618595004081726, -0.0003683151735458523, 0.027657389640808105, -0.04787427932024002, -0.03785525634884834, -0.022013619542121887, -0.030672596767544746, -0.008840964175760746, 0.010452666319906712, -0.02422930859029293, 0.021194878965616226, 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952 | 952 | ['Guido Montufar', 'Nihat Ay', 'Keyan Ghazi-Zahedi'] | 1402.3346v3 | Conditional restricted Boltzmann machines are undirected stochastic neural
networks with a layer of input and output units connected bipartitely to a
layer of hidden units. These networks define models of conditional probability
distributions on the states of the output units given the states of the input
units, parametrized by interaction weights and biases. We address the
representational power of these models, proving results their ability to
represent conditional Markov random fields and conditional distributions with
restricted supports, the minimal size of universal approximators, the maximal
model approximation errors, and on the dimension of the set of representable
conditional distributions. We contribute new tools for investigating
conditional probability models, which allow us to improve the results that can
be derived from existing work on restricted Boltzmann machine probability
models. | Geometry and Expressive Power of Conditional Restricted Boltzmann
Machines | 2,014 | http://arxiv.org/pdf/1402.3346v3 | Title Geometry Expressive Power Conditional Restricted Boltzmann Machines Summary Conditional restricted Boltzmann machine undirected stochastic neural network layer input output unit connected bipartitely layer hidden unit network define model conditional probability distribution state output unit given state input unit parametrized interaction weight bias address representational power model proving result ability represent conditional Markov random field conditional distribution restricted support minimal size universal approximators maximal model approximation error dimension set representable conditional distribution contribute new tool investigating conditional probability model allow u improve result derived existing work restricted Boltzmann machine probability 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 | [-0.03153286874294281, -0.010947375558316708, -0.03120209462940693, -0.011755645275115967, -0.03535754978656769, -0.02984841912984848, 0.02683355286717415, -0.03830824792385101, -0.020294515416026115, -0.00038627846515737474, 0.021498078480362892, -0.0029827968683093786, 0.0050195432268083096, 0.047754984349012375, 0.04439115524291992, -0.08372075110673904, 0.008405383676290512, -0.007966289296746254, 0.004309217445552349, -0.042510952800512314, 0.031167034059762955, -0.03144162520766258, 0.007071789354085922, 0.01483867783099413, -0.05465251952409744, 0.028844721615314484, -0.013457532972097397, 0.011068698018789291, 0.004507381469011307, -0.012277054600417614, -0.01489204540848732, 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953 | 953 | ['Yingbo Zhou', 'Devansh Arpit', 'Ifeoma Nwogu', 'Venu Govindaraju'] | 1405.1380v4 | Traditionally, when generative models of data are developed via deep
architectures, greedy layer-wise pre-training is employed. In a well-trained
model, the lower layer of the architecture models the data distribution
conditional upon the hidden variables, while the higher layers model the hidden
distribution prior. But due to the greedy scheme of the layerwise training
technique, the parameters of lower layers are fixed when training higher
layers. This makes it extremely challenging for the model to learn the hidden
distribution prior, which in turn leads to a suboptimal model for the data
distribution. We therefore investigate joint training of deep autoencoders,
where the architecture is viewed as one stack of two or more single-layer
autoencoders. A single global reconstruction objective is jointly optimized,
such that the objective for the single autoencoders at each layer acts as a
local, layer-level regularizer. We empirically evaluate the performance of this
joint training scheme and observe that it not only learns a better data model,
but also learns better higher layer representations, which highlights its
potential for unsupervised feature learning. In addition, we find that the
usage of regularizations in the joint training scheme is crucial in achieving
good performance. In the supervised setting, joint training also shows superior
performance when training deeper models. The joint training framework can thus
provide a platform for investigating more efficient usage of different types of
regularizers, especially in light of the growing volumes of available unlabeled
data. | Is Joint Training Better for Deep Auto-Encoders? | 2,014 | http://arxiv.org/pdf/1405.1380v4 | Title Joint Training Better Deep AutoEncoders Summary Traditionally generative model data developed via deep architecture greedy layerwise pretraining employed welltrained model lower layer architecture model data distribution conditional upon hidden variable higher layer model hidden distribution prior due greedy scheme layerwise training technique parameter lower layer fixed training higher layer make extremely challenging model learn hidden distribution prior turn lead suboptimal model data distribution therefore investigate joint training deep autoencoders architecture viewed one stack two singlelayer autoencoders single global reconstruction objective jointly optimized objective single autoencoders layer act local layerlevel regularizer empirically evaluate performance joint training scheme observe learns better data model also learns better higher layer representation highlight potential unsupervised feature learning addition find usage regularization joint training scheme crucial achieving good performance supervised setting joint training also show superior performance training deeper model joint training framework thus provide platform investigating efficient usage different type regularizers especially light growing volume available unlabeled 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 | [-0.012143359519541264, 0.09947140514850616, -0.027615344151854515, 0.04559614136815071, 0.035100433975458145, -0.005233585834503174, 0.06767681241035461, 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954 | 954 | ['Bharath Ramsundar', 'Steven Kearnes', 'Patrick Riley', 'Dale Webster', 'David Konerding', 'Vijay Pande'] | 1502.02072v1 | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | Massively Multitask Networks for Drug Discovery | 2,015 | http://arxiv.org/pdf/1502.02072v1 | Title Massively Multitask Networks Drug Discovery Summary Massively multitask neural architecture provide learning framework drug discovery synthesizes information many distinct biological source train architecture scale gather large amount data public source create dataset nearly 40 million measurement across 200 biological target investigate several aspect multitask framework performing series empirical study obtain interesting result 1 massively multitask network obtain predictive accuracy significantly better singletask method 2 predictive power multitask network improves additional task data added 3 total amount data total number task contribute significantly multitask improvement 4 multitask network afford limited transferability task training set result underscore need greater data sharing algorithmic innovation accelerate drug discovery process 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 | [-0.0063495696522295475, 0.04975642263889313, -0.04969564825296402, -0.02927136979997158, 0.023149767890572548, 0.01348835974931717, 0.05887255072593689, -0.004094231873750687, 0.03089132159948349, 0.031276870518922806, -0.04117029160261154, 0.006186211481690407, -0.054234955459833145, 0.09641271084547043, 0.041695427149534225, 0.04384585842490196, 0.057817719876766205, 0.016000911593437195, -0.017163332551717758, -0.04464021697640419, 0.016942283138632774, 0.0015348994638770819, 0.0109487259760499, 0.04767027497291565, 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955 | 955 | ['Junyoung Chung', 'Caglar Gulcehre', 'Kyunghyun Cho', 'Yoshua Bengio'] | 1502.02367v4 | In this work, we propose a novel recurrent neural network (RNN) architecture.
The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of
stacking multiple recurrent layers by allowing and controlling signals flowing
from upper recurrent layers to lower layers using a global gating unit for each
pair of layers. The recurrent signals exchanged between layers are gated
adaptively based on the previous hidden states and the current input. We
evaluated the proposed GF-RNN with different types of recurrent units, such as
tanh, long short-term memory and gated recurrent units, on the tasks of
character-level language modeling and Python program evaluation. Our empirical
evaluation of different RNN units, revealed that in both tasks, the GF-RNN
outperforms the conventional approaches to build deep stacked RNNs. We suggest
that the improvement arises because the GF-RNN can adaptively assign different
layers to different timescales and layer-to-layer interactions (including the
top-down ones which are not usually present in a stacked RNN) by learning to
gate these interactions. | Gated Feedback Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1502.02367v4 | Title Gated Feedback Recurrent Neural Networks Summary work propose novel recurrent neural network RNN architecture proposed RNN gatedfeedback RNN GFRNN extends existing approach stacking multiple recurrent layer allowing controlling signal flowing upper recurrent layer lower layer using global gating unit pair layer recurrent signal exchanged layer gated adaptively based previous hidden state current input evaluated proposed GFRNN different type recurrent unit tanh long shortterm memory gated recurrent unit task characterlevel language modeling Python program evaluation empirical evaluation different RNN unit revealed task GFRNN outperforms conventional approach build deep stacked RNNs suggest improvement arises GFRNN adaptively assign different layer different timescales layertolayer interaction including topdown one usually present stacked RNN learning gate interaction 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 | [0.010986789129674435, 0.02426208183169365, 0.015270414762198925, 0.050046589225530624, -0.002130315639078617, -0.008288975805044174, 0.014075572602450848, -0.009453745558857918, -0.006841693539172411, -0.013927253894507885, -0.034967657178640366, -0.0526738166809082, 0.049283236265182495, 0.05515960603952408, 0.0139584019780159, -0.06479386240243912, 0.035285789519548416, -0.004410734865814447, -0.014012116007506847, 0.0002946510212495923, 0.04116106778383255, -0.0037544085644185543, -0.013863041996955872, 0.037919722497463226, 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956 | 956 | ['Suyog Gupta', 'Ankur Agrawal', 'Kailash Gopalakrishnan', 'Pritish Narayanan'] | 1502.02551v1 | Training of large-scale deep neural networks is often constrained by the
available computational resources. We study the effect of limited precision
data representation and computation on neural network training. Within the
context of low-precision fixed-point computations, we observe the rounding
scheme to play a crucial role in determining the network's behavior during
training. Our results show that deep networks can be trained using only 16-bit
wide fixed-point number representation when using stochastic rounding, and
incur little to no degradation in the classification accuracy. We also
demonstrate an energy-efficient hardware accelerator that implements
low-precision fixed-point arithmetic with stochastic rounding. | Deep Learning with Limited Numerical Precision | 2,015 | http://arxiv.org/pdf/1502.02551v1 | Title Deep Learning Limited Numerical Precision Summary Training largescale deep neural network often constrained available computational resource study effect limited precision data representation computation neural network training Within context lowprecision fixedpoint computation observe rounding scheme play crucial role determining network behavior training result show deep network trained using 16bit wide fixedpoint number representation using stochastic rounding incur little degradation classification accuracy also demonstrate energyefficient hardware accelerator implement lowprecision fixedpoint arithmetic stochastic rounding 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 | [-0.049684226512908936, 0.01529654860496521, -0.028503447771072388, 0.026530034840106964, 0.023879896849393845, -0.025578662753105164, 0.06392213702201843, -0.020200366154313087, -0.06457959115505219, -0.0004043357621412724, -0.03333777189254761, -0.05165514349937439, 0.03641494736075401, 0.03819923475384712, 0.010586587712168694, -0.028852660208940506, 0.052370861172676086, 0.006142450962215662, -0.06672348082065582, -0.03791822865605354, 0.01816081628203392, -0.021476779133081436, -0.0031756048556417227, 0.023184340447187424, -0.031136363744735718, -0.025276916101574898, 0.02973271533846855, -0.015638889744877815, 0.034516338258981705, -0.01308431476354599, 0.03333132714033127, -0.03486344963312149, 0.043030839413404465, 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957 | 957 | ['Mathieu Germain', 'Karol Gregor', 'Iain Murray', 'Hugo Larochelle'] | 1502.03509v2 | There has been a lot of recent interest in designing neural network models to
estimate a distribution from a set of examples. We introduce a simple
modification for autoencoder neural networks that yields powerful generative
models. Our method masks the autoencoder's parameters to respect autoregressive
constraints: each input is reconstructed only from previous inputs in a given
ordering. Constrained this way, the autoencoder outputs can be interpreted as a
set of conditional probabilities, and their product, the full joint
probability. We can also train a single network that can decompose the joint
probability in multiple different orderings. Our simple framework can be
applied to multiple architectures, including deep ones. Vectorized
implementations, such as on GPUs, are simple and fast. Experiments demonstrate
that this approach is competitive with state-of-the-art tractable distribution
estimators. At test time, the method is significantly faster and scales better
than other autoregressive estimators. | MADE: Masked Autoencoder for Distribution Estimation | 2,015 | http://arxiv.org/pdf/1502.03509v2 | Title MADE Masked Autoencoder Distribution Estimation Summary lot recent interest designing neural network model estimate distribution set example introduce simple modification autoencoder neural network yield powerful generative model method mask autoencoders parameter respect autoregressive constraint input reconstructed previous input given ordering Constrained way autoencoder output interpreted set conditional probability product full joint probability also train single network decompose joint probability multiple different ordering simple framework applied multiple architecture including deep one Vectorized implementation GPUs simple fast Experiments demonstrate approach competitive stateoftheart tractable distribution estimator test time method significantly faster scale better autoregressive estimator 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 | [-0.022834602743387222, 0.09871013462543488, -0.05054556950926781, 0.019892383366823196, 0.013580773025751114, -0.03144886717200279, 0.051417261362075806, -0.011388998478651047, -0.0722624883055687, 0.0027885737363249063, 0.03075500577688217, 0.018086010590195656, 0.035774264484643936, 0.09353811293840408, 0.020069152116775513, -0.020940730348229408, -0.0325467623770237, -0.004933036398142576, -0.010723750106990337, -0.040936429053545, -0.030376652255654335, -0.01601056568324566, -0.0007778433500789106, 0.03816424682736397, -0.04527036473155022, 0.057532407343387604, 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958 | 958 | ['Sanjeev Arora', 'Rong Ge', 'Tengyu Ma', 'Ankur Moitra'] | 1503.00778v1 | Sparse coding is a basic task in many fields including signal processing,
neuroscience and machine learning where the goal is to learn a basis that
enables a sparse representation of a given set of data, if one exists. Its
standard formulation is as a non-convex optimization problem which is solved in
practice by heuristics based on alternating minimization. Re- cent work has
resulted in several algorithms for sparse coding with provable guarantees, but
somewhat surprisingly these are outperformed by the simple alternating
minimization heuristics. Here we give a general framework for understanding
alternating minimization which we leverage to analyze existing heuristics and
to design new ones also with provable guarantees. Some of these algorithms seem
implementable on simple neural architectures, which was the original motivation
of Olshausen and Field (1997a) in introducing sparse coding. We also give the
first efficient algorithm for sparse coding that works almost up to the
information theoretic limit for sparse recovery on incoherent dictionaries. All
previous algorithms that approached or surpassed this limit run in time
exponential in some natural parameter. Finally, our algorithms improve upon the
sample complexity of existing approaches. We believe that our analysis
framework will have applications in other settings where simple iterative
algorithms are used. | Simple, Efficient, and Neural Algorithms for Sparse Coding | 2,015 | http://arxiv.org/pdf/1503.00778v1 | Title Simple Efficient Neural Algorithms Sparse Coding Summary Sparse coding basic task many field including signal processing neuroscience machine learning goal learn basis enables sparse representation given set data one exists standard formulation nonconvex optimization problem solved practice heuristic based alternating minimization cent work resulted several algorithm sparse coding provable guarantee somewhat surprisingly outperformed simple alternating minimization heuristic give general framework understanding alternating minimization leverage analyze existing heuristic design new one also provable guarantee algorithm seem implementable simple neural architecture original motivation Olshausen Field 1997a introducing sparse coding also give first efficient algorithm sparse coding work almost information theoretic limit sparse recovery incoherent dictionary previous algorithm approached surpassed limit run time exponential natural parameter Finally algorithm improve upon sample complexity existing approach believe analysis framework application setting simple iterative algorithm used 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 | [-0.0086577283218503, 0.052953846752643585, 0.018169106915593147, 0.019068920984864235, 0.0068247378803789616, 0.01782236434519291, -0.007205440662801266, 0.0614737905561924, -0.05025531351566315, -0.006142206024378538, 0.0034828209318220615, -0.012042325921356678, 0.0408938005566597, 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959 | 959 | ['Thomas Unterthiner', 'Andreas Mayr', 'Günter Klambauer', 'Sepp Hochreiter'] | 1503.01445v1 | Everyday we are exposed to various chemicals via food additives, cleaning and
cosmetic products and medicines -- and some of them might be toxic. However
testing the toxicity of all existing compounds by biological experiments is
neither financially nor logistically feasible. Therefore the government
agencies NIH, EPA and FDA launched the Tox21 Data Challenge within the
"Toxicology in the 21st Century" (Tox21) initiative. The goal of this challenge
was to assess the performance of computational methods in predicting the
toxicity of chemical compounds. State of the art toxicity prediction methods
build upon specifically-designed chemical descriptors developed over decades.
Though Deep Learning is new to the field and was never applied to toxicity
prediction before, it clearly outperformed all other participating methods. In
this application paper we show that deep nets automatically learn features
resembling well-established toxicophores. In total, our Deep Learning approach
won both of the panel-challenges (nuclear receptors and stress response) as
well as the overall Grand Challenge, and thereby sets a new standard in tox
prediction. | Toxicity Prediction using Deep Learning | 2,015 | http://arxiv.org/pdf/1503.01445v1 | Title Toxicity Prediction using Deep Learning Summary Everyday exposed various chemical via food additive cleaning cosmetic product medicine might toxic However testing toxicity existing compound biological experiment neither financially logistically feasible Therefore government agency NIH EPA FDA launched Tox21 Data Challenge within Toxicology 21st Century Tox21 initiative goal challenge ass performance computational method predicting toxicity chemical compound State art toxicity prediction method build upon specificallydesigned chemical descriptor developed decade Though Deep Learning new field never applied toxicity prediction clearly outperformed participating method application paper show deep net automatically learn feature resembling wellestablished toxicophores total Deep Learning approach panelchallenges nuclear receptor stress response well overall Grand Challenge thereby set new standard tox prediction 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 | [-0.0007444783113896847, 0.04650363326072693, -0.016212070360779762, -0.03169908747076988, 0.030972357839345932, 0.009790626354515553, 0.043538421392440796, 0.03996269404888153, 0.04004068672657013, 0.024691862985491753, 0.00705236429348588, 0.04221075400710106, -0.03909539431333542, 0.08947142958641052, 0.03634662553668022, 0.012907025404274464, 0.03982367739081383, 0.030626319348812103, -0.03287413716316223, -0.008165833540260792, 0.018302887678146362, 0.012233476154506207, 0.03572998195886612, 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960 | 960 | ['Prateek Jain', 'Vivek Kulkarni', 'Abhradeep Thakurta', 'Oliver Williams'] | 1503.02031v1 | Training deep belief networks (DBNs) requires optimizing a non-convex
function with an extremely large number of parameters. Naturally, existing
gradient descent (GD) based methods are prone to arbitrarily poor local minima.
In this paper, we rigorously show that such local minima can be avoided (upto
an approximation error) by using the dropout technique, a widely used heuristic
in this domain. In particular, we show that by randomly dropping a few nodes of
a one-hidden layer neural network, the training objective function, up to a
certain approximation error, decreases by a multiplicative factor.
On the flip side, we show that for training convex empirical risk minimizers
(ERM), dropout in fact acts as a "stabilizer" or regularizer. That is, a simple
dropout based GD method for convex ERMs is stable in the face of arbitrary
changes to any one of the training points. Using the above assertion, we show
that dropout provides fast rates for generalization error in learning (convex)
generalized linear models (GLM). Moreover, using the above mentioned stability
properties of dropout, we design dropout based differentially private
algorithms for solving ERMs. The learned GLM thus, preserves privacy of each of
the individual training points while providing accurate predictions for new
test points. Finally, we empirically validate our stability assertions for
dropout in the context of convex ERMs and show that surprisingly, dropout
significantly outperforms (in terms of prediction accuracy) the L2
regularization based methods for several benchmark datasets. | To Drop or Not to Drop: Robustness, Consistency and Differential Privacy
Properties of Dropout | 2,015 | http://arxiv.org/pdf/1503.02031v1 | Title Drop Drop Robustness Consistency Differential Privacy Properties Dropout Summary Training deep belief network DBNs requires optimizing nonconvex function extremely large number parameter Naturally existing gradient descent GD based method prone arbitrarily poor local minimum paper rigorously show local minimum avoided upto approximation error using dropout technique widely used heuristic domain particular show randomly dropping node onehidden layer neural network training objective function certain approximation error decrease multiplicative factor flip side show training convex empirical risk minimizers ERM dropout fact act stabilizer regularizer simple dropout based GD method convex ERMs stable face arbitrary change one training point Using assertion show dropout provides fast rate generalization error learning convex generalized linear model GLM Moreover using mentioned stability property dropout design dropout based differentially private algorithm solving ERMs learned GLM thus preserve privacy individual training point providing accurate prediction new test point Finally empirically validate stability assertion dropout context convex ERMs show surprisingly dropout significantly outperforms term prediction accuracy L2 regularization based method several benchmark datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.017805472016334534, 0.09141001850366592, -0.030749650672078133, 0.042688459157943726, -0.03443252295255661, -0.04982313513755798, 0.06820384413003922, 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961 | 961 | ['Geoffrey Hinton', 'Oriol Vinyals', 'Jeff Dean'] | 1503.02531v1 | A very simple way to improve the performance of almost any machine learning
algorithm is to train many different models on the same data and then to
average their predictions. Unfortunately, making predictions using a whole
ensemble of models is cumbersome and may be too computationally expensive to
allow deployment to a large number of users, especially if the individual
models are large neural nets. Caruana and his collaborators have shown that it
is possible to compress the knowledge in an ensemble into a single model which
is much easier to deploy and we develop this approach further using a different
compression technique. We achieve some surprising results on MNIST and we show
that we can significantly improve the acoustic model of a heavily used
commercial system by distilling the knowledge in an ensemble of models into a
single model. We also introduce a new type of ensemble composed of one or more
full models and many specialist models which learn to distinguish fine-grained
classes that the full models confuse. Unlike a mixture of experts, these
specialist models can be trained rapidly and in parallel. | Distilling the Knowledge in a Neural Network | 2,015 | http://arxiv.org/pdf/1503.02531v1 | Title Distilling Knowledge Neural Network Summary simple way improve performance almost machine learning algorithm train many different model data average prediction Unfortunately making prediction using whole ensemble model cumbersome may computationally expensive allow deployment large number user especially individual model large neural net Caruana collaborator shown possible compress knowledge ensemble single model much easier deploy develop approach using different compression technique achieve surprising result MNIST show significantly improve acoustic model heavily used commercial system distilling knowledge ensemble model single model also introduce new type ensemble composed one full model many specialist model learn distinguish finegrained class full model confuse Unlike mixture expert specialist model trained rapidly parallel 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 | [-0.028798166662454605, 0.05130145326256752, 0.004625328816473484, 0.04425036162137985, -0.011224078945815563, -0.01668057218194008, 0.016323305666446686, 0.04563092812895775, -0.05150187760591507, 0.007483021821826696, -0.041148751974105835, 0.016859106719493866, 0.029475035145878792, 0.015684306621551514, 0.0027648410759866238, -0.09078150987625122, -0.019997118040919304, 0.007066037971526384, 0.01709592342376709, -0.03429679572582245, 0.015399377793073654, -0.017004188150167465, -0.024953613057732582, 0.04510023444890976, 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962 | 962 | ['Joan Bruna', 'Soumith Chintala', 'Yann LeCun', 'Serkan Piantino', 'Arthur Szlam', 'Mark Tygert'] | 1503.03438v3 | A complex-valued convolutional network (convnet) implements the repeated
application of the following composition of three operations, recursively
applying the composition to an input vector of nonnegative real numbers: (1)
convolution with complex-valued vectors followed by (2) taking the absolute
value of every entry of the resulting vectors followed by (3) local averaging.
For processing real-valued random vectors, complex-valued convnets can be
viewed as "data-driven multiscale windowed power spectra," "data-driven
multiscale windowed absolute spectra," "data-driven multiwavelet absolute
values," or (in their most general configuration) "data-driven nonlinear
multiwavelet packets." Indeed, complex-valued convnets can calculate multiscale
windowed spectra when the convnet filters are windowed complex-valued
exponentials. Standard real-valued convnets, using rectified linear units
(ReLUs), sigmoidal (for example, logistic or tanh) nonlinearities, max.
pooling, etc., do not obviously exhibit the same exact correspondence with
data-driven wavelets (whereas for complex-valued convnets, the correspondence
is much more than just a vague analogy). Courtesy of the exact correspondence,
the remarkably rich and rigorous body of mathematical analysis for wavelets
applies directly to (complex-valued) convnets. | A mathematical motivation for complex-valued convolutional networks | 2,015 | http://arxiv.org/pdf/1503.03438v3 | Title mathematical motivation complexvalued convolutional network Summary complexvalued convolutional network convnet implement repeated application following composition three operation recursively applying composition input vector nonnegative real number 1 convolution complexvalued vector followed 2 taking absolute value every entry resulting vector followed 3 local averaging processing realvalued random vector complexvalued convnets viewed datadriven multiscale windowed power spectrum datadriven multiscale windowed absolute spectrum datadriven multiwavelet absolute value general configuration datadriven nonlinear multiwavelet packet Indeed complexvalued convnets calculate multiscale windowed spectrum convnet filter windowed complexvalued exponential Standard realvalued convnets using rectified linear unit ReLUs sigmoidal example logistic tanh nonlinearities max pooling etc obviously exhibit exact correspondence datadriven wavelet whereas complexvalued convnets correspondence much vague analogy Courtesy exact correspondence remarkably rich rigorous body mathematical analysis wavelet applies directly complexvalued convnets 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 | [-0.044492967426776886, 0.03256741166114807, 0.00335773010738194, -0.015093979425728321, -0.026445599272847176, 0.003916062880307436, 0.047120507806539536, -0.028478853404521942, -0.07745401561260223, 0.018042949959635735, 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963 | 963 | ['James Martens', 'Roger Grosse'] | 1503.05671v6 | We propose an efficient method for approximating natural gradient descent in
neural networks which we call Kronecker-Factored Approximate Curvature (K-FAC).
K-FAC is based on an efficiently invertible approximation of a neural network's
Fisher information matrix which is neither diagonal nor low-rank, and in some
cases is completely non-sparse. It is derived by approximating various large
blocks of the Fisher (corresponding to entire layers) as being the Kronecker
product of two much smaller matrices. While only several times more expensive
to compute than the plain stochastic gradient, the updates produced by K-FAC
make much more progress optimizing the objective, which results in an algorithm
that can be much faster than stochastic gradient descent with momentum in
practice. And unlike some previously proposed approximate
natural-gradient/Newton methods which use high-quality non-diagonal curvature
matrices (such as Hessian-free optimization), K-FAC works very well in highly
stochastic optimization regimes. This is because the cost of storing and
inverting K-FAC's approximation to the curvature matrix does not depend on the
amount of data used to estimate it, which is a feature typically associated
only with diagonal or low-rank approximations to the curvature matrix. | Optimizing Neural Networks with Kronecker-factored Approximate Curvature | 2,015 | http://arxiv.org/pdf/1503.05671v6 | Title Optimizing Neural Networks Kroneckerfactored Approximate Curvature Summary propose efficient method approximating natural gradient descent neural network call KroneckerFactored Approximate Curvature KFAC KFAC based efficiently invertible approximation neural network Fisher information matrix neither diagonal lowrank case completely nonsparse derived approximating various large block Fisher corresponding entire layer Kronecker product two much smaller matrix several time expensive compute plain stochastic gradient update produced KFAC make much progress optimizing objective result algorithm much faster stochastic gradient descent momentum practice unlike previously proposed approximate naturalgradientNewton method use highquality nondiagonal curvature matrix Hessianfree optimization KFAC work well highly stochastic optimization regime cost storing inverting KFACs approximation curvature matrix depend amount data used estimate feature typically associated diagonal lowrank approximation curvature matrix 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 | [-0.012532608583569527, 0.014184918254613876, 0.0036409306339919567, 0.058472055941820145, 0.004249291494488716, -0.02122035063803196, -0.00973591860383749, 0.009059455245733261, -0.028992824256420135, 0.0506318099796772, -0.018538257107138634, 0.011797339655458927, 0.011298962868750095, -0.01199228037148714, 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964 | 964 | ['Xiao-Lei Zhang'] | 1503.06452v1 | Recently, multilayer bootstrap network (MBN) has demonstrated promising
performance in unsupervised dimensionality reduction. It can learn compact
representations in standard data sets, i.e. MNIST and RCV1. However, as a
bootstrap method, the prediction complexity of MBN is high. In this paper, we
propose an unsupervised model compression framework for this general problem of
unsupervised bootstrap methods. The framework compresses a large unsupervised
bootstrap model into a small model by taking the bootstrap model and its
application together as a black box and learning a mapping function from the
input of the bootstrap model to the output of the application by a supervised
learner. To specialize the framework, we propose a new technique, named
compressive MBN. It takes MBN as the unsupervised bootstrap model and deep
neural network (DNN) as the supervised learner. Our initial result on MNIST
showed that compressive MBN not only maintains the high prediction accuracy of
MBN but also is over thousands of times faster than MBN at the prediction
stage. Our result suggests that the new technique integrates the effectiveness
of MBN on unsupervised learning and the effectiveness and efficiency of DNN on
supervised learning together for the effectiveness and efficiency of
compressive MBN on unsupervised learning. | Unsupervised model compression for multilayer bootstrap networks | 2,015 | http://arxiv.org/pdf/1503.06452v1 | Title Unsupervised model compression multilayer bootstrap network Summary Recently multilayer bootstrap network MBN demonstrated promising performance unsupervised dimensionality reduction learn compact representation standard data set ie MNIST RCV1 However bootstrap method prediction complexity MBN high paper propose unsupervised model compression framework general problem unsupervised bootstrap method framework compress large unsupervised bootstrap model small model taking bootstrap model application together black box learning mapping function input bootstrap model output application supervised learner specialize framework propose new technique named compressive MBN take MBN unsupervised bootstrap model deep neural network DNN supervised learner initial result MNIST showed compressive MBN maintains high prediction accuracy MBN also thousand time faster MBN prediction stage result suggests new technique integrates effectiveness MBN unsupervised learning effectiveness efficiency DNN supervised learning together effectiveness efficiency compressive MBN unsupervised 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 | [-0.05592220276594162, 0.06550544500350952, -0.026258407160639763, -0.01240052655339241, -0.017331624403595924, -0.009575430303812027, 0.03874025121331215, 0.04392075166106224, -0.04054724797606468, 0.0416618250310421, -0.030330320820212364, 0.019403638318181038, 0.02352558635175228, 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965 | 965 | ['Leen De Baets', 'Joeri Ruyssinck', 'Thomas Peiffer', 'Johan Decruyenaere', 'Filip De Turck', 'Femke Ongenae', 'Tom Dhaene'] | 1612.00962v1 | The presence of bacteria or fungi in the bloodstream of patients is abnormal
and can lead to life-threatening conditions. A computational model based on a
bidirectional long short-term memory artificial neural network, is explored to
assist doctors in the intensive care unit to predict whether examination of
blood cultures of patients will return positive. As input it uses nine
monitored clinical parameters, presented as time series data, collected from
2177 ICU admissions at the Ghent University Hospital. Our main goal is to
determine if general machine learning methods and more specific, temporal
models, can be used to create an early detection system. This preliminary
research obtains an area of 71.95% under the precision recall curve, proving
the potential of temporal neural networks in this context. | Positive blood culture detection in time series data using a BiLSTM
network | 2,016 | http://arxiv.org/pdf/1612.00962v1 | Title Positive blood culture detection time series data using BiLSTM network Summary presence bacteria fungi bloodstream patient abnormal lead lifethreatening condition computational model based bidirectional long shortterm memory artificial neural network explored assist doctor intensive care unit predict whether examination blood culture patient return positive input us nine monitored clinical parameter presented time series data collected 2177 ICU admission Ghent University Hospital main goal determine general machine learning method specific temporal model used create early detection system preliminary research obtains area 7195 precision recall curve proving potential temporal neural network context 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 | [-0.012951232492923737, -0.008543558418750763, -0.031216444447636604, -0.031555403023958206, -0.0752585157752037, -0.02529570832848549, 0.03065352700650692, 0.00014822077355347574, 0.026094481348991394, 0.031777892261743546, 0.0319533497095108, -0.058320336043834686, -0.004646113608032465, 0.12846587598323822, -0.0011220306623727083, 0.045534875243902206, 0.0503222793340683, 0.02572545036673546, 0.003447937546297908, 0.03267086669802666, 0.006813202053308487, -0.006472267676144838, -0.017590057104825974, 0.03306221216917038, -0.02598244696855545, 0.04075053706765175, 0.01631104201078415, -0.07381404936313629, 0.037841372191905975, -0.07499909400939941, -5.061383490101434e-05, -0.046965423971414566, -0.02244114689528942, 0.01922621950507164, 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966 | 966 | ['Ramon Oliveira', 'Pedro Tabacof', 'Eduardo Valle'] | 1612.01251v2 | We evaluate the uncertainty quality in neural networks using anomaly
detection. We extract uncertainty measures (e.g. entropy) from the predictions
of candidate models, use those measures as features for an anomaly detector,
and gauge how well the detector differentiates known from unknown classes. We
assign higher uncertainty quality to candidate models that lead to better
detectors. We also propose a novel method for sampling a variational
approximation of a Bayesian neural network, called One-Sample Bayesian
Approximation (OSBA). We experiment on two datasets, MNIST and CIFAR10. We
compare the following candidate neural network models: Maximum Likelihood,
Bayesian Dropout, OSBA, and --- for MNIST --- the standard variational
approximation. We show that Bayesian Dropout and OSBA provide better
uncertainty information than Maximum Likelihood, and are essentially equivalent
to the standard variational approximation, but much faster. | Known Unknowns: Uncertainty Quality in Bayesian Neural Networks | 2,016 | http://arxiv.org/pdf/1612.01251v2 | Title Known Unknowns Uncertainty Quality Bayesian Neural Networks Summary evaluate uncertainty quality neural network using anomaly detection extract uncertainty measure eg entropy prediction candidate model use measure feature anomaly detector gauge well detector differentiates known unknown class assign higher uncertainty quality candidate model lead better detector also propose novel method sampling variational approximation Bayesian neural network called OneSample Bayesian Approximation OSBA experiment two datasets MNIST CIFAR10 compare following candidate neural network model Maximum Likelihood Bayesian Dropout OSBA MNIST standard variational approximation show Bayesian Dropout OSBA provide better uncertainty information Maximum Likelihood essentially equivalent standard variational approximation much faster 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 | [0.0036830634344369173, 0.08199070394039154, -0.010826492682099342, 0.004590068478137255, -0.00260705198161304, -0.02226206474006176, 0.019099324941635132, 0.007932019419968128, -0.06679008156061172, 0.024930398911237717, 0.07076667994260788, -0.02156977355480194, 0.012950809672474861, 0.044534120708703995, 0.0020351908169686794, -0.023545432835817337, 0.02230992168188095, -0.0011218857252970338, 0.028915859758853912, -0.005348429083824158, -0.02997194044291973, -0.05653630569577217, 0.021919654682278633, 0.02526581473648548, 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967 | 967 | ['Tan Nguyen', 'Wanjia Liu', 'Ethan Perez', 'Richard G. Baraniuk', 'Ankit B. Patel'] | 1612.01942v1 | Semi-supervised learning algorithms reduce the high cost of acquiring labeled
training data by using both labeled and unlabeled data during learning. Deep
Convolutional Networks (DCNs) have achieved great success in supervised tasks
and as such have been widely employed in the semi-supervised learning. In this
paper we leverage the recently developed Deep Rendering Mixture Model (DRMM), a
probabilistic generative model that models latent nuisance variation, and whose
inference algorithm yields DCNs. We develop an EM algorithm for the DRMM to
learn from both labeled and unlabeled data. Guided by the theory of the DRMM,
we introduce a novel non-negativity constraint and a variational inference
term. We report state-of-the-art performance on MNIST and SVHN and competitive
results on CIFAR10. We also probe deeper into how a DRMM trained in a
semi-supervised setting represents latent nuisance variation using
synthetically rendered images. Taken together, our work provides a unified
framework for supervised, unsupervised, and semi-supervised learning. | Semi-Supervised Learning with the Deep Rendering Mixture Model | 2,016 | http://arxiv.org/pdf/1612.01942v1 | Title SemiSupervised Learning Deep Rendering Mixture Model Summary Semisupervised learning algorithm reduce high cost acquiring labeled training data using labeled unlabeled data learning Deep Convolutional Networks DCNs achieved great success supervised task widely employed semisupervised learning paper leverage recently developed Deep Rendering Mixture Model DRMM probabilistic generative model model latent nuisance variation whose inference algorithm yield DCNs develop EM algorithm DRMM learn labeled unlabeled data Guided theory DRMM introduce novel nonnegativity constraint variational inference term report stateoftheart performance MNIST SVHN competitive result CIFAR10 also probe deeper DRMM trained semisupervised setting represents latent nuisance variation using synthetically rendered image Taken together work provides unified framework supervised unsupervised semisupervised 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 | [-0.008170673623681068, 0.025109563022851944, 0.037925224751234055, 0.043754056096076965, 0.02285914681851864, -0.0004681906139012426, 0.03678205609321594, -0.0021924348548054695, -0.04152170196175575, 0.028147898614406586, -0.00946744903922081, 0.022286899387836456, -0.01147459726780653, 0.01926068589091301, 0.03461194410920143, -0.09650631248950958, -0.05077836290001869, -0.003394162515178323, -0.021142330020666122, -0.03126230835914612, 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968 | 968 | ['Yuansi Chen', 'Cengiz Pehlevan', 'Dmitri B. Chklovskii'] | 1612.03480v1 | Recently, a novel family of biologically plausible online algorithms for
reducing the dimensionality of streaming data has been derived from the
similarity matching principle. In these algorithms, the number of output
dimensions can be determined adaptively by thresholding the singular values of
the input data matrix. However, setting such threshold requires knowing the
magnitude of the desired singular values in advance. Here we propose online
algorithms where the threshold is self-calibrating based on the singular values
computed from the existing observations. To derive these algorithms from the
similarity matching cost function we propose novel regularizers. As before,
these online algorithms can be implemented by Hebbian/anti-Hebbian neural
networks in which the learning rule depends on the chosen regularizer. We
demonstrate both mathematically and via simulation the effectiveness of these
online algorithms in various settings. | Self-calibrating Neural Networks for Dimensionality Reduction | 2,016 | http://arxiv.org/pdf/1612.03480v1 | Title Selfcalibrating Neural Networks Dimensionality Reduction Summary Recently novel family biologically plausible online algorithm reducing dimensionality streaming data derived similarity matching principle algorithm number output dimension determined adaptively thresholding singular value input data matrix However setting threshold requires knowing magnitude desired singular value advance propose online algorithm threshold selfcalibrating based singular value computed existing observation derive algorithm similarity matching cost function propose novel regularizers online algorithm implemented HebbianantiHebbian neural network learning rule depends chosen regularizer demonstrate mathematically via simulation effectiveness online algorithm various 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 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.05297711491584778, 0.00559360021725297, -0.033285997807979584, 0.0115312235429883, 0.02226196974515915, -0.005606521386653185, 0.06173083558678627, 0.007216775789856911, 0.024872606620192528, 0.041052237153053284, -0.041381120681762695, 0.03039839118719101, 0.007977558299899101, 0.08106248825788498, -0.002376965247094631, 0.011329937726259232, -0.010091694071888924, 0.04960371181368828, -0.05688464641571045, -0.059223711490631104, 0.02223741076886654, -0.03404506295919418, -0.0012874002568423748, -0.0032745038624852896, -0.056970976293087006, -0.016915252432227135, 0.007249670103192329, -0.022335229441523552, 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969 | 969 | ['Li Jing', 'Yichen Shen', 'Tena Dubček', 'John Peurifoy', 'Scott Skirlo', 'Yann LeCun', 'Max Tegmark', 'Marin Soljačić'] | 1612.05231v3 | Using unitary (instead of general) matrices in artificial neural networks
(ANNs) is a promising way to solve the gradient explosion/vanishing problem, as
well as to enable ANNs to learn long-term correlations in the data. This
approach appears particularly promising for Recurrent Neural Networks (RNNs).
In this work, we present a new architecture for implementing an Efficient
Unitary Neural Network (EUNNs); its main advantages can be summarized as
follows. Firstly, the representation capacity of the unitary space in an EUNN
is fully tunable, ranging from a subspace of SU(N) to the entire unitary space.
Secondly, the computational complexity for training an EUNN is merely
$\mathcal{O}(1)$ per parameter. Finally, we test the performance of EUNNs on
the standard copying task, the pixel-permuted MNIST digit recognition benchmark
as well as the Speech Prediction Test (TIMIT). We find that our architecture
significantly outperforms both other state-of-the-art unitary RNNs and the LSTM
architecture, in terms of the final performance and/or the wall-clock training
speed. EUNNs are thus promising alternatives to RNNs and LSTMs for a wide
variety of applications. | Tunable Efficient Unitary Neural Networks (EUNN) and their application
to RNNs | 2,016 | http://arxiv.org/pdf/1612.05231v3 | Title Tunable Efficient Unitary Neural Networks EUNN application RNNs Summary Using unitary instead general matrix artificial neural network ANNs promising way solve gradient explosionvanishing problem well enable ANNs learn longterm correlation data approach appears particularly promising Recurrent Neural Networks RNNs work present new architecture implementing Efficient Unitary Neural Network EUNNs main advantage summarized follows Firstly representation capacity unitary space EUNN fully tunable ranging subspace SUN entire unitary space Secondly computational complexity training EUNN merely mathcalO1 per parameter Finally test performance EUNNs standard copying task pixelpermuted MNIST digit recognition benchmark well Speech Prediction Test TIMIT find architecture significantly outperforms stateoftheart unitary RNNs LSTM architecture term final performance andor wallclock training speed EUNNs thus promising alternative RNNs LSTMs wide variety 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 | [-0.009237864054739475, 0.06816868484020233, 0.0008616929408162832, 0.03955705463886261, 0.0034008780494332314, 0.005337234120815992, 0.04632338881492615, -0.02107185311615467, -0.034575048834085464, -0.008716028183698654, -0.06766808032989502, -0.047045931220054626, 0.010109322145581245, -0.012877666391432285, 0.008467827923595905, -0.018240993842482567, 0.041377149522304535, -0.00455354293808341, -0.07749438285827637, -0.04433416202664375, 0.004486521240323782, -0.0455772764980793, -0.05832904204726219, 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970 | 970 | ['Alex Graves'] | 1211.3711v1 | Many machine learning tasks can be expressed as the transformation---or
\emph{transduction}---of input sequences into output sequences: speech
recognition, machine translation, protein secondary structure prediction and
text-to-speech to name but a few. One of the key challenges in sequence
transduction is learning to represent both the input and output sequences in a
way that is invariant to sequential distortions such as shrinking, stretching
and translating. Recurrent neural networks (RNNs) are a powerful sequence
learning architecture that has proven capable of learning such representations.
However RNNs traditionally require a pre-defined alignment between the input
and output sequences to perform transduction. This is a severe limitation since
\emph{finding} the alignment is the most difficult aspect of many sequence
transduction problems. Indeed, even determining the length of the output
sequence is often challenging. This paper introduces an end-to-end,
probabilistic sequence transduction system, based entirely on RNNs, that is in
principle able to transform any input sequence into any finite, discrete output
sequence. Experimental results for phoneme recognition are provided on the
TIMIT speech corpus. | Sequence Transduction with Recurrent Neural Networks | 2,012 | http://arxiv.org/pdf/1211.3711v1 | Title Sequence Transduction Recurrent Neural Networks Summary Many machine learning task expressed transformationor emphtransductionof input sequence output sequence speech recognition machine translation protein secondary structure prediction texttospeech name One key challenge sequence transduction learning represent input output sequence way invariant sequential distortion shrinking stretching translating Recurrent neural network RNNs powerful sequence learning architecture proven capable learning representation However RNNs traditionally require predefined alignment input output sequence perform transduction severe limitation since emphfinding alignment difficult aspect many sequence transduction problem Indeed even determining length output sequence often challenging paper introduces endtoend probabilistic sequence transduction system based entirely RNNs principle able transform input sequence finite discrete output sequence Experimental result phoneme recognition provided TIMIT speech corpus 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 | [0.03449882194399834, 0.03490511700510979, 0.008206603117287159, 0.022779539227485657, -0.01845366321504116, -0.024564433842897415, -0.00438099168241024, 0.0035732174292206764, -0.05922279506921768, -0.02199256420135498, -0.011115418747067451, -0.02383965626358986, 0.05342552065849304, 0.030779361724853516, 0.03450643643736839, -0.048359889537096024, 0.017545923590660095, 0.01764322631061077, 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971 | 971 | ['Justin Bayer', 'Christian Osendorfer', 'Daniela Korhammer', 'Nutan Chen', 'Sebastian Urban', 'Patrick van der Smagt'] | 1311.0701v7 | Recurrent Neural Networks (RNNs) are rich models for the processing of
sequential data. Recent work on advancing the state of the art has been focused
on the optimization or modelling of RNNs, mostly motivated by adressing the
problems of the vanishing and exploding gradients. The control of overfitting
has seen considerably less attention. This paper contributes to that by
analyzing fast dropout, a recent regularization method for generalized linear
models and neural networks from a back-propagation inspired perspective. We
show that fast dropout implements a quadratic form of an adaptive,
per-parameter regularizer, which rewards large weights in the light of
underfitting, penalizes them for overconfident predictions and vanishes at
minima of an unregularized training loss. The derivatives of that regularizer
are exclusively based on the training error signal. One consequence of this is
the absense of a global weight attractor, which is particularly appealing for
RNNs, since the dynamics are not biased towards a certain regime. We positively
test the hypothesis that this improves the performance of RNNs on four musical
data sets. | On Fast Dropout and its Applicability to Recurrent Networks | 2,013 | http://arxiv.org/pdf/1311.0701v7 | Title Fast Dropout Applicability Recurrent Networks Summary Recurrent Neural Networks RNNs rich model processing sequential data Recent work advancing state art focused optimization modelling RNNs mostly motivated adressing problem vanishing exploding gradient control overfitting seen considerably le attention paper contributes analyzing fast dropout recent regularization method generalized linear model neural network backpropagation inspired perspective show fast dropout implement quadratic form adaptive perparameter regularizer reward large weight light underfitting penalizes overconfident prediction vanishes minimum unregularized training loss derivative regularizer exclusively based training error signal One consequence absense global weight attractor particularly appealing RNNs since dynamic biased towards certain regime positively test hypothesis improves performance RNNs four musical 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 | [-0.024142397567629814, 0.002736502792686224, -0.014516575261950493, 0.03279123082756996, -0.006244538351893425, -0.026343349367380142, 0.04186433181166649, -0.0030760816298425198, -0.03644561395049095, 0.021019943058490753, -0.040526412427425385, 0.036918289959430695, -0.00025931018171831965, 0.07496020942926407, 0.02339334785938263, -0.03948717191815376, 0.019763819873332977, 0.009119199588894844, 0.008107434026896954, -0.007887220941483974, 0.006074529141187668, -0.00605819933116436, 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972 | 972 | ['Caglar Gulcehre', 'Kyunghyun Cho', 'Razvan Pascanu', 'Yoshua Bengio'] | 1311.1780v7 | In this paper we propose and investigate a novel nonlinear unit, called $L_p$
unit, for deep neural networks. The proposed $L_p$ unit receives signals from
several projections of a subset of units in the layer below and computes a
normalized $L_p$ norm. We notice two interesting interpretations of the $L_p$
unit. First, the proposed unit can be understood as a generalization of a
number of conventional pooling operators such as average, root-mean-square and
max pooling widely used in, for instance, convolutional neural networks (CNN),
HMAX models and neocognitrons. Furthermore, the $L_p$ unit is, to a certain
degree, similar to the recently proposed maxout unit (Goodfellow et al., 2013)
which achieved the state-of-the-art object recognition results on a number of
benchmark datasets. Secondly, we provide a geometrical interpretation of the
activation function based on which we argue that the $L_p$ unit is more
efficient at representing complex, nonlinear separating boundaries. Each $L_p$
unit defines a superelliptic boundary, with its exact shape defined by the
order $p$. We claim that this makes it possible to model arbitrarily shaped,
curved boundaries more efficiently by combining a few $L_p$ units of different
orders. This insight justifies the need for learning different orders for each
unit in the model. We empirically evaluate the proposed $L_p$ units on a number
of datasets and show that multilayer perceptrons (MLP) consisting of the $L_p$
units achieve the state-of-the-art results on a number of benchmark datasets.
Furthermore, we evaluate the proposed $L_p$ unit on the recently proposed deep
recurrent neural networks (RNN). | Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks | 2,013 | http://arxiv.org/pdf/1311.1780v7 | Title LearnedNorm Pooling Deep Feedforward Recurrent Neural Networks Summary paper propose investigate novel nonlinear unit called Lp unit deep neural network proposed Lp unit receives signal several projection subset unit layer computes normalized Lp norm notice two interesting interpretation Lp unit First proposed unit understood generalization number conventional pooling operator average rootmeansquare max pooling widely used instance convolutional neural network CNN HMAX model neocognitrons Furthermore Lp unit certain degree similar recently proposed maxout unit Goodfellow et al 2013 achieved stateoftheart object recognition result number benchmark datasets Secondly provide geometrical interpretation activation function based argue Lp unit efficient representing complex nonlinear separating boundary Lp unit defines superelliptic boundary exact shape defined order p claim make possible model arbitrarily shaped curved boundary efficiently combining Lp unit different order insight justifies need learning different order unit model empirically evaluate proposed Lp unit number datasets show multilayer perceptrons MLP consisting Lp unit achieve stateoftheart result number benchmark datasets Furthermore evaluate proposed Lp unit recently proposed deep recurrent neural network RNN 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 | [-0.0314551442861557, -0.0031415289267897606, 0.009956634603440762, 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973 | 973 | ['Michael S. Gashler', 'Michael R. Smith', 'Richard Morris', 'Tony Martinez'] | 1312.5394v1 | Many data mining and data analysis techniques operate on dense matrices or
complete tables of data. Real-world data sets, however, often contain unknown
values. Even many classification algorithms that are designed to operate with
missing values still exhibit deteriorated accuracy. One approach to handling
missing values is to fill in (impute) the missing values. In this paper, we
present a technique for unsupervised learning called Unsupervised
Backpropagation (UBP), which trains a multi-layer perceptron to fit to the
manifold sampled by a set of observed point-vectors. We evaluate UBP with the
task of imputing missing values in datasets, and show that UBP is able to
predict missing values with significantly lower sum-squared error than other
collaborative filtering and imputation techniques. We also demonstrate with 24
datasets and 9 supervised learning algorithms that classification accuracy is
usually higher when randomly-withheld values are imputed using UBP, rather than
with other methods. | Missing Value Imputation With Unsupervised Backpropagation | 2,013 | http://arxiv.org/pdf/1312.5394v1 | Title Missing Value Imputation Unsupervised Backpropagation Summary Many data mining data analysis technique operate dense matrix complete table data Realworld data set however often contain unknown value Even many classification algorithm designed operate missing value still exhibit deteriorated accuracy One approach handling missing value fill impute missing value paper present technique unsupervised learning called Unsupervised Backpropagation UBP train multilayer perceptron fit manifold sampled set observed pointvectors evaluate UBP task imputing missing value datasets show UBP able predict missing value significantly lower sumsquared error collaborative filtering imputation technique also demonstrate 24 datasets 9 supervised learning algorithm classification accuracy usually higher randomlywithheld value imputed using UBP rather 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 | [-0.02935723587870598, 0.022572703659534454, -0.010296245105564594, -0.03565344586968422, 0.010964639484882355, 0.014116673730313778, 0.02508564107120037, -0.0064797066152095795, 0.011858065612614155, 0.06767700612545013, 0.05249015986919403, 0.025064688175916672, 0.014964817091822624, 0.04103029519319534, 0.00569181377068162, -0.018670402467250824, 0.03484579548239708, 0.06421618163585663, 0.0043494016863405704, -0.03353046998381615, -0.02632637321949005, -0.02441585808992386, -0.002571998629719019, 0.0028645226266235113, -0.05234849452972412, 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974 | 974 | ['Mathias Berglund', 'Tapani Raiko'] | 1312.6002v3 | Contrastive Divergence (CD) and Persistent Contrastive Divergence (PCD) are
popular methods for training the weights of Restricted Boltzmann Machines.
However, both methods use an approximate method for sampling from the model
distribution. As a side effect, these approximations yield significantly
different biases and variances for stochastic gradient estimates of individual
data points. It is well known that CD yields a biased gradient estimate. In
this paper we however show empirically that CD has a lower stochastic gradient
estimate variance than exact sampling, while the mean of subsequent PCD
estimates has a higher variance than exact sampling. The results give one
explanation to the finding that CD can be used with smaller minibatches or
higher learning rates than PCD. | Stochastic Gradient Estimate Variance in Contrastive Divergence and
Persistent Contrastive Divergence | 2,013 | http://arxiv.org/pdf/1312.6002v3 | Title Stochastic Gradient Estimate Variance Contrastive Divergence Persistent Contrastive Divergence Summary Contrastive Divergence CD Persistent Contrastive Divergence PCD popular method training weight Restricted Boltzmann Machines However method use approximate method sampling model distribution side effect approximation yield significantly different bias variance stochastic gradient estimate individual data point well known CD yield biased gradient estimate paper however show empirically CD lower stochastic gradient estimate variance exact sampling mean subsequent PCD estimate higher variance exact sampling result give one explanation finding CD used smaller minibatches higher learning rate PCD 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 | [-0.005314758513122797, -0.021308433264493942, -0.029232794418931007, 0.00809037871658802, -0.053116679191589355, -0.007740939036011696, 0.07496026903390884, -0.006683580577373505, -0.059172842651605606, 0.049565389752388, 0.01710551045835018, -0.007842803373932838, 0.052812639623880386, 0.007303531747311354, 0.026744389906525612, -0.08057505637407303, 0.0007256402750499547, 0.007207838818430901, -0.04075457900762558, -0.05147833749651909, 0.029619136825203896, -0.049190226942300797, 0.018844101577997208, -0.03136768564581871, -0.007680641952902079, -0.014637447893619537, 0.0064719985239207745, -0.03486546128988266, 0.02665080688893795, -0.06619922071695328, 0.059392232447862625, 0.030226873233914375, 0.024598637595772743, -0.0124396663159132, 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975 | 975 | ['Razvan Pascanu', 'Caglar Gulcehre', 'Kyunghyun Cho', 'Yoshua Bengio'] | 1312.6026v5 | In this paper, we explore different ways to extend a recurrent neural network
(RNN) to a \textit{deep} RNN. We start by arguing that the concept of depth in
an RNN is not as clear as it is in feedforward neural networks. By carefully
analyzing and understanding the architecture of an RNN, however, we find three
points of an RNN which may be made deeper; (1) input-to-hidden function, (2)
hidden-to-hidden transition and (3) hidden-to-output function. Based on this
observation, we propose two novel architectures of a deep RNN which are
orthogonal to an earlier attempt of stacking multiple recurrent layers to build
a deep RNN (Schmidhuber, 1992; El Hihi and Bengio, 1996). We provide an
alternative interpretation of these deep RNNs using a novel framework based on
neural operators. The proposed deep RNNs are empirically evaluated on the tasks
of polyphonic music prediction and language modeling. The experimental result
supports our claim that the proposed deep RNNs benefit from the depth and
outperform the conventional, shallow RNNs. | How to Construct Deep Recurrent Neural Networks | 2,013 | http://arxiv.org/pdf/1312.6026v5 | Title Construct Deep Recurrent Neural Networks Summary paper explore different way extend recurrent neural network RNN textitdeep RNN start arguing concept depth RNN clear feedforward neural network carefully analyzing understanding architecture RNN however find three point RNN may made deeper 1 inputtohidden function 2 hiddentohidden transition 3 hiddentooutput function Based observation propose two novel architecture deep RNN orthogonal earlier attempt stacking multiple recurrent layer build deep RNN Schmidhuber 1992 El Hihi Bengio 1996 provide alternative interpretation deep RNNs using novel framework based neural operator proposed deep RNNs empirically evaluated task polyphonic music prediction language modeling experimental result support claim proposed deep RNNs benefit depth outperform conventional shallow RNNs 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 | [0.012716674245893955, 0.02758781984448433, 0.004509675782173872, 0.07180525362491608, -0.013282503001391888, -0.04989452660083771, 0.03858890011906624, -0.020954087376594543, -0.050997212529182434, 0.02703608199954033, -0.04728904739022255, -0.01823623850941658, 0.02020104043185711, 0.029123356565833092, 0.021963557228446007, -0.06208406388759613, 0.006426340434700251, -0.00744672492146492, 0.003861980978399515, 0.013239736668765545, 0.000672230904456228, -0.0011155684478580952, 0.007955970242619514, 0.04611460492014885, -0.0014992846408858895, 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976 | 976 | ['David P. Reichert', 'Thomas Serre'] | 1312.6115v5 | Deep learning has recently led to great successes in tasks such as image
recognition (e.g Krizhevsky et al., 2012). However, deep networks are still
outmatched by the power and versatility of the brain, perhaps in part due to
the richer neuronal computations available to cortical circuits. The challenge
is to identify which neuronal mechanisms are relevant, and to find suitable
abstractions to model them. Here, we show how aspects of spike timing, long
hypothesized to play a crucial role in cortical information processing, could
be incorporated into deep networks to build richer, versatile representations.
We introduce a neural network formulation based on complex-valued neuronal
units that is not only biologically meaningful but also amenable to a variety
of deep learning frameworks. Here, units are attributed both a firing rate and
a phase, the latter indicating properties of spike timing. We show how this
formulation qualitatively captures several aspects thought to be related to
neuronal synchrony, including gating of information processing and dynamic
binding of distributed object representations. Focusing on the latter, we
demonstrate the potential of the approach in several simple experiments. Thus,
neuronal synchrony could be a flexible mechanism that fulfills multiple
functional roles in deep networks. | Neuronal Synchrony in Complex-Valued Deep Networks | 2,013 | http://arxiv.org/pdf/1312.6115v5 | Title Neuronal Synchrony ComplexValued Deep Networks Summary Deep learning recently led great success task image recognition eg Krizhevsky et al 2012 However deep network still outmatched power versatility brain perhaps part due richer neuronal computation available cortical circuit challenge identify neuronal mechanism relevant find suitable abstraction model show aspect spike timing long hypothesized play crucial role cortical information processing could incorporated deep network build richer versatile representation introduce neural network formulation based complexvalued neuronal unit biologically meaningful also amenable variety deep learning framework unit attributed firing rate phase latter indicating property spike timing show formulation qualitatively capture several aspect thought related neuronal synchrony including gating information processing dynamic binding distributed object representation Focusing latter demonstrate potential approach several simple experiment Thus neuronal synchrony could flexible mechanism fulfills multiple functional role deep network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.024427782744169235, 0.018502578139305115, -0.01420583389699459, 0.02766118012368679, 0.0035562333650887012, 0.020545832812786102, 0.03273328021168709, -0.011375727131962776, -0.011664996854960918, 0.03170039877295494, -0.03996220603585243, -0.00932775717228651, 0.027115266770124435, 0.05409993976354599, 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977 | 977 | ['David Warde-Farley', 'Ian J. Goodfellow', 'Aaron Courville', 'Yoshua Bengio'] | 1312.6197v2 | The recently introduced dropout training criterion for neural networks has
been the subject of much attention due to its simplicity and remarkable
effectiveness as a regularizer, as well as its interpretation as a training
procedure for an exponentially large ensemble of networks that share
parameters. In this work we empirically investigate several questions related
to the efficacy of dropout, specifically as it concerns networks employing the
popular rectified linear activation function. We investigate the quality of the
test time weight-scaling inference procedure by evaluating the geometric
average exactly in small models, as well as compare the performance of the
geometric mean to the arithmetic mean more commonly employed by ensemble
techniques. We explore the effect of tied weights on the ensemble
interpretation by training ensembles of masked networks without tied weights.
Finally, we investigate an alternative criterion based on a biased estimator of
the maximum likelihood ensemble gradient. | An empirical analysis of dropout in piecewise linear networks | 2,013 | http://arxiv.org/pdf/1312.6197v2 | Title empirical analysis dropout piecewise linear network Summary recently introduced dropout training criterion neural network subject much attention due simplicity remarkable effectiveness regularizer well interpretation training procedure exponentially large ensemble network share parameter work empirically investigate several question related efficacy dropout specifically concern network employing popular rectified linear activation function investigate quality test time weightscaling inference procedure evaluating geometric average exactly small model well compare performance geometric mean arithmetic mean commonly employed ensemble technique explore effect tied weight ensemble interpretation training ensemble masked network without tied weight Finally investigate alternative criterion based biased estimator maximum likelihood ensemble gradient 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 | [-0.030541466549038887, 0.03167891129851341, -0.016241639852523804, 0.0310830045491457, 0.008423184044659138, 0.021112192422151566, 0.08107151091098785, -0.007142371963709593, 0.01009425986558199, 0.003466809866949916, 0.01621749810874462, 0.016501571983098984, 0.002564777387306094, 0.09142162650823593, 0.028111577033996582, -0.03388267382979393, -0.026897309347987175, 0.04735645651817322, 0.004697561264038086, -0.014072734862565994, 0.03930786997079849, -0.011984608136117458, -0.005494180601090193, 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978 | 978 | ['Ian J. Goodfellow', 'Mehdi Mirza', 'Da Xiao', 'Aaron Courville', 'Yoshua Bengio'] | 1312.6211v3 | Catastrophic forgetting is a problem faced by many machine learning models
and algorithms. When trained on one task, then trained on a second task, many
machine learning models "forget" how to perform the first task. This is widely
believed to be a serious problem for neural networks. Here, we investigate the
extent to which the catastrophic forgetting problem occurs for modern neural
networks, comparing both established and recent gradient-based training
algorithms and activation functions. We also examine the effect of the
relationship between the first task and the second task on catastrophic
forgetting. We find that it is always best to train using the dropout
algorithm--the dropout algorithm is consistently best at adapting to the new
task, remembering the old task, and has the best tradeoff curve between these
two extremes. We find that different tasks and relationships between tasks
result in very different rankings of activation function performance. This
suggests the choice of activation function should always be cross-validated. | An Empirical Investigation of Catastrophic Forgetting in Gradient-Based
Neural Networks | 2,013 | http://arxiv.org/pdf/1312.6211v3 | Title Empirical Investigation Catastrophic Forgetting GradientBased Neural Networks Summary Catastrophic forgetting problem faced many machine learning model algorithm trained one task trained second task many machine learning model forget perform first task widely believed serious problem neural network investigate extent catastrophic forgetting problem occurs modern neural network comparing established recent gradientbased training algorithm activation function also examine effect relationship first task second task catastrophic forgetting find always best train using dropout algorithmthe dropout algorithm consistently best adapting new task remembering old task best tradeoff curve two extreme find different task relationship task result different ranking activation function performance suggests choice activation function always crossvalidated 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 | [-0.011497528292238712, 0.013375970534980297, -0.0077837985008955, -0.008122350089251995, 0.013212005607783794, 0.022770186886191368, 0.019457323476672173, -0.012820876203477383, 0.017209367826581, 0.0022365832701325417, -0.0006202684016898274, 0.022808432579040527, -0.017661139369010925, 0.03322286531329155, 0.018732313066720963, -0.02776184119284153, 0.055279966443777084, 0.022999033331871033, -0.034620270133018494, -0.021649587899446487, 0.0348285473883152, -0.02487785555422306, 0.00535616697743535, 0.012959402054548264, -0.031428493559360504, -0.03366561979055405, 0.007050513755530119, 0.0029276609420776367, 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979 | 979 | ['Yaroslav Ganin', 'Victor Lempitsky'] | 1409.7495v2 | Top-performing deep architectures are trained on massive amounts of labeled
data. In the absence of labeled data for a certain task, domain adaptation
often provides an attractive option given that labeled data of similar nature
but from a different domain (e.g. synthetic images) are available. Here, we
propose a new approach to domain adaptation in deep architectures that can be
trained on large amount of labeled data from the source domain and large amount
of unlabeled data from the target domain (no labeled target-domain data is
necessary).
As the training progresses, the approach promotes the emergence of "deep"
features that are (i) discriminative for the main learning task on the source
domain and (ii) invariant with respect to the shift between the domains. We
show that this adaptation behaviour can be achieved in almost any feed-forward
model by augmenting it with few standard layers and a simple new gradient
reversal layer. The resulting augmented architecture can be trained using
standard backpropagation.
Overall, the approach can be implemented with little effort using any of the
deep-learning packages. The method performs very well in a series of image
classification experiments, achieving adaptation effect in the presence of big
domain shifts and outperforming previous state-of-the-art on Office datasets. | Unsupervised Domain Adaptation by Backpropagation | 2,014 | http://arxiv.org/pdf/1409.7495v2 | Title Unsupervised Domain Adaptation Backpropagation Summary Topperforming deep architecture trained massive amount labeled data absence labeled data certain task domain adaptation often provides attractive option given labeled data similar nature different domain eg synthetic image available propose new approach domain adaptation deep architecture trained large amount labeled data source domain large amount unlabeled data target domain labeled targetdomain data necessary training progress approach promotes emergence deep feature discriminative main learning task source domain ii invariant respect shift domain show adaptation behaviour achieved almost feedforward model augmenting standard layer simple new gradient reversal layer resulting augmented architecture trained using standard backpropagation Overall approach implemented little effort using deeplearning package method performs well series image classification experiment achieving adaptation effect presence big domain shift outperforming previous stateoftheart Office datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.012223862111568451, 0.02730708196759224, -0.029504571110010147, 0.002959083067253232, 0.022374561056494713, 0.013824899680912495, 0.06219928711652756, 0.005808903370052576, -0.025141624733805656, -0.011068616062402725, -0.04348711669445038, 0.010070228949189186, -0.03426011651754379, 0.07055418193340302, 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980 | 980 | ['Sherjil Ozair', 'Yoshua Bengio'] | 1410.0630v1 | For discrete data, the likelihood $P(x)$ can be rewritten exactly and
parametrized into $P(X = x) = P(X = x | H = f(x)) P(H = f(x))$ if $P(X | H)$
has enough capacity to put no probability mass on any $x'$ for which $f(x')\neq
f(x)$, where $f(\cdot)$ is a deterministic discrete function. The log of the
first factor gives rise to the log-likelihood reconstruction error of an
autoencoder with $f(\cdot)$ as the encoder and $P(X|H)$ as the (probabilistic)
decoder. The log of the second term can be seen as a regularizer on the encoded
activations $h=f(x)$, e.g., as in sparse autoencoders. Both encoder and decoder
can be represented by a deep neural network and trained to maximize the average
of the optimal log-likelihood $\log p(x)$. The objective is to learn an encoder
$f(\cdot)$ that maps $X$ to $f(X)$ that has a much simpler distribution than
$X$ itself, estimated by $P(H)$. This "flattens the manifold" or concentrates
probability mass in a smaller number of (relevant) dimensions over which the
distribution factorizes. Generating samples from the model is straightforward
using ancestral sampling. One challenge is that regular back-propagation cannot
be used to obtain the gradient on the parameters of the encoder, but we find
that using the straight-through estimator works well here. We also find that
although optimizing a single level of such architecture may be difficult, much
better results can be obtained by pre-training and stacking them, gradually
transforming the data distribution into one that is more easily captured by a
simple parametric model. | Deep Directed Generative Autoencoders | 2,014 | http://arxiv.org/pdf/1410.0630v1 | Title Deep Directed Generative Autoencoders Summary discrete data likelihood Px rewritten exactly parametrized PX x PX x H fx PH fx PX H enough capacity put probability mass x fxneq fx fcdot deterministic discrete function log first factor give rise loglikelihood reconstruction error autoencoder fcdot encoder PXH probabilistic decoder log second term seen regularizer encoded activation hfx eg sparse autoencoders encoder decoder represented deep neural network trained maximize average optimal loglikelihood log px objective learn encoder fcdot map X fX much simpler distribution X estimated PH flattens manifold concentrate probability mass smaller number relevant dimension distribution factorizes Generating sample model straightforward using ancestral sampling One challenge regular backpropagation cannot used obtain gradient parameter encoder find using straightthrough estimator work well also find although optimizing single level architecture may difficult much better result obtained pretraining stacking gradually transforming data distribution one easily captured simple parametric 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 | [-0.04753543436527252, 0.08173410594463348, -0.012823665514588356, 0.012111885473132133, 0.026507612317800522, -0.01866910606622696, 0.040435802191495895, -0.017728904262185097, -0.07300161570310593, 0.029092561453580856, 0.011009586974978447, 0.005185193847864866, 0.002752364845946431, 0.08532621711492538, 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981 | 981 | ['Pankaj Mehta', 'David J. Schwab'] | 1410.3831v1 | Deep learning is a broad set of techniques that uses multiple layers of
representation to automatically learn relevant features directly from
structured data. Recently, such techniques have yielded record-breaking results
on a diverse set of difficult machine learning tasks in computer vision, speech
recognition, and natural language processing. Despite the enormous success of
deep learning, relatively little is understood theoretically about why these
techniques are so successful at feature learning and compression. Here, we show
that deep learning is intimately related to one of the most important and
successful techniques in theoretical physics, the renormalization group (RG).
RG is an iterative coarse-graining scheme that allows for the extraction of
relevant features (i.e. operators) as a physical system is examined at
different length scales. We construct an exact mapping from the variational
renormalization group, first introduced by Kadanoff, and deep learning
architectures based on Restricted Boltzmann Machines (RBMs). We illustrate
these ideas using the nearest-neighbor Ising Model in one and two-dimensions.
Our results suggests that deep learning algorithms may be employing a
generalized RG-like scheme to learn relevant features from data. | An exact mapping between the Variational Renormalization Group and Deep
Learning | 2,014 | http://arxiv.org/pdf/1410.3831v1 | Title exact mapping Variational Renormalization Group Deep Learning Summary Deep learning broad set technique us multiple layer representation automatically learn relevant feature directly structured data Recently technique yielded recordbreaking result diverse set difficult machine learning task computer vision speech recognition natural language processing Despite enormous success deep learning relatively little understood theoretically technique successful feature learning compression show deep learning intimately related one important successful technique theoretical physic renormalization group RG RG iterative coarsegraining scheme allows extraction relevant feature ie operator physical system examined different length scale construct exact mapping variational renormalization group first introduced Kadanoff deep learning architecture based Restricted Boltzmann Machines RBMs illustrate idea using nearestneighbor Ising Model one twodimensions result suggests deep learning algorithm may employing generalized RGlike scheme learn relevant feature 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 | [-0.0023762525524944067, 0.020458418875932693, -0.03103753924369812, 0.032984085381031036, -0.006900325883179903, 0.008116996847093105, 0.007856132462620735, -0.0002189859951613471, -0.06804229319095612, 0.02625308558344841, -0.01627558283507824, -0.03649413585662842, -0.012955756857991219, 0.0626741424202919, 0.011091265827417374, -0.08173298835754395, 0.021357383579015732, 0.005347761325538158, 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982 | 982 | ['Erte Pan', 'Zhu Han'] | 1410.4599v2 | In this paper, we present an infinite hierarchical non-parametric Bayesian
model to extract the hidden factors over observed data, where the number of
hidden factors for each layer is unknown and can be potentially infinite.
Moreover, the number of layers can also be infinite. We construct the model
structure that allows continuous values for the hidden factors and weights,
which makes the model suitable for various applications. We use the
Metropolis-Hastings method to infer the model structure. Then the performance
of the algorithm is evaluated by the experiments. Simulation results show that
the model fits the underlying structure of simulated data. | Non-parametric Bayesian Learning with Deep Learning Structure and Its
Applications in Wireless Networks | 2,014 | http://arxiv.org/pdf/1410.4599v2 | Title Nonparametric Bayesian Learning Deep Learning Structure Applications Wireless Networks Summary paper present infinite hierarchical nonparametric Bayesian model extract hidden factor observed data number hidden factor layer unknown potentially infinite Moreover number layer also infinite construct model structure allows continuous value hidden factor weight make model suitable various application use MetropolisHastings method infer model structure performance algorithm evaluated experiment Simulation result show model fit underlying structure simulated 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 | [-0.06093990057706833, 0.016605103388428688, -0.0024285053368657827, -0.014800445176661015, -0.010646031238138676, -0.004425301216542721, 0.03849741071462631, -0.03221261501312256, -0.05683831870555878, 0.002845281967893243, -0.011117312125861645, 0.017581436783075333, 0.023697085678577423, 0.03657323867082596, 0.00429582828655839, 0.008928102441132069, -0.018909528851509094, -0.048442114144563675, 0.007996040396392345, 0.005713042337447405, -0.029901742935180664, -0.012865730561316013, 0.00016323552699759603, 0.017098236829042435, -0.04023035988211632, -0.007045662496238947, -0.04952981323003769, 0.03531942889094353, 0.014331545680761337, -0.06381697952747345, 0.03514503687620163, -0.0188111811876297, -0.006069534923881292, 0.009095992892980576, 1.981607510970207e-06, 0.03505586460232735, 0.0019039332401007414, -0.03432074561715126, -0.012036433443427086, 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983 | 983 | ['Daniel Povey', 'Xiaohui Zhang', 'Sanjeev Khudanpur'] | 1410.7455v8 | We describe the neural-network training framework used in the Kaldi speech
recognition toolkit, which is geared towards training DNNs with large amounts
of training data using multiple GPU-equipped or multi-core machines. In order
to be as hardware-agnostic as possible, we needed a way to use multiple
machines without generating excessive network traffic. Our method is to average
the neural network parameters periodically (typically every minute or two), and
redistribute the averaged parameters to the machines for further training. Each
machine sees different data. By itself, this method does not work very well.
However, we have another method, an approximate and efficient implementation of
Natural Gradient for Stochastic Gradient Descent (NG-SGD), which seems to allow
our periodic-averaging method to work well, as well as substantially improving
the convergence of SGD on a single machine. | Parallel training of DNNs with Natural Gradient and Parameter Averaging | 2,014 | http://arxiv.org/pdf/1410.7455v8 | Title Parallel training DNNs Natural Gradient Parameter Averaging Summary describe neuralnetwork training framework used Kaldi speech recognition toolkit geared towards training DNNs large amount training data using multiple GPUequipped multicore machine order hardwareagnostic possible needed way use multiple machine without generating excessive network traffic method average neural network parameter periodically typically every minute two redistribute averaged parameter machine training machine see different data method work well However another method approximate efficient implementation Natural Gradient Stochastic Gradient Descent NGSGD seems allow periodicaveraging method work well well substantially improving convergence SGD single 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 Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02147987298667431, 0.03240162879228592, 0.011054708622395992, 0.020620791241526604, -0.007865452207624912, 0.013133641332387924, 0.05932673066854477, -0.039054032415151596, -0.04459226876497269, 0.02164766564965248, -0.03262799605727196, -0.00869940035045147, 0.04856467247009277, -0.004756217356771231, 0.0011192006058990955, -0.07590065896511078, 0.020962581038475037, -0.007968577556312084, -0.022775093093514442, -0.022327996790409088, 0.03787946701049805, 0.025358442217111588, -0.06133668124675751, 0.03301703929901123, -0.02983163297176361, -0.032344549894332886, -0.013128773309290409, -0.019837427884340286, 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984 | 984 | ['Jan Chorowski', 'Dzmitry Bahdanau', 'Kyunghyun Cho', 'Yoshua Bengio'] | 1412.1602v1 | We replace the Hidden Markov Model (HMM) which is traditionally used in in
continuous speech recognition with a bi-directional recurrent neural network
encoder coupled to a recurrent neural network decoder that directly emits a
stream of phonemes. The alignment between the input and output sequences is
established using an attention mechanism: the decoder emits each symbol based
on a context created with a subset of input symbols elected by the attention
mechanism. We report initial results demonstrating that this new approach
achieves phoneme error rates that are comparable to the state-of-the-art
HMM-based decoders, on the TIMIT dataset. | End-to-end Continuous Speech Recognition using Attention-based Recurrent
NN: First Results | 2,014 | http://arxiv.org/pdf/1412.1602v1 | Title Endtoend Continuous Speech Recognition using Attentionbased Recurrent NN First Results Summary replace Hidden Markov Model HMM traditionally used continuous speech recognition bidirectional recurrent neural network encoder coupled recurrent neural network decoder directly emits stream phoneme alignment input output sequence established using attention mechanism decoder emits symbol based context created subset input symbol elected attention mechanism report initial result demonstrating new approach achieves phoneme error rate comparable stateoftheart HMMbased decoder TIMIT 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 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985 | 985 | ['Hanie Sedghi', 'Anima Anandkumar'] | 1412.2693v4 | We provide novel guaranteed approaches for training feedforward neural
networks with sparse connectivity. We leverage on the techniques developed
previously for learning linear networks and show that they can also be
effectively adopted to learn non-linear networks. We operate on the moments
involving label and the score function of the input, and show that their
factorization provably yields the weight matrix of the first layer of a deep
network under mild conditions. In practice, the output of our method can be
employed as effective initializers for gradient descent. | Provable Methods for Training Neural Networks with Sparse Connectivity | 2,014 | http://arxiv.org/pdf/1412.2693v4 | Title Provable Methods Training Neural Networks Sparse Connectivity Summary provide novel guaranteed approach training feedforward neural network sparse connectivity leverage technique developed previously learning linear network show also effectively adopted learn nonlinear network operate moment involving label score function input show factorization provably yield weight matrix first layer deep network mild condition practice output method employed effective initializers gradient descent 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 | [-0.02425442263484001, 0.05809585750102997, 0.002752972301095724, 0.037369973957538605, 0.04178553819656372, -0.06789786368608475, 0.004382973071187735, -0.0024964474141597748, -0.007964118383824825, 0.026224713772535324, 0.0039517381228506565, -0.0015477631241083145, 0.028708918020129204, 0.06455957889556885, 0.028444373980164528, 0.0036080849822610617, -0.012100842781364918, 0.030823703855276108, -0.0486532598733902, -0.004012208431959152, 0.03768489137291908, -0.02052026242017746, 0.0173909030854702, 0.009857839904725552, -0.11324954777956009, 0.022677019238471985, -0.04861139506101608, 0.01526475790888071, 0.010900992900133133, -0.06798093765974045, 0.016596170142292976, 0.02546575292944908, -0.01826806552708149, 0.015984047204256058, 2.35885431720817e-06, -0.0004663185100071132, -0.01377591397613287, -0.0662660300731659, 0.0035377121530473232, 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986 | 986 | ['Hana Ajakan', 'Pascal Germain', 'Hugo Larochelle', 'François Laviolette', 'Mario Marchand'] | 1412.4446v2 | We introduce a new representation learning algorithm suited to the context of
domain adaptation, in which data at training and test time come from similar
but different distributions. Our algorithm is directly inspired by theory on
domain adaptation suggesting that, for effective domain transfer to be
achieved, predictions must be made based on a data representation that cannot
discriminate between the training (source) and test (target) domains. We
propose a training objective that implements this idea in the context of a
neural network, whose hidden layer is trained to be predictive of the
classification task, but uninformative as to the domain of the input. Our
experiments on a sentiment analysis classification benchmark, where the target
domain data available at training time is unlabeled, show that our neural
network for domain adaption algorithm has better performance than either a
standard neural network or an SVM, even if trained on input features extracted
with the state-of-the-art marginalized stacked denoising autoencoders of Chen
et al. (2012). | Domain-Adversarial Neural Networks | 2,014 | http://arxiv.org/pdf/1412.4446v2 | Title DomainAdversarial Neural Networks Summary introduce new representation learning algorithm suited context domain adaptation data training test time come similar different distribution algorithm directly inspired theory domain adaptation suggesting effective domain transfer achieved prediction must made based data representation cannot discriminate training source test target domain propose training objective implement idea context neural network whose hidden layer trained predictive classification task uninformative domain input experiment sentiment analysis classification benchmark target domain data available training time unlabeled show neural network domain adaption algorithm better performance either standard neural network SVM even trained input feature extracted stateoftheart marginalized stacked denoising autoencoders Chen et al 2012 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 | [0.006073937751352787, 0.024887021631002426, -0.03739439323544502, 0.041518259793519974, 0.010741428472101688, -0.015602017752826214, 0.0417422391474247, -0.008342403918504715, -0.01444221194833517, -0.047414060682058334, -0.05214863643050194, -0.007189407013356686, -0.0120255621150136, 0.06032171845436096, 0.004629259463399649, -0.06954500824213028, 0.03153195232152939, 0.05840955674648285, -0.007255472708493471, -0.02895207889378071, -0.0037462576292455196, -0.01912865787744522, 0.010982468724250793, 0.01803649589419365, 0.010362448170781136, -0.007326140999794006, 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987 | 987 | ['Philip Bachman', 'Ouais Alsharif', 'Doina Precup'] | 1412.4864v1 | We formalize the notion of a pseudo-ensemble, a (possibly infinite)
collection of child models spawned from a parent model by perturbing it
according to some noise process. E.g., dropout (Hinton et. al, 2012) in a deep
neural network trains a pseudo-ensemble of child subnetworks generated by
randomly masking nodes in the parent network. We present a novel regularizer
based on making the behavior of a pseudo-ensemble robust with respect to the
noise process generating it. In the fully-supervised setting, our regularizer
matches the performance of dropout. But, unlike dropout, our regularizer
naturally extends to the semi-supervised setting, where it produces
state-of-the-art results. We provide a case study in which we transform the
Recursive Neural Tensor Network of (Socher et. al, 2013) into a
pseudo-ensemble, which significantly improves its performance on a real-world
sentiment analysis benchmark. | Learning with Pseudo-Ensembles | 2,014 | http://arxiv.org/pdf/1412.4864v1 | Title Learning PseudoEnsembles Summary formalize notion pseudoensemble possibly infinite collection child model spawned parent model perturbing according noise process Eg dropout Hinton et al 2012 deep neural network train pseudoensemble child subnetworks generated randomly masking node parent network present novel regularizer based making behavior pseudoensemble robust respect noise process generating fullysupervised setting regularizer match performance dropout unlike dropout regularizer naturally extends semisupervised setting produce stateoftheart result provide case study transform Recursive Neural Tensor Network Socher et al 2013 pseudoensemble significantly improves performance realworld sentiment analysis benchmark Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.009160557761788368, 0.05967608839273453, -0.028614666312932968, 0.03325432538986206, -0.011192547157406807, -0.007061092182993889, 0.003979053348302841, 0.022278467193245888, -0.007695604115724564, 0.001157507300376892, 0.0103920828551054, 0.0007392219849862158, -0.046328116208314896, 0.047495149075984955, -0.002452569082379341, -0.09100336581468582, 0.024488771334290504, 0.005375895649194717, 5.950803097221069e-05, -0.03847259655594826, 0.015194381587207317, -0.02134687267243862, 0.0006768601597286761, 0.038496702909469604, -0.039436060935258865, -0.0440678708255291, -0.007613732945173979, 0.02619149163365364, 0.014995886944234371, -0.017576636746525764, 0.03496623784303665, 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988 | 988 | ['David Sussillo', 'L. F. Abbott'] | 1412.6558v3 | Training very deep networks is an important open problem in machine learning.
One of many difficulties is that the norm of the back-propagated error gradient
can grow or decay exponentially. Here we show that training very deep
feed-forward networks (FFNs) is not as difficult as previously thought. Unlike
when back-propagation is applied to a recurrent network, application to an FFN
amounts to multiplying the error gradient by a different random matrix at each
layer. We show that the successive application of correctly scaled random
matrices to an initial vector results in a random walk of the log of the norm
of the resulting vectors, and we compute the scaling that makes this walk
unbiased. The variance of the random walk grows only linearly with network
depth and is inversely proportional to the size of each layer. Practically,
this implies a gradient whose log-norm scales with the square root of the
network depth and shows that the vanishing gradient problem can be mitigated by
increasing the width of the layers. Mathematical analyses and experimental
results using stochastic gradient descent to optimize tasks related to the
MNIST and TIMIT datasets are provided to support these claims. Equations for
the optimal matrix scaling are provided for the linear and ReLU cases. | Random Walk Initialization for Training Very Deep Feedforward Networks | 2,014 | http://arxiv.org/pdf/1412.6558v3 | Title Random Walk Initialization Training Deep Feedforward Networks Summary Training deep network important open problem machine learning One many difficulty norm backpropagated error gradient grow decay exponentially show training deep feedforward network FFNs difficult previously thought Unlike backpropagation applied recurrent network application FFN amount multiplying error gradient different random matrix layer show successive application correctly scaled random matrix initial vector result random walk log norm resulting vector compute scaling make walk unbiased variance random walk grows linearly network depth inversely proportional size layer Practically implies gradient whose lognorm scale square root network depth show vanishing gradient problem mitigated increasing width layer Mathematical analysis experimental result using stochastic gradient descent optimize task related MNIST TIMIT datasets provided support claim Equations optimal matrix scaling provided linear ReLU case 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 | [-0.0044240802526474, 0.0069373114965856075, -0.013173596933484077, -0.0073425062000751495, 0.009337766095995903, -0.00873428676277399, 0.028067974373698235, -0.035599641501903534, 0.0035190260969102383, 0.02239438146352768, 0.04211749881505966, -0.011814677156507969, 0.012025756761431694, 0.07086333632469177, 0.023403797298669815, 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989 | 989 | ['Otto Fabius', 'Joost R. van Amersfoort'] | 1412.6581v6 | In this paper we propose a model that combines the strengths of RNNs and
SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used
for efficient, large scale unsupervised learning on time series data, mapping
the time series data to a latent vector representation. The model is
generative, such that data can be generated from samples of the latent space.
An important contribution of this work is that the model can make use of
unlabeled data in order to facilitate supervised training of RNNs by
initialising the weights and network state. | Variational Recurrent Auto-Encoders | 2,014 | http://arxiv.org/pdf/1412.6581v6 | Title Variational Recurrent AutoEncoders Summary paper propose model combine strength RNNs SGVB Variational Recurrent AutoEncoder VRAE model used efficient large scale unsupervised learning time series data mapping time series data latent vector representation model generative data generated sample latent space important contribution work model make use unlabeled data order facilitate supervised training RNNs initialising weight network state Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name 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990 | 990 | ['Jan Rudy', 'Weiguang Ding', 'Daniel Jiwoong Im', 'Graham W. Taylor'] | 1412.6630v2 | Regularization is essential when training large neural networks. As deep
neural networks can be mathematically interpreted as universal function
approximators, they are effective at memorizing sampling noise in the training
data. This results in poor generalization to unseen data. Therefore, it is no
surprise that a new regularization technique, Dropout, was partially
responsible for the now-ubiquitous winning entry to ImageNet 2012 by the
University of Toronto. Currently, Dropout (and related methods such as
DropConnect) are the most effective means of regularizing large neural
networks. These amount to efficiently visiting a large number of related models
at training time, while aggregating them to a single predictor at test time.
The proposed FaMe model aims to apply a similar strategy, yet learns a
factorization of each weight matrix such that the factors are robust to noise. | Neural Network Regularization via Robust Weight Factorization | 2,014 | http://arxiv.org/pdf/1412.6630v2 | Title Neural Network Regularization via Robust Weight Factorization Summary Regularization essential training large neural network deep neural network mathematically interpreted universal function approximators effective memorizing sampling noise training data result poor generalization unseen data Therefore surprise new regularization technique Dropout partially responsible nowubiquitous winning entry ImageNet 2012 University Toronto Currently Dropout related method DropConnect effective mean regularizing large neural network amount efficiently visiting large number related model training time aggregating single predictor test time proposed FaMe model aim apply similar strategy yet learns factorization weight matrix factor robust noise 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 | [-0.019928568974137306, 0.06103457137942314, -0.019916892051696777, 0.03545447811484337, 0.054630864411592484, -0.026063794270157814, 0.03763958811759949, -0.020228462293744087, -0.031058156862854958, 0.05151055380702019, 0.014162488281726837, -0.0016411741962656379, 0.004072746727615595, 0.05240050330758095, 0.031838174909353256, -0.048698972910642624, -0.010729736648499966, 0.0033329392317682505, -0.042995914816856384, -0.024540681391954422, 0.013822807930409908, -0.043803922832012177, 0.03824835270643234, 0.05745210126042366, -0.06140504777431488, 0.0403532013297081, -0.001593664288520813, 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991 | 991 | ['Shin-ichi Maeda'] | 1412.7003v3 | Dropout is one of the key techniques to prevent the learning from
overfitting. It is explained that dropout works as a kind of modified L2
regularization. Here, we shed light on the dropout from Bayesian standpoint.
Bayesian interpretation enables us to optimize the dropout rate, which is
beneficial for learning of weight parameters and prediction after learning. The
experiment result also encourages the optimization of the dropout. | A Bayesian encourages dropout | 2,014 | http://arxiv.org/pdf/1412.7003v3 | Title Bayesian encourages dropout Summary Dropout one key technique prevent learning overfitting explained dropout work kind modified L2 regularization shed light dropout Bayesian standpoint Bayesian interpretation enables u optimize dropout rate beneficial learning weight parameter prediction learning experiment result also encourages optimization dropout 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 | [-0.011000306345522404, 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992 | 992 | ['Zichao Yang', 'Marcin Moczulski', 'Misha Denil', 'Nando de Freitas', 'Alex Smola', 'Le Song', 'Ziyu Wang'] | 1412.7149v4 | The fully connected layers of a deep convolutional neural network typically
contain over 90% of the network parameters, and consume the majority of the
memory required to store the network parameters. Reducing the number of
parameters while preserving essentially the same predictive performance is
critically important for operating deep neural networks in memory constrained
environments such as GPUs or embedded devices.
In this paper we show how kernel methods, in particular a single Fastfood
layer, can be used to replace all fully connected layers in a deep
convolutional neural network. This novel Fastfood layer is also end-to-end
trainable in conjunction with convolutional layers, allowing us to combine them
into a new architecture, named deep fried convolutional networks, which
substantially reduces the memory footprint of convolutional networks trained on
MNIST and ImageNet with no drop in predictive performance. | Deep Fried Convnets | 2,014 | http://arxiv.org/pdf/1412.7149v4 | Title Deep Fried Convnets Summary fully connected layer deep convolutional neural network typically contain 90 network parameter consume majority memory required store network parameter Reducing number parameter preserving essentially predictive performance critically important operating deep neural network memory constrained environment GPUs embedded device paper show kernel method particular single Fastfood layer used replace fully connected layer deep convolutional neural network novel Fastfood layer also endtoend trainable conjunction convolutional layer allowing u combine new architecture named deep fried convolutional network substantially reduces memory footprint convolutional network trained MNIST ImageNet drop predictive performance 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 | [0.003953681327402592, 0.04814979061484337, -0.007932237349450588, 0.0726790726184845, 0.030183227732777596, -0.013543183915317059, 0.023007377982139587, 0.00977381318807602, -0.007258429657667875, -0.01303425244987011, -0.04898957535624504, 0.016132162883877754, 0.009048628620803356, 0.06714611500501633, 0.02202516980469227, -0.020178064703941345, 0.023275362327694893, 0.07245812565088272, -0.046606432646512985, -0.004681827500462532, 0.02132013998925686, -0.002778449794277549, 0.06049051135778427, -0.00707693537697196, -0.04140576347708702, -0.013697676360607147, -0.01843194104731083, 0.032987259328365326, 0.022326231002807617, 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993 | 993 | ['Antti Rasmus', 'Harri Valpola', 'Tapani Raiko'] | 1504.08215v1 | We show how a deep denoising autoencoder with lateral connections can be used
as an auxiliary unsupervised learning task to support supervised learning. The
proposed model is trained to minimize simultaneously the sum of supervised and
unsupervised cost functions by back-propagation, avoiding the need for
layer-wise pretraining. It improves the state of the art significantly in the
permutation-invariant MNIST classification task. | Lateral Connections in Denoising Autoencoders Support Supervised
Learning | 2,015 | http://arxiv.org/pdf/1504.08215v1 | Title Lateral Connections Denoising Autoencoders Support Supervised Learning Summary show deep denoising autoencoder lateral connection used auxiliary unsupervised learning task support supervised learning proposed model trained minimize simultaneously sum supervised unsupervised cost function backpropagation avoiding need layerwise pretraining improves state art significantly permutationinvariant MNIST classification 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 | [-0.04022599756717682, 0.03306163102388382, 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994 | 994 | ['Raja Giryes', 'Guillermo Sapiro', 'Alex M. Bronstein'] | 1504.08291v5 | Three important properties of a classification machinery are: (i) the system
preserves the core information of the input data; (ii) the training examples
convey information about unseen data; and (iii) the system is able to treat
differently points from different classes. In this work we show that these
fundamental properties are satisfied by the architecture of deep neural
networks. We formally prove that these networks with random Gaussian weights
perform a distance-preserving embedding of the data, with a special treatment
for in-class and out-of-class data. Similar points at the input of the network
are likely to have a similar output. The theoretical analysis of deep networks
here presented exploits tools used in the compressed sensing and dictionary
learning literature, thereby making a formal connection between these important
topics. The derived results allow drawing conclusions on the metric learning
properties of the network and their relation to its structure, as well as
providing bounds on the required size of the training set such that the
training examples would represent faithfully the unseen data. The results are
validated with state-of-the-art trained networks. | Deep Neural Networks with Random Gaussian Weights: A Universal
Classification Strategy? | 2,015 | http://arxiv.org/pdf/1504.08291v5 | Title Deep Neural Networks Random Gaussian Weights Universal Classification Strategy Summary Three important property classification machinery system preserve core information input data ii training example convey information unseen data iii system able treat differently point different class work show fundamental property satisfied architecture deep neural network formally prove network random Gaussian weight perform distancepreserving embedding data special treatment inclass outofclass data Similar point input network likely similar output theoretical analysis deep network presented exploit tool used compressed sensing dictionary learning literature thereby making formal connection important topic derived result allow drawing conclusion metric learning property network relation structure well providing bound required size training set training example would represent faithfully unseen data result validated stateoftheart trained 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 | [-0.01569700427353382, 0.03660830110311508, -0.02110263891518116, 0.015260525979101658, -0.02043360099196434, 0.0010939789935946465, 0.0314251184463501, 0.0053098187781870365, -0.01996506005525589, 0.006346167530864477, 0.031633105129003525, -0.007351071573793888, 0.022093666717410088, 0.03891381621360779, 0.013218160718679428, -0.05890187248587608, 0.0023387358523905277, 0.04277723282575607, -0.033996257930994034, -0.04301109164953232, -0.013345593586564064, -0.048373401165008545, 0.0005730908014811575, 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995 | 995 | ['Zhiguang Wang', 'Tim Oates'] | 1506.00327v1 | Inspired by recent successes of deep learning in computer vision, we propose
a novel framework for encoding time series as different types of images,
namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov
Transition Fields (MTF). This enables the use of techniques from computer
vision for time series classification and imputation. We used Tiled
Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn
high-level features from the individual and compound GASF-GADF-MTF images. Our
approaches achieve highly competitive results when compared to nine of the
current best time series classification approaches. Inspired by the bijection
property of GASF on 0/1 rescaled data, we train Denoised Auto-encoders (DA) on
the GASF images of four standard and one synthesized compound dataset. The
imputation MSE on test data is reduced by 12.18%-48.02% when compared to using
the raw data. An analysis of the features and weights learned via tiled CNNs
and DAs explains why the approaches work. | Imaging Time-Series to Improve Classification and Imputation | 2,015 | http://arxiv.org/pdf/1506.00327v1 | Title Imaging TimeSeries Improve Classification Imputation Summary Inspired recent success deep learning computer vision propose novel framework encoding time series different type image namely Gramian Angular SummationDifference Fields GASFGADF Markov Transition Fields MTF enables use technique computer vision time series classification imputation used Tiled Convolutional Neural Networks tiled CNNs 20 standard datasets learn highlevel feature individual compound GASFGADFMTF image approach achieve highly competitive result compared nine current best time series classification approach Inspired bijection property GASF 01 rescaled data train Denoised Autoencoders DA GASF image four standard one synthesized compound dataset imputation MSE test data reduced 12184802 compared using raw data analysis feature weight learned via tiled CNNs DAs explains approach work Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03319625183939934, 0.09899730235338211, -0.005162252578884363, 0.042760130017995834, 0.0001874628651421517, 0.036478545516729355, 0.049345582723617554, 0.04922117292881012, 0.0013519965577870607, 0.03677152097225189, 0.034278277307748795, -0.02306956611573696, 0.03465266898274422, 0.049860235303640366, 0.007589690387248993, -0.09835445135831833, 0.021774737164378166, 0.03581489250063896, -0.032492008060216904, -0.03198329731822014, 0.016233066096901894, -0.03974786400794983, 0.0011664523044601083, 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996 | 996 | ['Bart van Merriënboer', 'Dzmitry Bahdanau', 'Vincent Dumoulin', 'Dmitriy Serdyuk', 'David Warde-Farley', 'Jan Chorowski', 'Yoshua Bengio'] | 1506.00619v1 | We introduce two Python frameworks to train neural networks on large
datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler
with CUDA-support. It facilitates the training of complex neural network models
by providing parametrized Theano operations, attaching metadata to Theano's
symbolic computational graph, and providing an extensive set of utilities to
assist training the networks, e.g. training algorithms, logging, monitoring,
visualization, and serialization. Fuel provides a standard format for machine
learning datasets. It allows the user to easily iterate over large datasets,
performing many types of pre-processing on the fly. | Blocks and Fuel: Frameworks for deep learning | 2,015 | http://arxiv.org/pdf/1506.00619v1 | Title Blocks Fuel Frameworks deep learning Summary introduce two Python framework train neural network large datasets Blocks Fuel Blocks based Theano linear algebra compiler CUDAsupport facilitates training complex neural network model providing parametrized Theano operation attaching metadata Theanos symbolic computational graph providing extensive set utility assist training network eg training algorithm logging monitoring visualization serialization Fuel provides standard format machine learning datasets allows user easily iterate large datasets performing many type preprocessing fly 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 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997 | 997 | ['Zhiguang Wang', 'Tim Oates', 'James Lo'] | 1506.02690v3 | This paper proposes a set of new error criteria and learning approaches,
Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex
optimization problem in training deep neural networks (DNNs). Theoretically, we
demonstrate its effectiveness on global and local convexity lower-bounded by
the standard $L_p$-norm error. By analyzing the gradient on the convexity index
$\lambda$, we explain the reason why to learn $\lambda$ adaptively using
gradient descent works. In practice, we show how this method improves training
of deep neural networks to solve visual recognition tasks on the MNIST and
CIFAR-10 datasets. Without using pretraining or other tricks, we obtain results
comparable or superior to those reported in recent literature on the same tasks
using standard ConvNets + MSE/cross entropy. Performance on deep/shallow
multilayer perceptrons and Denoised Auto-encoders is also explored. ANRAT can
be combined with other quasi-Newton training methods, innovative network
variants, regularization techniques and other specific tricks in DNNs. Other
than unsupervised pretraining, it provides a new perspective to address the
non-convex optimization problem in DNNs. | Adaptive Normalized Risk-Averting Training For Deep Neural Networks | 2,015 | http://arxiv.org/pdf/1506.02690v3 | Title Adaptive Normalized RiskAverting Training Deep Neural Networks Summary paper proposes set new error criterion learning approach Adaptive Normalized RiskAverting Training ANRAT attack nonconvex optimization problem training deep neural network DNNs Theoretically demonstrate effectiveness global local convexity lowerbounded standard Lpnorm error analyzing gradient convexity index lambda explain reason learn lambda adaptively using gradient descent work practice show method improves training deep neural network solve visual recognition task MNIST CIFAR10 datasets Without using pretraining trick obtain result comparable superior reported recent literature task using standard ConvNets MSEcross entropy Performance deepshallow multilayer perceptrons Denoised Autoencoders also explored ANRAT combined quasiNewton training method innovative network variant regularization technique specific trick DNNs unsupervised pretraining provides new perspective address nonconvex optimization problem DNNs 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 | [-0.03892209753394127, 0.02396414242684841, -0.007979538291692734, 0.04277722164988518, 0.02514675445854664, 0.005754395853728056, 0.03764493763446808, -0.014654085971415043, -0.04229920357465744, 0.021381240338087082, -0.03835045546293259, 0.01774756796658039, -0.02433912083506584, 0.010545829311013222, 0.03758712857961655, -0.04735001549124718, -0.007297920063138008, 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998 | 998 | ['Marylou Gabrié', 'Eric W. Tramel', 'Florent Krzakala'] | 1506.02914v2 | Restricted Boltzmann machines are undirected neural networks which have been
shown to be effective in many applications, including serving as
initializations for training deep multi-layer neural networks. One of the main
reasons for their success is the existence of efficient and practical
stochastic algorithms, such as contrastive divergence, for unsupervised
training. We propose an alternative deterministic iterative procedure based on
an improved mean field method from statistical physics known as the
Thouless-Anderson-Palmer approach. We demonstrate that our algorithm provides
performance equal to, and sometimes superior to, persistent contrastive
divergence, while also providing a clear and easy to evaluate objective
function. We believe that this strategy can be easily generalized to other
models as well as to more accurate higher-order approximations, paving the way
for systematic improvements in training Boltzmann machines with hidden units. | Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer
Free Energy | 2,015 | http://arxiv.org/pdf/1506.02914v2 | Title Training Restricted Boltzmann Machines via ThoulessAndersonPalmer Free Energy Summary Restricted Boltzmann machine undirected neural network shown effective many application including serving initialization training deep multilayer neural network One main reason success existence efficient practical stochastic algorithm contrastive divergence unsupervised training propose alternative deterministic iterative procedure based improved mean field method statistical physic known ThoulessAndersonPalmer approach demonstrate algorithm provides performance equal sometimes superior persistent contrastive divergence also providing clear easy evaluate objective function believe strategy easily generalized model well accurate higherorder approximation paving way systematic improvement training Boltzmann machine hidden unit 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 | [-0.017963889986276627, 0.01140306144952774, -0.014971827156841755, 0.04444911330938339, -0.01435950305312872, 0.0023342478089034557, 0.062007542699575424, -0.005196407437324524, -0.01400814950466156, -0.0021559600718319416, -0.030290434136986732, -0.00036210299003869295, 0.024807089939713478, 0.025061191990971565, 0.020926108583807945, -0.040460363030433655, -0.01035576406866312, 0.017090465873479843, 0.014843589626252651, -0.04578815773129463, 0.05346958711743355, -0.033177699893713, -0.005459549371153116, -0.015900472179055214, -0.0512361079454422, 0.007447986397892237, -0.0170430950820446, 0.0026312358677387238, 0.073989637196064, 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999 | 999 | ['Oriol Vinyals', 'Meire Fortunato', 'Navdeep Jaitly'] | 1506.03134v2 | We introduce a new neural architecture to learn the conditional probability
of an output sequence with elements that are discrete tokens corresponding to
positions in an input sequence. Such problems cannot be trivially addressed by
existent approaches such as sequence-to-sequence and Neural Turing Machines,
because the number of target classes in each step of the output depends on the
length of the input, which is variable. Problems such as sorting variable sized
sequences, and various combinatorial optimization problems belong to this
class. Our model solves the problem of variable size output dictionaries using
a recently proposed mechanism of neural attention. It differs from the previous
attention attempts in that, instead of using attention to blend hidden units of
an encoder to a context vector at each decoder step, it uses attention as a
pointer to select a member of the input sequence as the output. We call this
architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn
approximate solutions to three challenging geometric problems -- finding planar
convex hulls, computing Delaunay triangulations, and the planar Travelling
Salesman Problem -- using training examples alone. Ptr-Nets not only improve
over sequence-to-sequence with input attention, but also allow us to generalize
to variable size output dictionaries. We show that the learnt models generalize
beyond the maximum lengths they were trained on. We hope our results on these
tasks will encourage a broader exploration of neural learning for discrete
problems. | Pointer Networks | 2,015 | http://arxiv.org/pdf/1506.03134v2 | Title Pointer Networks Summary introduce new neural architecture learn conditional probability output sequence element discrete token corresponding position input sequence problem cannot trivially addressed existent approach sequencetosequence Neural Turing Machines number target class step output depends length input variable Problems sorting variable sized sequence various combinatorial optimization problem belong class model solves problem variable size output dictionary using recently proposed mechanism neural attention differs previous attention attempt instead using attention blend hidden unit encoder context vector decoder step us attention pointer select member input sequence output call architecture Pointer Net PtrNet show PtrNets used learn approximate solution three challenging geometric problem finding planar convex hull computing Delaunay triangulation planar Travelling Salesman Problem using training example alone PtrNets improve sequencetosequence input attention also allow u generalize variable size output dictionary show learnt model generalize beyond maximum length trained hope result task encourage broader exploration neural learning discrete 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 | [0.003444165922701359, 0.021055107936263084, -0.022053953260183334, 0.05184723064303398, -0.019682303071022034, -0.03767344728112221, -0.007297187112271786, -0.029132695868611336, -0.04402274638414383, 0.011482217349112034, 0.03488033637404442, 0.014378183521330357, 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Subsets and Splits