Unnamed: 0.1 int64 0 41k | Unnamed: 0 int64 0 41k | author stringlengths 9 1.39k | id stringlengths 11 18 | summary stringlengths 25 3.66k | title stringlengths 4 258 | year int64 1.99k 2.02k | arxiv_url stringlengths 32 39 | info stringlengths 523 3.18k | embeddings stringlengths 16.9k 17.1k |
|---|---|---|---|---|---|---|---|---|---|
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) l... | 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 lea... | [-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, -0.016805088147... |
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-lin... | 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 el... | [-0.01175389438867569, 0.012245922349393368, -0.016391614452004433, 0.06159992888569832, -0.029291750863194466, -0.04699232801795006, 0.03928838297724724, -0.012378379702568054, -0.03245335817337036, 0.033251505345106125, 0.02554790861904621, -0.012750370427966118, 0.005581865087151527, 0.035941287875175476, 0.01612184... |
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 un... | 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 ... | [-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, 0.0088... |
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 quan... | 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 grow... | [-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, 0.025093082338... |
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 cli... | 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 techniqu... | [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, 0.07887084782... |
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... | 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 s... | [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, 0.0296381805... |
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 greatl... | 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... | [-0.017659468576312065, 0.04269912838935852, 0.004770276136696339, 0.04584232717752457, -0.03638337180018425, -0.019156763330101967, 0.02794671803712845, 0.010106617584824562, -0.04143384471535683, 0.022138074040412903, 0.004321425687521696, 0.02169121615588665, 0.01978026144206524, 0.053596775978803635, 0.048114225268... |
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 m... | 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 ... | [-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, 0.0183857977... |
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 aggregat... | 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 exponent... | [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, 0.0343609675... |
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 wit... | 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 exponenti... | [-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, 0.07929632812... |
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 th... | 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 supervise... | [-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, 0.011684... |
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 stochast... | 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... | [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, -0.00094968... |
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 netw... | 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 incorpora... | [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, 0.00167819787... |
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... | 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 m... | [0.038477201014757156, -0.022839749231934547, -0.019205721095204353, 0.04534827545285225, -0.05568360164761543, -0.02706184610724449, 0.06314827501773834, -0.037587426602840424, 0.027869094163179398, -0.06569738686084747, 0.04174518957734108, -0.03659254312515259, -0.011877326294779778, 0.11472046375274658, 0.021196575... |
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, th... | 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 resul... | [-0.01810164377093315, 0.044370271265506744, -0.03122018091380596, 0.03759416937828064, -0.0023366259410977364, -0.043730318546295166, 0.043661925941705704, -0.03650018945336342, 0.0038855327293276787, -0.005678831599652767, 0.012616811320185661, 0.017511045560240746, 0.013287299312651157, 0.05987581983208656, 0.068602... |
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 a... | 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 feat... | [-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, 0.03180719... |
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 application... | 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... | [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, -0.00538595812395215, 0.0273787... |
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 i... | 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 allowi... | [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, 0.054071109... |
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 se... | 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 sep... | [-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, 0.06... |
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... | 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... | [-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, 0.043786711... |
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 d... | 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 fea... | [-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, 0.012704470... |
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 ... | 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 C... | [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, 0.0218542... |
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 ... | 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 lea... | [-0.024706732481718063, 0.03879423812031746, -0.018889041617512703, -0.0029749220702797174, 0.010376326739788055, 0.020491337403655052, 0.03496556729078293, -0.034987542778253555, 0.01740151271224022, 0.04554947465658188, -0.012515983544290066, -0.02676737867295742, -0.023947542533278465, 0.045896418392658234, 0.033725... |
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 network... | 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 curren... | [-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, 0.0143... |
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 fi... | 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... | [-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, 0.02484... |
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 ... | 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 l... | [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, 0.0242297... |
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.... | 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 Mac... | [-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, 0.03... |
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, w... | 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 challengin... | [-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, 0.030500292778... |
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 a... | 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 ens... | [-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, 0.02480197325348854, -0.05285409... |
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 g... | 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... | [-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, 0.02560720... |
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 th... | 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 distribut... | [-0.0109257148578763, 0.06264904886484146, 0.0043680788949131966, 0.020792240276932716, 0.015698611736297607, -0.01729300431907177, 0.046514227986335754, -0.013023522682487965, -0.04297305643558502, 0.01335382554680109, -0.007356017362326384, -0.006719722878187895, 0.021091314032673836, 0.015134235844016075, 0.03541594... |
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 a... | 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... | [-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, 0.0101448... |
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 ... | 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 se... | [-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.... |
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 lear... | 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 we... | [0.0017769146943464875, 0.045442476868629456, -0.012156003154814243, -0.0026195708196610212, 0.053141359239816666, -0.021875018253922462, 0.03320778161287308, -0.004966241307556629, -0.025984233245253563, 0.04710373654961586, 0.013110204599797726, 0.004445841070264578, 0.016273245215415955, 0.06793177127838135, 0.01967... |
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 ... | 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 multiplic... | [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, 0.010026331059634... |
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 ... | 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 su... | [-0.034978121519088745, 0.04601696506142616, -0.039164163172245026, -3.180909698130563e-05, -0.0012761772377416492, -0.026541996747255325, 0.03580918535590172, -0.016084803268313408, -0.034720487892627716, 0.01704436168074608, 0.03706144541501999, 0.005556359421461821, 0.03033619001507759, 0.047243595123291016, 0.01455... |
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 suc... | 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 d... | [-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, 0.037571411... |
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 ... | 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 ... | [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, -0.00265... |
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 i... | 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 outp... | [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, 0.0613628... |
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 Author... | [-0.047780271619558334, -0.05507497489452362, -0.04572468250989914, 0.022593537345528603, 0.03775206580758095, 0.000574636273086071, 0.008601496927440166, -0.0022422426845878363, 0.01774703525006771, 0.04359229654073715, 0.0014751049457117915, 0.05325311794877052, -0.0011684485943987966, 0.05012005195021629, 0.02129371... |
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 ... | 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 algorith... | [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, -0.0040868... |
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 ov... | 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 fas... | [-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, 0.032638389... |
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 b... | 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 canno... | [0.019201599061489105, 0.034077271819114685, -0.026657868176698685, 0.03172437846660614, 0.0033702924847602844, -0.0048766229301691055, 0.042705897241830826, -0.012422313913702965, -0.018111437559127808, -0.048603322356939316, -0.05782104283571243, -0.010119378566741943, -0.02643941529095173, 0.04546100273728371, -0.00... |
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 n... | 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 p... | [-0.0025042917113751173, 0.028802013024687767, -0.022399017587304115, 0.042485352605581284, -0.027306577190756798, -0.037745244801044464, 0.010233091190457344, -0.01977705955505371, -0.05484988912940025, 0.023186886683106422, 0.008752051740884781, -0.029419537633657455, -0.0033265207894146442, 0.1092197373509407, 0.025... |
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... | 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... | [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, -0.03587032854... |
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 interactio... | 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 intuitivel... | [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, -0.02872847... |
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 weigh... | 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 isometr... | [-0.05608515068888664, 0.02231414243578911, -0.014134297147393227, -0.01099756546318531, 0.026236237958073616, 0.0034751086495816708, 0.0441448874771595, 0.008070431649684906, -0.0455341599881649, 0.062275830656290054, -0.015970904380083084, -0.036701396107673645, -0.0011890233727172017, 0.07825721800327301, -0.0166249... |
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. W... | 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... | [-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, 0.05576291680335998... |
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 usin... | 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 usi... | [0.0008468810701742768, 0.06830428540706635, -0.03854633495211601, 0.028634129092097282, 0.0020932983607053757, -0.010270696133375168, 0.06676343083381653, -0.0018370755715295672, -0.0471697635948658, -0.017862211912870407, -0.056982871145009995, 0.009396135807037354, 0.024866638705134392, 0.025313515216112137, -0.0032... |
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) prin... | 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 g... | [-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, 0.0347... |
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 dif... | 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 ... | [-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, 0.01... |
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 c... | 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 patter... | [-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, 0.016840938... |
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, parame... | 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 un... | [-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, 0.0... |
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. Bu... | 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 sche... | [-0.012143359519541264, 0.09947140514850616, -0.027615344151854515, 0.04559614136815071, 0.035100433975458145, -0.005233585834503174, 0.06767681241035461, -0.0028550676070153713, -0.051131196320056915, 0.008130588568747044, -0.045346204191446304, 0.01610741578042507, -0.000984018319286406, 0.06274141371250153, 0.017853... |
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 2... | 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 biol... | [-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, 0.0416954271... |
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
pai... | 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 r... | [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, 0.013958... |
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 cruc... | 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 crucia... | [-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, 0.0105865... |
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: ea... | 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 reconstru... | [-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, 0.0200691521... |
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 heuri... | 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 altern... | [-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, 0.034481193870306015, 0.0286012236... |
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 ... | 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 ... | [-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, 0.036346625536... |
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 ... | 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 av... | [-0.017805472016334534, 0.09141001850366592, -0.030749650672078133, 0.042688459157943726, -0.03443252295255661, -0.04982313513755798, 0.06820384413003922, -0.051053207367658615, 0.018832944333553314, 0.03526526317000389, -0.01326978113502264, 0.024896224960684776, -0.003951945807784796, 0.08358075469732285, -0.01370614... |
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... | 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 individu... | [-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, 0.002764841... |
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... | 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 v... | [-0.044492967426776886, 0.03256741166114807, 0.00335773010738194, -0.015093979425728321, -0.026445599272847176, 0.003916062880307436, 0.047120507806539536, -0.028478853404521942, -0.07745401561260223, 0.018042949959635735, 0.021035758778452873, -0.02250479720532894, 0.00202585244551301, 0.029762981459498405, 0.03109667... |
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... | 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 lowr... | [-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, 0.0418176... |
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 unsupervis... | 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 ... | [-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, 0.039835456758737564, 0.02974588796... |
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 cult... | 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 exam... | [-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, -0.00112... |
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 unc... | 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 uncert... | [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, 0.002035190... |
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... | 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 recen... | [-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, 0.0346119... |
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. ... | 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 ... | [-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, -0.002376965247... |
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, w... | 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 RNN... | [-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, 0.00846... |
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 ... | 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... | [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, 0.034506436... |
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 consid... | 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 co... | [-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, 0.02... |
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... | 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 un... | [-0.0314551442861557, -0.0031415289267897606, 0.009956634603440762, 0.08124497532844543, 0.003736332291737199, -0.010780435055494308, 0.07215265184640884, -0.033342182636260986, -0.023948101326823235, 0.02725079655647278, -0.01886402815580368, -0.05439789965748787, -0.008117456920444965, 0.047099582850933075, 0.0138787... |
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 valu... | 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 ... | [-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, 0.0056918137... |
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... | 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 approxim... | [-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, 0.0267443... |
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 ... | 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 ... | [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, 0.02196355722... |
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 ... | 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 mechanis... | [-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, 0.026172310... |
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 ... | 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 investig... | [-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, 0.0281115770339... |
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... | 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 ex... | [-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, 0.0187323... |
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 a... | 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 domai... | [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, -0.00909160... |
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 giv... | 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 proba... | [-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, 0.061463844... |
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 n... | 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... | [-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, 0.011091... |
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 allo... | 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 ... | [-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, 0.00429... |
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 ... | 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 w... | [-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, 0.0011192006... |
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 usin... | 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 outp... | [-0.03826582059264183, 0.04862799495458603, 0.02661972865462303, 0.037740353494882584, 0.005929560400545597, -0.0165259912610054, 0.01713675633072853, -0.03426314890384674, -0.035356104373931885, -0.00047799330786801875, -0.037393536418676376, -0.008781025186181068, 0.06878233700990677, 0.05213642120361328, 0.019133005... |
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 sco... | 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... | [-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, 0.028444... |
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, predict... | 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 ... | [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, 0.004629259... |
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 t... | 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 reg... | [-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, -0.00245... |
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-pro... | 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 applie... | [-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, 0.023403... |
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 ... | 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 importa... | [-0.018449978902935982, 0.06967470049858093, -0.02229575254023075, 0.00022500869818031788, -0.01353391632437706, 0.015410826541483402, 0.01675359718501568, -0.018347375094890594, -0.03326942399144173, -0.0021442759316414595, 0.020723307505249977, -0.017815684899687767, 0.03199680149555206, 0.0761215090751648, 0.0360033... |
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 ne... | 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 n... | [-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, 0.0318381... |
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 wei... | 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 resul... | [-0.011000306345522404, 0.04186517000198364, -0.02787579782307148, -0.0035959735978394747, -0.01786748692393303, 0.014211051166057587, 0.04555059224367142, 0.02868657559156418, -0.015029782429337502, -0.0038209096528589725, 0.04330866038799286, 0.05958765745162964, 0.0038028869312256575, 0.05666129291057587, -0.0077756... |
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 ... | 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 cons... | [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, 0.02202516980... |
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 pr... | 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 ... | [-0.04022599756717682, 0.03306163102388382, -0.04414362832903862, 0.050157252699136734, 0.027704793959856033, 0.00064628547988832, 0.022003471851348877, -0.0456169918179512, -0.013479175046086311, 0.006852654740214348, -0.051992353051900864, 0.017777154222130775, 0.037417296320199966, 0.059839680790901184, 0.0396613441... |
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 pro... | 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 prop... | [-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, 0.0132181607... |
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 serie... | 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 se... | [-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, 0.0075896903... |
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 comput... | 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 computat... | [-0.023373784497380257, 0.03020104207098484, -0.039356790482997894, 0.010949266143143177, 0.0060263327322900295, 0.03363582491874695, 0.07424229383468628, 0.018225261941552162, -0.019602276384830475, -0.015069367364048958, 0.038894981145858765, 0.006550794467329979, -0.002780164824798703, 0.09700920432806015, 0.0244682... |
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 ... | 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 low... | [-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, 0.037587128579... |
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 a... | 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 al... | [-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, 0.0... |
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 th... | 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 ... | [0.003444165922701359, 0.021055107936263084, -0.022053953260183334, 0.05184723064303398, -0.019682303071022034, -0.03767344728112221, -0.007297187112271786, -0.029132695868611336, -0.04402274638414383, 0.011482217349112034, 0.03488033637404442, 0.014378183521330357, 0.003138013882562518, 0.055737096816301346, 0.0540490... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.