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 |
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1,000 | 1,000 | ['Pierre Baldi', 'Peter Sadowski'] | 1506.06472v2 | In a physical neural system, where storage and processing are intimately
intertwined, the rules for adjusting the synaptic weights can only depend on
variables that are available locally, such as the activity of the pre- and
post-synaptic neurons, resulting in local learning rules. A systematic
framework for studying t... | A Theory of Local Learning, the Learning Channel, and the Optimality of
Backpropagation | 2,015 | http://arxiv.org/pdf/1506.06472v2 | Title Theory Local Learning Learning Channel Optimality Backpropagation Summary physical neural system storage processing intimately intertwined rule adjusting synaptic weight depend variable available locally activity pre postsynaptic neuron resulting local learning rule systematic framework studying space local learn... | [0.00760247278958559, -0.0013965636026114225, -0.011607717722654343, 0.050949905067682266, 0.007959111593663692, -0.033396292477846146, 0.041610006242990494, -0.011325334198772907, -0.03875073418021202, 0.015684904530644417, -0.019185174256563187, -0.005184045527130365, 0.005953799933195114, 0.04171661287546158, 0.0443... |
1,001 | 1,001 | ['Majid Janzamin', 'Hanie Sedghi', 'Anima Anandkumar'] | 1506.08473v3 | Training neural networks is a challenging non-convex optimization problem,
and backpropagation or gradient descent can get stuck in spurious local optima.
We propose a novel algorithm based on tensor decomposition for guaranteed
training of two-layer neural networks. We provide risk bounds for our proposed
method, with... | Beating the Perils of Non-Convexity: Guaranteed Training of Neural
Networks using Tensor Methods | 2,015 | http://arxiv.org/pdf/1506.08473v3 | Title Beating Perils NonConvexity Guaranteed Training Neural Networks using Tensor Methods Summary Training neural network challenging nonconvex optimization problem backpropagation gradient descent get stuck spurious local optimum propose novel algorithm based tensor decomposition guaranteed training twolayer neural n... | [-0.011419358663260937, 0.06404642760753632, -0.025790397077798843, 0.03155630826950073, 0.006474959198385477, -0.004853494465351105, 0.043829306960105896, 0.0015866432804614305, -0.0032157341483980417, 0.00563422916457057, -0.0043036011047661304, -0.018643533810973167, 0.002184829441830516, -0.0006265626288950443, 0.0... |
1,002 | 1,002 | ['Guillaume Desjardins', 'Karen Simonyan', 'Razvan Pascanu', 'Koray Kavukcuoglu'] | 1507.00210v1 | We introduce Natural Neural Networks, a novel family of algorithms that speed
up convergence by adapting their internal representation during training to
improve conditioning of the Fisher matrix. In particular, we show a specific
example that employs a simple and efficient reparametrization of the neural
network weigh... | Natural Neural Networks | 2,015 | http://arxiv.org/pdf/1507.00210v1 | Title Natural Neural Networks Summary introduce Natural Neural Networks novel family algorithm speed convergence adapting internal representation training improve conditioning Fisher matrix particular show specific example employ simple efficient reparametrization neural network weight implicitly whitening representati... | [-0.023316465318202972, 0.04403283819556236, -0.008403029292821884, 0.04698909819126129, 0.006800304166972637, -0.028438495472073555, 0.009840313345193863, 0.003920476883649826, 0.007934129796922207, 0.025590742006897926, -0.04626111686229706, 0.030625753104686737, 0.024648960679769516, -0.0024142409674823284, 0.023473... |
1,003 | 1,003 | ['Antti Rasmus', 'Harri Valpola', 'Mikko Honkala', 'Mathias Berglund', 'Tapani Raiko'] | 1507.02672v2 | We combine supervised learning with unsupervised learning in deep neural
networks. The proposed model is trained to simultaneously minimize the sum of
supervised and unsupervised cost functions by backpropagation, avoiding the
need for layer-wise pre-training. Our work builds on the Ladder network
proposed by Valpola (... | Semi-Supervised Learning with Ladder Networks | 2,015 | http://arxiv.org/pdf/1507.02672v2 | Title SemiSupervised Learning Ladder Networks Summary combine supervised learning unsupervised learning deep neural network proposed model trained simultaneously minimize sum supervised unsupervised cost function backpropagation avoiding need layerwise pretraining work build Ladder network proposed Valpola 2015 extend ... | [-0.039266400039196014, 0.0740010216832161, 0.00045614270493388176, 0.012034674175083637, 0.013148428872227669, 0.006837876047939062, 0.026733368635177612, -0.05170517787337303, 0.022176256403326988, 0.04301025718450546, -0.04440532997250557, 0.011744906194508076, -0.00117692188359797, 0.06924199312925339, 0.0464486442... |
1,004 | 1,004 | ['Rossella Cancelliere', 'Mario Gai', 'Patrick Gallinari', 'Luca Rubini'] | 1508.06095v1 | In this paper we consider the training of single hidden layer neural networks
by pseudoinversion, which, in spite of its popularity, is sometimes affected by
numerical instability issues. Regularization is known to be effective in such
cases, so that we introduce, in the framework of Tikhonov regularization, a
matricia... | OCReP: An Optimally Conditioned Regularization for Pseudoinversion Based
Neural Training | 2,015 | http://arxiv.org/pdf/1508.06095v1 | Title OCReP Optimally Conditioned Regularization Pseudoinversion Based Neural Training Summary paper consider training single hidden layer neural network pseudoinversion spite popularity sometimes affected numerical instability issue Regularization known effective case introduce framework Tikhonov regularization matric... | [-0.01080725621432066, 0.028573397547006607, -0.0020157950930297375, -0.0019419288728386164, 0.023659273982048035, -0.03376099467277527, 0.0176223274320364, 0.042202483862638474, -0.021722324192523956, 0.01823808066546917, -0.02855880744755268, 0.022159788757562637, 0.0282483771443367, -0.014263048768043518, 0.04104025... |
1,005 | 1,005 | ['Yanping Huang', 'Sai Zhang'] | 1508.07096v1 | Deep learning methods have shown great promise in many practical
applications, ranging from speech recognition, visual object recognition, to
text processing. However, most of the current deep learning methods suffer from
scalability problems for large-scale applications, forcing researchers or users
to focus on small-... | Partitioning Large Scale Deep Belief Networks Using Dropout | 2,015 | http://arxiv.org/pdf/1508.07096v1 | Title Partitioning Large Scale Deep Belief Networks Using Dropout Summary Deep learning method shown great promise many practical application ranging speech recognition visual object recognition text processing However current deep learning method suffer scalability problem largescale application forcing researcher use... | [-0.01561122015118599, 0.05842526629567146, -0.01607709378004074, 0.062400564551353455, -0.03784314915537834, 0.018610667437314987, 0.09203869849443436, -0.02367348037660122, 0.0642785057425499, -0.0076059335842728615, -0.02280244044959545, -0.03673293814063072, -0.02257743664085865, 0.08498809486627579, -0.01718771643... |
1,006 | 1,006 | ['Minhyung Cho', 'Chandra Shekhar Dhir', 'Jaehyung Lee'] | 1509.03475v2 | Multidimensional recurrent neural networks (MDRNNs) have shown a remarkable
performance in the area of speech and handwriting recognition. The performance
of an MDRNN is improved by further increasing its depth, and the difficulty of
learning the deeper network is overcome by using Hessian-free (HF)
optimization. Given... | Hessian-free Optimization for Learning Deep Multidimensional Recurrent
Neural Networks | 2,015 | http://arxiv.org/pdf/1509.03475v2 | Title Hessianfree Optimization Learning Deep Multidimensional Recurrent Neural Networks Summary Multidimensional recurrent neural network MDRNNs shown remarkable performance area speech handwriting recognition performance MDRNN improved increasing depth difficulty learning deeper network overcome using Hessianfree HF o... | [-0.02317788451910019, 0.050238821655511856, -0.0011568962363526225, 0.07076552510261536, 0.006518575828522444, 0.014560024254024029, 0.03768059238791466, -0.01388279814273119, 0.007555229589343071, -0.022181354463100433, -0.041420578956604004, -0.06325777620077133, 0.03561465069651604, -0.010540228337049484, 0.0050849... |
1,007 | 1,007 | ['Nadav Cohen', 'Or Sharir', 'Amnon Shashua'] | 1509.05009v3 | It has long been conjectured that hypotheses spaces suitable for data that is
compositional in nature, such as text or images, may be more efficiently
represented with deep hierarchical networks than with shallow ones. Despite the
vast empirical evidence supporting this belief, theoretical justifications to
date are li... | On the Expressive Power of Deep Learning: A Tensor Analysis | 2,015 | http://arxiv.org/pdf/1509.05009v3 | Title Expressive Power Deep Learning Tensor Analysis Summary long conjectured hypothesis space suitable data compositional nature text image may efficiently represented deep hierarchical network shallow one Despite vast empirical evidence supporting belief theoretical justification date limited particular account local... | [-0.025506973266601562, 0.05522305518388748, -0.024910204112529755, 0.05504650995135307, -0.018744299188256264, 0.011751165613532066, 0.05135875195264816, 0.02122006006538868, -0.030108461156487465, -0.03114894963800907, -0.02738891914486885, -0.003963015973567963, 0.02069639042019844, 0.057300031185150146, 0.032450508... |
1,008 | 1,008 | ['David Nova', 'Pablo A. Estevez'] | 1509.07093v1 | In this work we present a review of the state of the art of Learning Vector
Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the
most relevant LVQ approaches to date. The main concepts associated with modern
LVQ approaches are defined. A comparison is made among eleven LVQ classifiers
using one r... | A review of learning vector quantization classifiers | 2,015 | http://arxiv.org/pdf/1509.07093v1 | Title review learning vector quantization classifier Summary work present review state art Learning Vector Quantization LVQ classifier taxonomy proposed integrates relevant LVQ approach date main concept associated modern LVQ approach defined comparison made among eleven LVQ classifier using one realworld two artificia... | [-0.05977300554513931, 0.004736436530947685, -0.029403463006019592, 0.014884956181049347, 0.022385310381650925, 0.007275397889316082, 0.005885349586606026, 0.059824470430612564, -0.000953124719671905, -0.02724575437605381, 0.04968015104532242, -0.009075534529983997, -0.017313819378614426, 0.027814656496047974, -0.01105... |
1,009 | 1,009 | ['Uri Shaham', 'Alexander Cloninger', 'Ronald R. Coifman'] | 1509.07385v3 | We discuss approximation of functions using deep neural nets. Given a
function $f$ on a $d$-dimensional manifold $\Gamma \subset \mathbb{R}^m$, we
construct a sparsely-connected depth-4 neural network and bound its error in
approximating $f$. The size of the network depends on dimension and curvature
of the manifold $\... | Provable approximation properties for deep neural networks | 2,015 | http://arxiv.org/pdf/1509.07385v3 | Title Provable approximation property deep neural network Summary discus approximation function using deep neural net Given function f ddimensional manifold Gamma subset mathbbRm construct sparselyconnected depth4 neural network bound error approximating f size network depends dimension curvature manifold Gamma complex... | [-0.05590597912669182, 0.01858479715883732, 0.028689654543995857, 0.0371275320649147, 0.0035450495779514313, -0.01502046175301075, 0.011005731299519539, -0.027167141437530518, 0.0021194193977862597, 0.0738958790898323, 0.006760123651474714, -0.008111672475934029, -0.01495177298784256, 0.04407349228858948, 0.03492267429... |
1,010 | 1,010 | ['David Balduzzi'] | 1509.08627v1 | Deep learning is currently the subject of intensive study. However,
fundamental concepts such as representations are not formally defined --
researchers "know them when they see them" -- and there is no common language
for describing and analyzing algorithms. This essay proposes an abstract
framework that identifies th... | Semantics, Representations and Grammars for Deep Learning | 2,015 | http://arxiv.org/pdf/1509.08627v1 | Title Semantics Representations Grammars Deep Learning Summary Deep learning currently subject intensive study However fundamental concept representation formally defined researcher know see common language describing analyzing algorithm essay proposes abstract framework identifies essential feature current practice ma... | [-0.0013996713096275926, 0.020266596227884293, -0.022982850670814514, 0.01866045780479908, -0.047065068036317825, -0.0113881416618824, 0.037124473601579666, -0.010451005771756172, -0.05943715199828148, -0.05291685461997986, 0.00984866637736559, -0.010585596784949303, 0.0031278925016522408, 0.09333193302154541, 0.024312... |
1,011 | 1,011 | ['Chen-Yu Lee', 'Patrick W. Gallagher', 'Zhuowen Tu'] | 1509.08985v2 | We seek to improve deep neural networks by generalizing the pooling
operations that play a central role in current architectures. We pursue a
careful exploration of approaches to allow pooling to learn and to adapt to
complex and variable patterns. The two primary directions lie in (1) learning a
pooling function via (... | Generalizing Pooling Functions in Convolutional Neural Networks: Mixed,
Gated, and Tree | 2,015 | http://arxiv.org/pdf/1509.08985v2 | Title Generalizing Pooling Functions Convolutional Neural Networks Mixed Gated Tree Summary seek improve deep neural network generalizing pooling operation play central role current architecture pursue careful exploration approach allow pooling learn adapt complex variable pattern two primary direction lie 1 learning p... | [-0.005272310227155685, 0.025088487192988396, 0.0010660000843927264, 0.03123207576572895, 0.017109090462327003, -0.010477676056325436, 0.013040158897638321, -0.016844142228364944, -0.04249121993780136, -0.002917418023571372, -0.02521940879523754, -0.04021741822361946, 0.02709580585360527, 0.03698455169796944, 0.0172950... |
1,012 | 1,012 | ['César Laurent', 'Gabriel Pereyra', 'Philémon Brakel', 'Ying Zhang', 'Yoshua Bengio'] | 1510.01378v1 | Recurrent Neural Networks (RNNs) are powerful models for sequential data that
have the potential to learn long-term dependencies. However, they are
computationally expensive to train and difficult to parallelize. Recent work
has shown that normalizing intermediate representations of neural networks can
significantly im... | Batch Normalized Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1510.01378v1 | Title Batch Normalized Recurrent Neural Networks Summary Recurrent Neural Networks RNNs powerful model sequential data potential learn longterm dependency However computationally expensive train difficult parallelize Recent work shown normalizing intermediate representation neural network significantly improve converge... | [-0.027521861717104912, 0.03971173241734505, 0.023022178560495377, 0.019709840416908264, -0.012142783030867577, -0.00977847259491682, 0.04466661438345909, 0.0017847318667918444, -0.029096905142068863, 0.001522875507362187, -0.02627219259738922, -0.011297101154923439, 0.03435905650258064, 0.027394583448767662, -0.019226... |
1,013 | 1,013 | ['Ben Krause'] | 1510.04953v1 | Recurrent Neural Networks (RNNs) have long been recognized for their
potential to model complex time series. However, it remains to be determined
what optimization techniques and recurrent architectures can be used to best
realize this potential. The experiments presented take a deep look into Hessian
free optimization... | Optimizing and Contrasting Recurrent Neural Network Architectures | 2,015 | http://arxiv.org/pdf/1510.04953v1 | Title Optimizing Contrasting Recurrent Neural Network Architectures Summary Recurrent Neural Networks RNNs long recognized potential model complex time series However remains determined optimization technique recurrent architecture used best realize potential experiment presented take deep look Hessian free optimizatio... | [0.018120326101779938, 0.06562254577875137, 0.00030987634090706706, 0.02336784452199936, -0.02435116656124592, -0.012681765481829643, 0.018707696348428726, 0.014799770899116993, -0.033419013023376465, -0.008177623152732849, -0.017388323321938515, -0.08129463344812393, 0.043445803225040436, 0.00026696265558712184, 0.020... |
1,014 | 1,014 | ['Alexandre de Brébisson', 'Pascal Vincent'] | 1511.05042v3 | In a multi-class classification problem, it is standard to model the output
of a neural network as a categorical distribution conditioned on the inputs.
The output must therefore be positive and sum to one, which is traditionally
enforced by a softmax. This probabilistic mapping allows to use the maximum
likelihood pri... | An Exploration of Softmax Alternatives Belonging to the Spherical Loss
Family | 2,015 | http://arxiv.org/pdf/1511.05042v3 | Title Exploration Softmax Alternatives Belonging Spherical Loss Family Summary multiclass classification problem standard model output neural network categorical distribution conditioned input output must therefore positive sum one traditionally enforced softmax probabilistic mapping allows use maximum likelihood princ... | [-0.011322312988340855, 0.0020932150073349476, 0.011889001354575157, 0.007676499430090189, 0.022251417860388756, -0.015849504619836807, 0.030473507940769196, -0.00724671920761466, -0.03328094258904457, -0.018747184425592422, -0.04571295529603958, -0.0598076656460762, 0.015659049153327942, 0.04293930158019066, 0.0299765... |
1,015 | 1,015 | ['Uri Shaham', 'Yutaro Yamada', 'Sahand Negahban'] | 1511.05432v3 | We propose a general framework for increasing local stability of Artificial
Neural Nets (ANNs) using Robust Optimization (RO). We achieve this through an
alternating minimization-maximization procedure, in which the loss of the
network is minimized over perturbed examples that are generated at each
parameter update. We... | Understanding Adversarial Training: Increasing Local Stability of Neural
Nets through Robust Optimization | 2,015 | http://arxiv.org/pdf/1511.05432v3 | Title Understanding Adversarial Training Increasing Local Stability Neural Nets Robust Optimization Summary propose general framework increasing local stability Artificial Neural Nets ANNs using Robust Optimization RO achieve alternating minimizationmaximization procedure loss network minimized perturbed example genera... | [-0.004029142204672098, 0.015982283279299736, -0.017641833052039146, 0.02705393359065056, 0.027294622734189034, -0.04971231520175934, 0.0039017926901578903, -0.017038116231560707, 0.0030511992517858744, 0.0006444669561460614, 0.007238368503749371, 0.003184942062944174, 0.008727705106139183, -0.004711437039077282, 0.062... |
1,016 | 1,016 | ['Martin Arjovsky', 'Amar Shah', 'Yoshua Bengio'] | 1511.06464v4 | Recurrent neural networks (RNNs) are notoriously difficult to train. When the
eigenvalues of the hidden to hidden weight matrix deviate from absolute value
1, optimization becomes difficult due to the well studied issue of vanishing
and exploding gradients, especially when trying to learn long-term
dependencies. To cir... | Unitary Evolution Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1511.06464v4 | Title Unitary Evolution Recurrent Neural Networks Summary Recurrent neural network RNNs notoriously difficult train eigenvalue hidden hidden weight matrix deviate absolute value 1 optimization becomes difficult due well studied issue vanishing exploding gradient especially trying learn longterm dependency circumvent pr... | [0.0019006810616701841, 0.0763901025056839, 0.00197855313308537, 0.02733837068080902, 0.0023853941820561886, 0.005786016117781401, -0.0056060669012367725, -0.0029748764354735613, -0.024224046617746353, -0.008765719830989838, -0.011217683553695679, -0.022924989461898804, -0.008992144837975502, 0.00693783862516284, 0.016... |
1,017 | 1,017 | ['Nicolas Papernot', 'Patrick McDaniel', 'Somesh Jha', 'Matt Fredrikson', 'Z. Berkay Celik', 'Ananthram Swami'] | 1511.07528v1 | Deep learning takes advantage of large datasets and computationally efficient
training algorithms to outperform other approaches at various machine learning
tasks. However, imperfections in the training phase of deep neural networks
make them vulnerable to adversarial samples: inputs crafted by adversaries with
the int... | The Limitations of Deep Learning in Adversarial Settings | 2,015 | http://arxiv.org/pdf/1511.07528v1 | Title Limitations Deep Learning Adversarial Settings Summary Deep learning take advantage large datasets computationally efficient training algorithm outperform approach various machine learning task However imperfection training phase deep neural network make vulnerable adversarial sample input crafted adversary inten... | [0.02380998805165291, 0.06371823698282242, -0.040228307247161865, 0.011923722922801971, -0.02521265298128128, -0.01379898376762867, 0.06262260675430298, -0.021116524934768677, -0.0199461430311203, -0.03555198386311531, 0.012148430570960045, 0.01339671015739441, 0.026439659297466278, 0.039241306483745575, 0.066861122846... |
1,018 | 1,018 | ['Ronen Eldan', 'Ohad Shamir'] | 1512.03965v4 | We show that there is a simple (approximately radial) function on $\reals^d$,
expressible by a small 3-layer feedforward neural networks, which cannot be
approximated by any 2-layer network, to more than a certain constant accuracy,
unless its width is exponential in the dimension. The result holds for
virtually all kn... | The Power of Depth for Feedforward Neural Networks | 2,015 | http://arxiv.org/pdf/1512.03965v4 | Title Power Depth Feedforward Neural Networks Summary show simple approximately radial function realsd expressible small 3layer feedforward neural network cannot approximated 2layer network certain constant accuracy unless width exponential dimension result hold virtually known activation function including rectified l... | [-0.025231948122382164, -0.015978693962097168, -0.019615404307842255, 0.030008630827069283, -0.008808150887489319, -0.03327452391386032, 0.0336979515850544, -0.039953604340553284, -0.01616176962852478, 0.0377151295542717, 0.04056018963456154, -0.00040590125718154013, 0.02106557972729206, 0.050211530178785324, 0.0497252... |
1,019 | 1,019 | ['Sajid Anwar', 'Kyuyeon Hwang', 'Wonyong Sung'] | 1512.08571v1 | Real time application of deep learning algorithms is often hindered by high
computational complexity and frequent memory accesses. Network pruning is a
promising technique to solve this problem. However, pruning usually results in
irregular network connections that not only demand extra representation efforts
but also ... | Structured Pruning of Deep Convolutional Neural Networks | 2,015 | http://arxiv.org/pdf/1512.08571v1 | Title Structured Pruning Deep Convolutional Neural Networks Summary Real time application deep learning algorithm often hindered high computational complexity frequent memory access Network pruning promising technique solve problem However pruning usually result irregular network connection demand extra representation ... | [-0.010910236276686192, 0.015242780558764935, -0.020245423540472984, 0.07711878418922424, 0.018374234437942505, -0.05077040195465088, 0.04685645550489426, 0.03043891116976738, -0.045074425637722015, 0.017209837213158607, 0.012775520794093609, -0.01960749737918377, -0.00017783624934963882, 0.038504406809806824, -0.01493... |
1,020 | 1,020 | ['Uri Shaham', 'Roy Lederman'] | 1512.08806v3 | We consider the statistical problem of learning common source of variability
in data which are synchronously captured by multiple sensors, and demonstrate
that Siamese neural networks can be naturally applied to this problem. This
approach is useful in particular in exploratory, data-driven applications,
where neither ... | Common Variable Learning and Invariant Representation Learning using
Siamese Neural Networks | 2,015 | http://arxiv.org/pdf/1512.08806v3 | Title Common Variable Learning Invariant Representation Learning using Siamese Neural Networks Summary consider statistical problem learning common source variability data synchronously captured multiple sensor demonstrate Siamese neural network naturally applied problem approach useful particular exploratory datadrive... | [-0.0544157475233078, 0.024771135300397873, -0.008793048560619354, 0.014006986282765865, -0.026938587427139282, 0.025043006986379623, 0.04505455493927002, -0.01592770591378212, -0.013735727407038212, -0.007581098936498165, -0.0060582952573895454, 0.04268019273877144, 0.030583487823605537, 0.032423462718725204, 0.027445... |
1,021 | 1,021 | ['Siamak Ravanbakhsh', 'Barnabas Poczos', 'Jeff Schneider', 'Dale Schuurmans', 'Russell Greiner'] | 1601.00034v4 | We propose a Laplace approximation that creates a stochastic unit from any
smooth monotonic activation function, using only Gaussian noise. This paper
investigates the application of this stochastic approximation in training a
family of Restricted Boltzmann Machines (RBM) that are closely linked to
Bregman divergences.... | Stochastic Neural Networks with Monotonic Activation Functions | 2,016 | http://arxiv.org/pdf/1601.00034v4 | Title Stochastic Neural Networks Monotonic Activation Functions Summary propose Laplace approximation creates stochastic unit smooth monotonic activation function using Gaussian noise paper investigates application stochastic approximation training family Restricted Boltzmann Machines RBM closely linked Bregman diverge... | [-0.022105617448687553, -0.012747292406857014, -0.013226406648755074, 0.007011051755398512, -0.010834104381501675, -0.018352391198277473, 0.0627669170498848, -0.026270363479852676, -0.03777092695236206, 0.04276793822646141, -0.0025533935986459255, -0.04123224690556526, 0.0041479626670479774, 0.07758092880249023, 0.0235... |
1,022 | 1,022 | ['Conrado S. Miranda', 'Fernando J. Von Zuben'] | 1602.01164v1 | This paper introduces the hypervolume maximization with a single solution as
an alternative to the mean loss minimization. The relationship between the two
problems is proved through bounds on the cost function when an optimal solution
to one of the problems is evaluated on the other, with a hyperparameter to
control t... | Single-Solution Hypervolume Maximization and its use for Improving
Generalization of Neural Networks | 2,016 | http://arxiv.org/pdf/1602.01164v1 | Title SingleSolution Hypervolume Maximization use Improving Generalization Neural Networks Summary paper introduces hypervolume maximization single solution alternative mean loss minimization relationship two problem proved bound cost function optimal solution one problem evaluated hyperparameter control similarity two... | [-0.0027464719023555517, 0.009001024067401886, -0.008193577639758587, 0.02726939134299755, 0.02189466543495655, -0.023885119706392288, 0.04536872357130051, 0.019456245005130768, -0.015219427645206451, 0.012663464993238449, -0.02107631228864193, 0.042969170957803726, 0.005190565250813961, -0.012795457616448402, 0.017654... |
1,023 | 1,023 | ['Mark Tygert'] | 1602.02823v1 | Poor (even random) starting points for learning/training/optimization are
common in machine learning. In many settings, the method of Robbins and Monro
(online stochastic gradient descent) is known to be optimal for good starting
points, but may not be optimal for poor starting points -- indeed, for poor
starting point... | Poor starting points in machine learning | 2,016 | http://arxiv.org/pdf/1602.02823v1 | Title Poor starting point machine learning Summary Poor even random starting point learningtrainingoptimization common machine learning many setting method Robbins Monro online stochastic gradient descent known optimal good starting point may optimal poor starting point indeed poor starting point Nesterov acceleration ... | [-0.03629722073674202, 0.018068384379148483, -0.008501116186380386, -0.019850803539156914, -0.005484607070684433, 0.01216861791908741, 0.022362254559993744, 0.033891402184963226, -0.00452084606513381, -0.012383509427309036, 0.08977865427732468, 0.0027387761510908604, 0.017566345632076263, 0.03579370304942131, -0.004166... |
1,024 | 1,024 | ['Matus Telgarsky'] | 1602.04485v2 | For any positive integer $k$, there exist neural networks with $\Theta(k^3)$
layers, $\Theta(1)$ nodes per layer, and $\Theta(1)$ distinct parameters which
can not be approximated by networks with $\mathcal{O}(k)$ layers unless they
are exponentially large --- they must possess $\Omega(2^k)$ nodes. This result
is prove... | Benefits of depth in neural networks | 2,016 | http://arxiv.org/pdf/1602.04485v2 | Title Benefits depth neural network Summary positive integer k exist neural network Thetak3 layer Theta1 node per layer Theta1 distinct parameter approximated network mathcalOk layer unless exponentially large must posse Omega2k node result proved class node termed semialgebraic gate includes common choice ReLU maximum... | [-0.05724705755710602, 0.05195591598749161, -0.010790308937430382, 0.07703837752342224, -0.032453905791044235, -0.05051332339644432, 0.027538588270545006, -0.005830776412039995, -0.012861654162406921, -0.017301520332694054, 0.07778756320476532, 0.04497380182147026, -0.008873707614839077, 0.069909006357193, 0.0183462016... |
1,025 | 1,025 | ['Prateek Jain', 'Chi Jin', 'Sham M. Kakade', 'Praneeth Netrapalli', 'Aaron Sidford'] | 1602.06929v2 | This work provides improved guarantees for streaming principle component
analysis (PCA). Given $A_1, \ldots, A_n\in \mathbb{R}^{d\times d}$ sampled
independently from distributions satisfying $\mathbb{E}[A_i] = \Sigma$ for
$\Sigma \succeq \mathbf{0}$, this work provides an $O(d)$-space linear-time
single-pass streaming... | Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample
Guarantees for Oja's Algorithm | 2,016 | http://arxiv.org/pdf/1602.06929v2 | Title Streaming PCA Matching Matrix Bernstein NearOptimal Finite Sample Guarantees Ojas Algorithm Summary work provides improved guarantee streaming principle component analysis PCA Given A1 ldots Anin mathbbRdtimes sampled independently distribution satisfying mathbbEAi Sigma Sigma succeq mathbf0 work provides Odspace... | [-0.05177688226103783, 0.0025803453754633665, -0.032424625009298325, 0.030767343938350677, -0.0019016548758372664, 0.0030492173973470926, -0.0014358807820826769, 0.0364130362868309, -0.017607303336262703, 0.002995114540681243, 0.040183454751968384, -0.0178676750510931, 0.05465877801179886, 0.09350184351205826, -0.00825... |
1,026 | 1,026 | ['Gaétan Marceau-Caron', 'Yann Ollivier'] | 1602.08007v1 | We provide the first experimental results on non-synthetic datasets for the
quasi-diagonal Riemannian gradient descents for neural networks introduced in
[Ollivier, 2015]. These include the MNIST, SVHN, and FACE datasets as well as a
previously unpublished electroencephalogram dataset. The quasi-diagonal
Riemannian alg... | Practical Riemannian Neural Networks | 2,016 | http://arxiv.org/pdf/1602.08007v1 | Title Practical Riemannian Neural Networks Summary provide first experimental result nonsynthetic datasets quasidiagonal Riemannian gradient descent neural network introduced Ollivier 2015 include MNIST SVHN FACE datasets well previously unpublished electroencephalogram dataset quasidiagonal Riemannian algorithm consis... | [-0.01422437746077776, 0.015026831068098545, 0.001314919674769044, 0.03444475308060646, 0.007506160996854305, 0.007985351607203484, 0.03505011275410652, -0.021717244759202003, 0.019317619502544403, 0.06532058119773865, -0.050407420843839645, 0.0018943356117233634, 0.042873673141002655, 0.0022290360648185015, 0.06274704... |
1,027 | 1,027 | ['Ryan Spring', 'Anshumali Shrivastava'] | 1602.08194v2 | Current deep learning architectures are growing larger in order to learn from
complex datasets. These architectures require giant matrix multiplication
operations to train millions of parameters. Conversely, there is another
growing trend to bring deep learning to low-power, embedded devices. The matrix
operations, ass... | Scalable and Sustainable Deep Learning via Randomized Hashing | 2,016 | http://arxiv.org/pdf/1602.08194v2 | Title Scalable Sustainable Deep Learning via Randomized Hashing Summary Current deep learning architecture growing larger order learn complex datasets architecture require giant matrix multiplication operation train million parameter Conversely another growing trend bring deep learning lowpower embedded device matrix o... | [0.0010114377364516258, 0.03945699706673622, -0.009542764164507389, 0.03983019292354584, 0.002935656812041998, -0.002689250512048602, 0.03509049117565155, -0.01152210496366024, -0.01446304377168417, -0.009551228024065495, 0.0005053200293332338, 0.015780463814735413, -0.016175471246242523, 0.05343971401453018, -0.002425... |
1,028 | 1,028 | ['Caglar Gulcehre', 'Marcin Moczulski', 'Misha Denil', 'Yoshua Bengio'] | 1603.00391v3 | Common nonlinear activation functions used in neural networks can cause
training difficulties due to the saturation behavior of the activation
function, which may hide dependencies that are not visible to vanilla-SGD
(using first order gradients only). Gating mechanisms that use softly
saturating activation functions t... | Noisy Activation Functions | 2,016 | http://arxiv.org/pdf/1603.00391v3 | Title Noisy Activation Functions Summary Common nonlinear activation function used neural network cause training difficulty due saturation behavior activation function may hide dependency visible vanillaSGD using first order gradient Gating mechanism use softly saturating activation function emulate discrete switching ... | [-0.02933085896074772, -0.005668508354574442, -0.014781647361814976, 0.023311136290431023, 0.015231486409902573, -0.04363773763179779, 0.04838943108916283, -0.017878500744700432, -0.0022393171675503254, -0.009032201021909714, 0.0012381107080727816, 0.05114392191171646, 0.018531769514083862, 0.06450876593589783, 0.03022... |
1,029 | 1,029 | ['Hanie Sedghi', 'Anima Anandkumar'] | 1603.00954v5 | We consider the problem of training input-output recurrent neural networks
(RNN) for sequence labeling tasks. We propose a novel spectral approach for
learning the network parameters. It is based on decomposition of the
cross-moment tensor between the output and a non-linear transformation of the
input, based on score ... | Training Input-Output Recurrent Neural Networks through Spectral Methods | 2,016 | http://arxiv.org/pdf/1603.00954v5 | Title Training InputOutput Recurrent Neural Networks Spectral Methods Summary consider problem training inputoutput recurrent neural network RNN sequence labeling task propose novel spectral approach learning network parameter based decomposition crossmoment tensor output nonlinear transformation input based score func... | [0.014532573521137238, 0.011313416063785553, 0.018280792981386185, 0.027680059894919395, -0.018323451280593872, -0.019043298438191414, -0.013410831801593304, -0.007060344330966473, -0.010939265601336956, -0.0278327576816082, -0.029019979760050774, -0.009128183126449585, 0.018436765298247337, 0.037789370864629745, -0.00... |
1,030 | 1,030 | ['Wei Li', 'Melvin Gauci', 'Roderich Gross'] | 1603.04904v2 | We propose Turing Learning, a novel system identification method for
inferring the behavior of natural or artificial systems. Turing Learning
simultaneously optimizes two populations of computer programs, one representing
models of the behavior of the system under investigation, and the other
representing classifiers. ... | Turing learning: a metric-free approach to inferring behavior and its
application to swarms | 2,016 | http://arxiv.org/pdf/1603.04904v2 | Title Turing learning metricfree approach inferring behavior application swarm Summary propose Turing Learning novel system identification method inferring behavior natural artificial system Turing Learning simultaneously optimizes two population computer program one representing model behavior system investigation rep... | [-0.02025068923830986, 0.012597393244504929, -0.024991095066070557, -0.002386539475992322, -0.00498027540743351, -0.002023395849391818, 0.001367067568935454, 0.037467971444129944, -0.005943096708506346, -0.02246209979057312, -0.012003985233604908, 0.05816323682665825, -0.035783879458904266, 0.005197503603994846, 0.0232... |
1,031 | 1,031 | ['Sashank J. Reddi', 'Ahmed Hefny', 'Suvrit Sra', 'Barnabas Poczos', 'Alex Smola'] | 1603.06160v2 | We study nonconvex finite-sum problems and analyze stochastic variance
reduced gradient (SVRG) methods for them. SVRG and related methods have
recently surged into prominence for convex optimization given their edge over
stochastic gradient descent (SGD); but their theoretical analysis almost
exclusively assumes convex... | Stochastic Variance Reduction for Nonconvex Optimization | 2,016 | http://arxiv.org/pdf/1603.06160v2 | Title Stochastic Variance Reduction Nonconvex Optimization Summary study nonconvex finitesum problem analyze stochastic variance reduced gradient SVRG method SVRG related method recently surged prominence convex optimization given edge stochastic gradient descent SGD theoretical analysis almost exclusively assumes conv... | [-0.017825353890657425, -0.004558364395052195, 0.0011095072841271758, 0.02004215493798256, 0.007348756771534681, -0.03906097635626793, -0.02140718512237072, -0.0020931249018758535, -0.05875280126929283, 0.040182650089263916, -0.002938437508419156, -0.009760996326804161, 0.019895298406481743, 0.04601144790649414, 0.0272... |
1,032 | 1,032 | ['R. Devon Hjelm', 'Sergey M. Plis', 'Vince C. Calhoun'] | 1603.06624v1 | Independent component analysis (ICA), as an approach to the blind
source-separation (BSS) problem, has become the de-facto standard in many
medical imaging settings. Despite successes and a large ongoing research
effort, the limitation of ICA to square linear transformations have not been
overcome, so that general INFO... | Variational Autoencoders for Feature Detection of Magnetic Resonance
Imaging Data | 2,016 | http://arxiv.org/pdf/1603.06624v1 | Title Variational Autoencoders Feature Detection Magnetic Resonance Imaging Data Summary Independent component analysis ICA approach blind sourceseparation BSS problem become defacto standard many medical imaging setting Despite success large ongoing research effort limitation ICA square linear transformation overcome ... | [-0.029280539602041245, 0.06551916152238846, -0.03624175861477852, 0.03891884163022041, 0.01904877834022045, 0.03921347111463547, 0.034090276807546616, 0.02394743822515011, -0.0011691383551806211, 0.05344498157501221, 0.019847573712468147, -0.019907116889953613, 0.007700404617935419, 0.07628156989812851, 0.051622044295... |
1,033 | 1,033 | ['Vincent Dumoulin', 'Francesco Visin'] | 1603.07285v2 | We introduce a guide to help deep learning practitioners understand and
manipulate convolutional neural network architectures. The guide clarifies the
relationship between various properties (input shape, kernel shape, zero
padding, strides and output shape) of convolutional, pooling and transposed
convolutional layers... | A guide to convolution arithmetic for deep learning | 2,016 | http://arxiv.org/pdf/1603.07285v2 | Title guide convolution arithmetic deep learning Summary introduce guide help deep learning practitioner understand manipulate convolutional neural network architecture guide clarifies relationship various property input shape kernel shape zero padding stride output shape convolutional pooling transposed convolutional ... | [-0.027366986498236656, -0.025669442489743233, -0.02870669960975647, 0.07416903227567673, -0.020394761115312576, -0.02669597417116165, 0.06578706949949265, -0.028624963015317917, -0.06529634445905685, 0.006211624015122652, -0.0026299890596419573, -0.032536134123802185, 0.030836543068289757, 0.069731704890728, 0.0371372... |
1,034 | 1,034 | ['Tayfun Gokmen', 'Yurii Vlasov'] | 1603.07341v1 | In recent years, deep neural networks (DNN) have demonstrated significant
business impact in large scale analysis and classification tasks such as speech
recognition, visual object detection, pattern extraction, etc. Training of
large DNNs, however, is universally considered as time consuming and
computationally intens... | Acceleration of Deep Neural Network Training with Resistive Cross-Point
Devices | 2,016 | http://arxiv.org/pdf/1603.07341v1 | Title Acceleration Deep Neural Network Training Resistive CrossPoint Devices Summary recent year deep neural network DNN demonstrated significant business impact large scale analysis classification task speech recognition visual object detection pattern extraction etc Training large DNNs however universally considered ... | [-0.012012002989649773, 0.010204619728028774, -0.048822712153196335, 0.07510970532894135, -0.018761029466986656, -0.014101080596446991, 0.08337471634149551, -0.021849609911441803, 0.02988363802433014, -0.004502861760556698, -0.009541725739836693, 0.008387312293052673, 0.005764232482761145, 0.02505887672305107, 0.009183... |
1,035 | 1,035 | ['Benjamin Migliori', 'Riley Zeller-Townson', 'Daniel Grady', 'Daniel Gebhardt'] | 1605.05239v1 | Automatic modulation classification (AMC) is an important task for modern
communication systems; however, it is a challenging problem when signal
features and precise models for generating each modulation may be unknown. We
present a new biologically-inspired AMC method without the need for models or
manually specified... | Biologically Inspired Radio Signal Feature Extraction with Sparse
Denoising Autoencoders | 2,016 | http://arxiv.org/pdf/1605.05239v1 | Title Biologically Inspired Radio Signal Feature Extraction Sparse Denoising Autoencoders Summary Automatic modulation classification AMC important task modern communication system however challenging problem signal feature precise model generating modulation may unknown present new biologicallyinspired AMC method with... | [0.004506845027208328, 0.04426071047782898, -0.012995606288313866, -0.035241685807704926, 0.0505380742251873, 0.03157045319676399, 0.04344221577048302, 0.00989223551005125, -0.09838833659887314, -0.03230344504117966, -0.00015275782789103687, 0.049426715821027756, 0.003318492090329528, 0.04024110361933708, 0.01992030814... |
1,036 | 1,036 | ['Simone Scardapane', 'Michele Scarpiniti', 'Danilo Comminiello', 'Aurelio Uncini'] | 1605.05509v2 | Neural networks require a careful design in order to perform properly on a
given task. In particular, selecting a good activation function (possibly in a
data-dependent fashion) is a crucial step, which remains an open problem in the
research community. Despite a large amount of investigations, most current
implementat... | Learning activation functions from data using cubic spline interpolation | 2,016 | http://arxiv.org/pdf/1605.05509v2 | Title Learning activation function data using cubic spline interpolation Summary Neural network require careful design order perform properly given task particular selecting good activation function possibly datadependent fashion crucial step remains open problem research community Despite large amount investigation cu... | [-0.010667058639228344, 0.031108738854527473, 0.0010392069816589355, 0.03380172327160835, 0.024867622181773186, -0.029170237481594086, 0.017008671537041664, -0.013125554658472538, -0.007886748760938644, 0.038197822868824005, -0.03163711726665497, 0.014261537231504917, 0.0019798658322542906, 0.07050881534814835, -0.0134... |
1,037 | 1,037 | ['Devansh Arpit', 'Yingbo Zhou', 'Hung Q. Ngo', 'Nils Napp', 'Venu Govindaraju'] | 1605.07145v2 | Auto-Encoders are unsupervised models that aim to learn patterns from
observed data by minimizing a reconstruction cost. The useful representations
learned are often found to be sparse and distributed. On the other hand,
compressed sensing and sparse coding assume a data generating process, where
the observed data is g... | On Optimality Conditions for Auto-Encoder Signal Recovery | 2,016 | http://arxiv.org/pdf/1605.07145v2 | Title Optimality Conditions AutoEncoder Signal Recovery Summary AutoEncoders unsupervised model aim learn pattern observed data minimizing reconstruction cost useful representation learned often found sparse distributed hand compressed sensing sparse coding assume data generating process observed data generated true la... | [-0.03757653012871742, 0.07418263703584671, -0.008229772560298443, 0.056047700345516205, -0.007916820235550404, 0.009706190787255764, 0.031618766486644745, 0.07330065220594406, -0.06116146221756935, -0.021794728934764862, 0.014838628470897675, 0.020254770293831825, 0.01566500775516033, 0.07702795416116714, 0.0302096884... |
1,038 | 1,038 | ['Zhengping Che', 'Sanjay Purushotham', 'Kyunghyun Cho', 'David Sontag', 'Yan Liu'] | 1606.01865v2 | Multivariate time series data in practical applications, such as health care,
geoscience, and biology, are characterized by a variety of missing values. In
time series prediction and other related tasks, it has been noted that missing
values and their missing patterns are often correlated with the target labels,
a.k.a.... | Recurrent Neural Networks for Multivariate Time Series with Missing
Values | 2,016 | http://arxiv.org/pdf/1606.01865v2 | Title Recurrent Neural Networks Multivariate Time Series Missing Values Summary Multivariate time series data practical application health care geoscience biology characterized variety missing value time series prediction related task noted missing value missing pattern often correlated target label aka informative mis... | [-0.012370516546070576, 0.05453154817223549, -0.006999610923230648, -0.03447144106030464, 0.003401670604944229, 0.006981666199862957, 0.03035300225019455, -0.00612240843474865, -0.006608666852116585, 0.05855646729469299, 0.07647636532783508, -0.04249528422951698, 0.03462782874703407, 0.07694125175476074, -0.00040219174... |
1,039 | 1,039 | ['Zachary C. Lipton', 'David C. Kale', 'Randall Wetzel'] | 1606.04130v5 | We demonstrate a simple strategy to cope with missing data in sequential
inputs, addressing the task of multilabel classification of diagnoses given
clinical time series. Collected from the pediatric intensive care unit (PICU)
at Children's Hospital Los Angeles, our data consists of multivariate time
series of observat... | Modeling Missing Data in Clinical Time Series with RNNs | 2,016 | http://arxiv.org/pdf/1606.04130v5 | Title Modeling Missing Data Clinical Time Series RNNs Summary demonstrate simple strategy cope missing data sequential input addressing task multilabel classification diagnosis given clinical time series Collected pediatric intensive care unit PICU Childrens Hospital Los Angeles data consists multivariate time series o... | [0.011131436564028263, 0.05854105204343796, -0.011960997246205807, -0.03848806023597717, -0.01573006622493267, 0.026324205100536346, 0.02152855694293976, -0.0020901032257825136, -0.014020009897649288, 0.018067780882120132, 0.08838817477226257, -0.06427156925201416, 0.024277424439787865, 0.04747456684708595, -0.00392925... |
1,040 | 1,040 | ['Stefan Hosein', 'Patrick Hosein'] | 1606.05018v1 | Recently there has been significant research on power generation,
distribution and transmission efficiency especially in the case of renewable
resources. The main objective is reduction of energy losses and this requires
improvements on data acquisition and analysis. In this paper we address these
concerns by using con... | Improving Power Generation Efficiency using Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1606.05018v1 | Title Improving Power Generation Efficiency using Deep Neural Networks Summary Recently significant research power generation distribution transmission efficiency especially case renewable resource main objective reduction energy loss requires improvement data acquisition analysis paper address concern using consumer e... | [-0.03407803550362587, 0.05291131138801575, -0.015052832663059235, 0.024366991594433784, 0.05610652267932892, -0.001329451915808022, 0.03773383051156998, -0.03784646838903427, -0.019566189497709274, 0.03193863853812218, -0.003670220961794257, 0.035747263580560684, 0.013334088027477264, 0.05060300603508949, 0.0537500642... |
1,041 | 1,041 | ['Trang Pham', 'Truyen Tran', 'Dinh Phung', 'Svetha Venkatesh'] | 1608.03639v1 | A major contributing factor to the recent advances in deep neural networks is
structural units that let sensory information and gradients to propagate
easily. Gating is one such structure that acts as a flow control. Gates are
employed in many recent state-of-the-art recurrent models such as LSTM and GRU,
and feedforwa... | Faster Training of Very Deep Networks Via p-Norm Gates | 2,016 | http://arxiv.org/pdf/1608.03639v1 | Title Faster Training Deep Networks Via pNorm Gates Summary major contributing factor recent advance deep neural network structural unit let sensory information gradient propagate easily Gating one structure act flow control Gates employed many recent stateoftheart recurrent model LSTM GRU feedforward model Residual Ne... | [-0.038673896342515945, 0.07525280117988586, -0.027758315205574036, 0.06350535899400711, -0.038745224475860596, -0.005100186448544264, 0.02834760770201683, 0.016896206885576248, -0.017322776839137077, 0.024394335225224495, -0.02355293184518814, -0.055701106786727905, 0.01647026464343071, 0.10443779826164246, 0.01980346... |
1,042 | 1,042 | ['Zachary C. Lipton', 'Xiujun Li', 'Jianfeng Gao', 'Lihong Li', 'Faisal Ahmed', 'Li Deng'] | 1608.05081v4 | We present a new algorithm that significantly improves the efficiency of
exploration for deep Q-learning agents in dialogue systems. Our agents explore
via Thompson sampling, drawing Monte Carlo samples from a Bayes-by-Backprop
neural network. Our algorithm learns much faster than common exploration
strategies such as ... | BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for
Task-Oriented Dialogue Systems | 2,016 | http://arxiv.org/pdf/1608.05081v4 | Title BBQNetworks Efficient Exploration Deep Reinforcement Learning TaskOriented Dialogue Systems Summary present new algorithm significantly improves efficiency exploration deep Qlearning agent dialogue system agent explore via Thompson sampling drawing Monte Carlo sample BayesbyBackprop neural network algorithm learn... | [0.01756395399570465, -0.002453365596011281, -0.006325583439320326, 0.007751567754894495, 0.0036683231592178345, 0.004726892337203026, -0.002970527857542038, -0.003614576067775488, -0.025236308574676514, -0.012071250006556511, -0.06712344288825989, 0.004469552543014288, -0.030904719606041908, 0.08419699221849442, -0.01... |
1,043 | 1,043 | ['Henry W. Lin', 'Max Tegmark', 'David Rolnick'] | 1608.08225v4 | We show how the success of deep learning could depend not only on mathematics
but also on physics: although well-known mathematical theorems guarantee that
neural networks can approximate arbitrary functions well, the class of
functions of practical interest can frequently be approximated through "cheap
learning" with ... | Why does deep and cheap learning work so well? | 2,016 | http://arxiv.org/pdf/1608.08225v4 | Title deep cheap learning work well Summary show success deep learning could depend mathematics also physic although wellknown mathematical theorem guarantee neural network approximate arbitrary function well class function practical interest frequently approximated cheap learning exponentially fewer parameter generic ... | [-0.05316087603569031, 0.05271539092063904, -0.0023642354644834995, 0.043239373713731766, -0.0020512137562036514, -0.029747430235147476, 0.07406356185674667, -0.0006282221875153482, -0.03803754597902298, 0.008790702559053898, -0.02639138698577881, 0.015553015284240246, 0.013995025306940079, 0.04369424656033516, 0.03127... |
1,044 | 1,044 | ['Ohad Shamir'] | 1609.01037v2 | Although neural networks are routinely and successfully trained in practice
using simple gradient-based methods, most existing theoretical results are
negative, showing that learning such networks is difficult, in a worst-case
sense over all data distributions. In this paper, we take a more nuanced view,
and consider w... | Distribution-Specific Hardness of Learning Neural Networks | 2,016 | http://arxiv.org/pdf/1609.01037v2 | Title DistributionSpecific Hardness Learning Neural Networks Summary Although neural network routinely successfully trained practice using simple gradientbased method existing theoretical result negative showing learning network difficult worstcase sense data distribution paper take nuanced view consider whether specif... | [-0.01701528951525688, 0.05463641881942749, -0.03403698652982712, 0.045400600880384445, -0.03130735456943512, -0.025421524420380592, 0.021013692021369934, -0.018950212746858597, -0.030385931953787804, 0.0040805102325975895, 0.03568645194172859, 0.013218891806900501, 0.03322708234190941, 0.013763370923697948, 0.02486516... |
1,045 | 1,045 | ['Tom White'] | 1609.04468v3 | We introduce several techniques for sampling and visualizing the latent
spaces of generative models. Replacing linear interpolation with spherical
linear interpolation prevents diverging from a model's prior distribution and
produces sharper samples. J-Diagrams and MINE grids are introduced as
visualizations of manifol... | Sampling Generative Networks | 2,016 | http://arxiv.org/pdf/1609.04468v3 | Title Sampling Generative Networks Summary introduce several technique sampling visualizing latent space generative model Replacing linear interpolation spherical linear interpolation prevents diverging model prior distribution produce sharper sample JDiagrams MINE grid introduced visualization manifold created analogy... | [-0.013721062801778316, 0.04511566832661629, -0.04850391298532486, 0.012499610893428326, -0.023033253848552704, -0.008584017865359783, 0.054385073482990265, -0.02627229504287243, -0.04574090987443924, 0.021454133093357086, -0.0011326386593282223, 0.037688031792640686, 0.00016109456191770732, 0.043710559606552124, 0.051... |
1,046 | 1,046 | ['Alexandre de Brébisson', 'Pascal Vincent'] | 1609.05866v1 | The softmax content-based attention mechanism has proven to be very
beneficial in many applications of recurrent neural networks. Nevertheless it
suffers from two major computational limitations. First, its computations for
an attention lookup scale linearly in the size of the attended sequence.
Second, it does not enc... | A Cheap Linear Attention Mechanism with Fast Lookups and Fixed-Size
Representations | 2,016 | http://arxiv.org/pdf/1609.05866v1 | Title Cheap Linear Attention Mechanism Fast Lookups FixedSize Representations Summary softmax contentbased attention mechanism proven beneficial many application recurrent neural network Nevertheless suffers two major computational limitation First computation attention lookup scale linearly size attended sequence Seco... | [0.019965214654803276, 0.017598077654838562, -0.005461378023028374, 0.030027788132429123, -0.022849038243293762, -0.011782084591686726, 0.025866692885756493, -0.018963102251291275, 0.0034295115619897842, -0.06577866524457932, 0.028971312567591667, -0.013260751962661743, -0.02671240083873272, 0.07389020919799805, 0.0232... |
1,047 | 1,047 | ['Sakyasingha Dasgupta', 'Takayuki Yoshizumi', 'Takayuki Osogami'] | 1610.01989v1 | We introduce Delay Pruning, a simple yet powerful technique to regularize
dynamic Boltzmann machines (DyBM). The recently introduced DyBM provides a
particularly structured Boltzmann machine, as a generative model of a
multi-dimensional time-series. This Boltzmann machine can have infinitely many
layers of units but al... | Regularized Dynamic Boltzmann Machine with Delay Pruning for
Unsupervised Learning of Temporal Sequences | 2,016 | http://arxiv.org/pdf/1610.01989v1 | Title Regularized Dynamic Boltzmann Machine Delay Pruning Unsupervised Learning Temporal Sequences Summary introduce Delay Pruning simple yet powerful technique regularize dynamic Boltzmann machine DyBM recently introduced DyBM provides particularly structured Boltzmann machine generative model multidimensional timeser... | [-0.034820955246686935, 0.015977049246430397, -0.04693754389882088, 0.009623349644243717, -0.010128208436071873, -0.011577432975172997, 0.04971451312303543, -0.022431986406445503, -0.04560653492808342, 0.04773739352822304, 0.023729410022497177, 0.004891319666057825, -0.007737250532954931, 0.06745558977127075, 0.0119249... |
1,048 | 1,048 | ['Or Sharir', 'Ronen Tamari', 'Nadav Cohen', 'Amnon Shashua'] | 1610.04167v4 | Casting neural networks in generative frameworks is a highly sought-after
endeavor these days. Existing methods, such as Generative Adversarial Networks,
capture some of the generative capabilities, but not all. To truly leverage the
power of generative models, tractable marginalization is needed, a feature
outside the... | Tractable Generative Convolutional Arithmetic Circuits | 2,016 | http://arxiv.org/pdf/1610.04167v4 | Title Tractable Generative Convolutional Arithmetic Circuits Summary Casting neural network generative framework highly soughtafter endeavor day Existing method Generative Adversarial Networks capture generative capability truly leverage power generative model tractable marginalization needed feature outside realm curr... | [-0.026159675791859627, 0.1338084191083908, 0.0044853538274765015, 0.04394255578517914, 0.0053377398289740086, -0.01644374616444111, 0.0651826485991478, 0.004866212606430054, -0.07171468436717987, -0.007775270380079746, -0.02130560204386711, -0.014915110543370247, 0.03056446462869644, 0.020416980609297752, 0.0446075983... |
1,049 | 1,049 | ['Jimmy Ba', 'Geoffrey Hinton', 'Volodymyr Mnih', 'Joel Z. Leibo', 'Catalin Ionescu'] | 1610.06258v3 | Until recently, research on artificial neural networks was largely restricted
to systems with only two types of variable: Neural activities that represent
the current or recent input and weights that learn to capture regularities
among inputs, outputs and payoffs. There is no good reason for this
restriction. Synapses ... | Using Fast Weights to Attend to the Recent Past | 2,016 | http://arxiv.org/pdf/1610.06258v3 | Title Using Fast Weights Attend Recent Past Summary recently research artificial neural network largely restricted system two type variable Neural activity represent current recent input weight learn capture regularity among input output payoff good reason restriction Synapses dynamic many different timescales suggests... | [0.0042250556871294975, 0.06523290276527405, -0.018991587683558464, -0.012209667824208736, -0.025263655930757523, -0.02415217086672783, 0.0346950925886631, 0.022041847929358482, -0.008921664208173752, -0.008219674229621887, 0.014070840552449226, -0.05241796746850014, -0.004168669693171978, 0.07041047513484955, 0.032904... |
1,050 | 1,050 | ['Yevgeniy Bodyanskiy', 'Olena Vynokurova', 'Volodymyr Savvo', 'Tatiana Tverdokhlib', 'Pavlo Mulesa'] | 1610.07857v1 | The hybrid clustering-classification neural network is proposed. This network
allows increasing a quality of information processing under the condition of
overlapping classes due to the rational choice of a learning rate parameter and
introducing a special procedure of fuzzy reasoning in the clustering process,
which o... | Hybrid clustering-classification neural network in the medical
diagnostics of reactive arthritis | 2,016 | http://arxiv.org/pdf/1610.07857v1 | Title Hybrid clusteringclassification neural network medical diagnostics reactive arthritis Summary hybrid clusteringclassification neural network proposed network allows increasing quality information processing condition overlapping class due rational choice learning rate parameter introducing special procedure fuzzy... | [-0.04642784222960472, -0.02085297740995884, -0.032319098711013794, 0.006815905682742596, 0.020100697875022888, 0.018149347975850105, 0.05504541099071503, 0.0762290358543396, 0.08380621671676636, 0.014367308467626572, 0.03662105277180672, 0.01445157639682293, 0.0012496589915826917, 0.01340700313448906, 0.01372126117348... |
1,051 | 1,051 | ['Scott Wisdom', 'Thomas Powers', 'John R. Hershey', 'Jonathan Le Roux', 'Les Atlas'] | 1611.00035v1 | Recurrent neural networks are powerful models for processing sequential data,
but they are generally plagued by vanishing and exploding gradient problems.
Unitary recurrent neural networks (uRNNs), which use unitary recurrence
matrices, have recently been proposed as a means to avoid these issues.
However, in previous ... | Full-Capacity Unitary Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1611.00035v1 | Title FullCapacity Unitary Recurrent Neural Networks Summary Recurrent neural network powerful model processing sequential data generally plagued vanishing exploding gradient problem Unitary recurrent neural network uRNNs use unitary recurrence matrix recently proposed mean avoid issue However previous experiment recur... | [-0.030734920874238014, 0.052602607756853104, 0.0006041592569090426, 0.03892534598708153, 0.026264594867825508, 0.00020796956960111856, 0.04626817628741264, -0.051186393946409225, -0.006274225190281868, 0.007698203902691603, -0.000739139795769006, -0.05875573307275772, -0.016060475260019302, 0.02370956540107727, 0.0273... |
1,052 | 1,052 | ['Sihan Li', 'Jiantao Jiao', 'Yanjun Han', 'Tsachy Weissman'] | 1611.01186v2 | The Residual Network (ResNet), proposed in He et al. (2015), utilized
shortcut connections to significantly reduce the difficulty of training, which
resulted in great performance boosts in terms of both training and
generalization error.
It was empirically observed in He et al. (2015) that stacking more layers of
res... | Demystifying ResNet | 2,016 | http://arxiv.org/pdf/1611.01186v2 | Title Demystifying ResNet Summary Residual Network ResNet proposed et al 2015 utilized shortcut connection significantly reduce difficulty training resulted great performance boost term training generalization error empirically observed et al 2015 stacking layer residual block shortcut 2 result smaller training error t... | [-0.028482018038630486, 0.014436997473239899, -0.010756446979939938, 0.045224107801914215, 0.03620850667357445, -0.004042657557874918, -0.00974203273653984, -0.022486062720417976, -0.07816368341445923, 0.0018403935246169567, -8.067340240813792e-05, 0.03390999510884285, 0.01682714931666851, -0.0018463052110746503, 0.009... |
1,053 | 1,053 | ['Zachary C. Lipton', 'Kamyar Azizzadenesheli', 'Abhishek Kumar', 'Lihong Li', 'Jianfeng Gao', 'Li Deng'] | 1611.01211v8 | Many practical environments contain catastrophic states that an optimal agent
would visit infrequently or never. Even on toy problems, Deep Reinforcement
Learning (DRL) agents tend to periodically revisit these states upon forgetting
their existence under a new policy. We introduce intrinsic fear (IF), a learned
reward... | Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear | 2,016 | http://arxiv.org/pdf/1611.01211v8 | Title Combating Reinforcement Learnings Sisyphean Curse Intrinsic Fear Summary Many practical environment contain catastrophic state optimal agent would visit infrequently never Even toy problem Deep Reinforcement Learning DRL agent tend periodically revisit state upon forgetting existence new policy introduce intrinsi... | [-0.019792810082435608, 0.022421997040510178, -0.023461276665329933, -0.05199126899242401, 0.02416764572262764, 0.03155511990189552, -0.052378132939338684, -0.0004106828710064292, -0.05425058305263519, 0.04195147752761841, 0.00558619387447834, 0.04211270436644554, -0.05505409091711044, 0.03129200637340546, 0.0038214386... |
1,054 | 1,054 | ['Juan Maroñas Molano', 'Alberto Albiol Colomer', 'Roberto Paredes Palacios'] | 1611.02320v1 | The use of unsupervised data in addition to supervised data in training
discriminative neural networks has improved the performance of this clas-
sification scheme. However, the best results were achieved with a training
process that is divided in two parts: first an unsupervised pre-training step
is done for initializ... | Adversarial Ladder Networks | 2,016 | http://arxiv.org/pdf/1611.02320v1 | Title Adversarial Ladder Networks Summary use unsupervised data addition supervised data training discriminative neural network improved performance clas sification scheme However best result achieved training process divided two part first unsupervised pretraining step done initializing weight network weight refined u... | [-0.006075280252844095, 0.06084270402789116, -0.015357154421508312, 0.025425808504223824, 0.028498176485300064, -0.026468999683856964, 0.03860553726553917, -0.031894292682409286, 0.05148080363869667, 0.03106657974421978, -0.027396125718951225, 0.021526359021663666, 0.009733046405017376, 0.017647814005613327, 0.04040824... |
1,055 | 1,055 | ['David Balduzzi', 'Brian McWilliams', 'Tony Butler-Yeoman'] | 1611.02345v2 | Modern convolutional networks, incorporating rectifiers and max-pooling, are
neither smooth nor convex. Standard guarantees therefore do not apply.
Nevertheless, methods from convex optimization such as gradient descent and
Adam are widely used as building blocks for deep learning algorithms. This
paper provides the fi... | Neural Taylor Approximations: Convergence and Exploration in Rectifier
Networks | 2,016 | http://arxiv.org/pdf/1611.02345v2 | Title Neural Taylor Approximations Convergence Exploration Rectifier Networks Summary Modern convolutional network incorporating rectifier maxpooling neither smooth convex Standard guarantee therefore apply Nevertheless method convex optimization gradient descent Adam widely used building block deep learning algorithm ... | [-0.03941693156957626, 0.0035694059915840626, -0.010908045805990696, 0.044448450207710266, 0.006061987020075321, -0.04058769345283508, -0.00604519248008728, 0.01572156697511673, -0.035835932940244675, 0.012853969819843769, -0.0072275856509804726, -0.05246378853917122, 0.04613615572452545, 0.05757799744606018, 0.0047241... |
1,056 | 1,056 | ['Nat Dilokthanakul', 'Pedro A. M. Mediano', 'Marta Garnelo', 'Matthew C. H. Lee', 'Hugh Salimbeni', 'Kai Arulkumaran', 'Murray Shanahan'] | 1611.02648v2 | We study a variant of the variational autoencoder model (VAE) with a Gaussian
mixture as a prior distribution, with the goal of performing unsupervised
clustering through deep generative models. We observe that the known problem of
over-regularisation that has been shown to arise in regular VAEs also manifests
itself i... | Deep Unsupervised Clustering with Gaussian Mixture Variational
Autoencoders | 2,016 | http://arxiv.org/pdf/1611.02648v2 | Title Deep Unsupervised Clustering Gaussian Mixture Variational Autoencoders Summary study variant variational autoencoder model VAE Gaussian mixture prior distribution goal performing unsupervised clustering deep generative model observe known problem overregularisation shown arise regular VAEs also manifest model lea... | [-0.034761372953653336, 0.04398951306939125, -0.04592457786202431, 0.006551637779921293, 0.0191801805049181, 0.0023211322259157896, 0.02561366930603981, -0.01467252429574728, -0.04048890992999077, 0.046030595898628235, 0.01781448721885681, 0.015582984313368797, 0.007907512597739697, 0.0870896503329277, 0.03281857818365... |
1,057 | 1,057 | ['Moritz Hardt', 'Tengyu Ma'] | 1611.04231v2 | An emerging design principle in deep learning is that each layer of a deep
artificial neural network should be able to easily express the identity
transformation. This idea not only motivated various normalization techniques,
such as \emph{batch normalization}, but was also key to the immense success of
\emph{residual ... | Identity Matters in Deep Learning | 2,016 | http://arxiv.org/pdf/1611.04231v2 | Title Identity Matters Deep Learning Summary emerging design principle deep learning layer deep artificial neural network able easily express identity transformation idea motivated various normalization technique emphbatch normalization also key immense success emphresidual network work put principle emphidentity param... | [0.005022880155593157, 0.06987550854682922, -0.00843107420951128, 0.03470754995942116, 0.019836002960801125, -0.018270617350935936, 0.08320346474647522, -0.014904382638633251, -0.03487559035420418, 0.0009235618636012077, -0.009102459996938705, 0.005694924388080835, 0.014065858907997608, 0.01970188319683075, 0.029320327... |
1,058 | 1,058 | ['Siamak Ravanbakhsh', 'Jeff Schneider', 'Barnabas Poczos'] | 1611.04500v3 | We introduce a simple permutation equivariant layer for deep learning with
set structure.This type of layer, obtained by parameter-sharing, has a simple
implementation and linear-time complexity in the size of each set. We use deep
permutation-invariant networks to perform point-could classification and
MNIST-digit sum... | Deep Learning with Sets and Point Clouds | 2,016 | http://arxiv.org/pdf/1611.04500v3 | Title Deep Learning Sets Point Clouds Summary introduce simple permutation equivariant layer deep learning set structureThis type layer obtained parametersharing simple implementation lineartime complexity size set use deep permutationinvariant network perform pointcould classification MNISTdigit summation case output ... | [-0.08200529962778091, -0.003272661939263344, -0.033327195793390274, 0.02612738125026226, -0.018871000036597252, 0.013011643663048744, 0.055305685847997665, -0.04713607206940651, -0.008249286562204361, 0.034251317381858826, 0.012211918830871582, 0.01897413656115532, 0.0017339433543384075, 0.06297551840543747, 0.0358491... |
1,059 | 1,059 | ['Yotaro Kubo', 'George Tucker', 'Simon Wiesler'] | 1611.06148v2 | We introduce dropout compaction, a novel method for training feed-forward
neural networks which realizes the performance gains of training a large model
with dropout regularization, yet extracts a compact neural network for run-time
efficiency. In the proposed method, we introduce a sparsity-inducing prior on
the per u... | Compacting Neural Network Classifiers via Dropout Training | 2,016 | http://arxiv.org/pdf/1611.06148v2 | Title Compacting Neural Network Classifiers via Dropout Training Summary introduce dropout compaction novel method training feedforward neural network realizes performance gain training large model dropout regularization yet extract compact neural network runtime efficiency proposed method introduce sparsityinducing pr... | [-0.031969599425792694, 0.05555571988224983, -0.005156401544809341, 0.007052110508084297, -0.00039117818232625723, 0.0005590925575233996, 0.07456076890230179, 0.0061189280822873116, -0.03168214112520218, 0.03842666745185852, -0.03931460529565811, 0.042380694299936295, 0.06471101194620132, 0.060442499816417694, -0.00516... |
1,060 | 1,060 | ['Anders Søgaard'] | 1611.06245v1 | We present a confidence-based single-layer feed-forward learning algorithm
SPIRAL (Spike Regularized Adaptive Learning) relying on an encoding of
activation spikes. We adaptively update a weight vector relying on confidence
estimates and activation offsets relative to previous activity. We regularize
updates proportion... | Spikes as regularizers | 2,016 | http://arxiv.org/pdf/1611.06245v1 | Title Spikes regularizers Summary present confidencebased singlelayer feedforward learning algorithm SPIRAL Spike Regularized Adaptive Learning relying encoding activation spike adaptively update weight vector relying confidence estimate activation offset relative previous activity regularize update proportionally item... | [-0.05139300599694252, -0.023373285308480263, -0.01818792149424553, 0.007478630170226097, 0.028674595057964325, -0.01217301283031702, -0.0011967546306550503, -0.006157814525067806, -0.0016135062323883176, 0.06982237845659256, 0.021045703440904617, 0.03801541402935982, -0.008924521505832672, 0.07269978523254395, 0.04228... |
1,061 | 1,061 | ['Grzegorz Swirszcz', 'Wojciech Marian Czarnecki', 'Razvan Pascanu'] | 1611.06310v2 | There has been a lot of recent interest in trying to characterize the error
surface of deep models. This stems from a long standing question. Given that
deep networks are highly nonlinear systems optimized by local gradient methods,
why do they not seem to be affected by bad local minima? It is widely believed
that tra... | Local minima in training of neural networks | 2,016 | http://arxiv.org/pdf/1611.06310v2 | Title Local minimum training neural network Summary lot recent interest trying characterize error surface deep model stem long standing question Given deep network highly nonlinear system optimized local gradient method seem affected bad local minimum widely believed training deep model using gradient method work well ... | [-0.025040727108716965, 0.018828732892870903, 0.0020180989522486925, 0.03851878643035889, 0.016874512657523155, -0.029997412115335464, 0.048505693674087524, 0.010365145280957222, -0.013248194009065628, 0.04150661826133728, 0.007211761083453894, 0.031743504106998444, 0.0005282129859551787, 0.00604730611667037, 0.0477937... |
1,062 | 1,062 | ['Zhiguang Wang', 'Weizhong Yan', 'Tim Oates'] | 1611.06455v4 | We propose a simple but strong baseline for time series classification from
scratch with deep neural networks. Our proposed baseline models are pure
end-to-end without any heavy preprocessing on the raw data or feature crafting.
The proposed Fully Convolutional Network (FCN) achieves premium performance to
other state-... | Time Series Classification from Scratch with Deep Neural Networks: A
Strong Baseline | 2,016 | http://arxiv.org/pdf/1611.06455v4 | Title Time Series Classification Scratch Deep Neural Networks Strong Baseline Summary propose simple strong baseline time series classification scratch deep neural network proposed baseline model pure endtoend without heavy preprocessing raw data feature crafting proposed Fully Convolutional Network FCN achieves premiu... | [-0.06151938810944557, 0.044059768319129944, -0.03874833136796951, 0.004693314433097839, -0.017601080238819122, 0.02240394987165928, 0.042283061891794205, -0.01776360347867012, -0.007370558101683855, -0.004207574296742678, 0.04923185333609581, 0.008842306211590767, 0.040825922042131424, 0.08796941488981247, -0.00677602... |
1,063 | 1,063 | ['Gil Keren', 'Sivan Sabato', 'Björn Schuller'] | 1611.07743v1 | When humans learn a new concept, they might ignore examples that they cannot
make sense of at first, and only later focus on such examples, when they are
more useful for learning. We propose incorporating this idea of tunable
sensitivity for hard examples in neural network learning, using a new
generalization of the cr... | Tunable Sensitivity to Large Errors in Neural Network Training | 2,016 | http://arxiv.org/pdf/1611.07743v1 | Title Tunable Sensitivity Large Errors Neural Network Training Summary human learn new concept might ignore example cannot make sense first later focus example useful learning propose incorporating idea tunable sensitivity hard example neural network learning using new generalization crossentropy gradient step used pla... | [0.0001895406749099493, -0.009627725929021835, -0.04425737261772156, 0.03413127735257149, -0.004997651558369398, -0.03961694613099098, 0.02413165010511875, -0.009105011820793152, -0.024802515283226967, -0.007966424338519573, -0.04739246517419815, 0.04947291687130928, 0.04345014691352844, 0.05209972336888313, -0.0052366... |
1,064 | 1,064 | ['Meshia Cédric Oveneke', 'Mitchel Aliosha-Perez', 'Yong Zhao', 'Dongmei Jiang', 'Hichem Sahli'] | 1611.09232v1 | The omnipresence of deep learning architectures such as deep convolutional
neural networks (CNN)s is fueled by the synergistic combination of
ever-increasing labeled datasets and specialized hardware. Despite the
indisputable success, the reliance on huge amounts of labeled data and
specialized hardware can be a limiti... | Efficient Convolutional Auto-Encoding via Random Convexification and
Frequency-Domain Minimization | 2,016 | http://arxiv.org/pdf/1611.09232v1 | Title Efficient Convolutional AutoEncoding via Random Convexification FrequencyDomain Minimization Summary omnipresence deep learning architecture deep convolutional neural network CNNs fueled synergistic combination everincreasing labeled datasets specialized hardware Despite indisputable success reliance huge amount ... | [-0.002733794506639242, 0.07162054628133774, -0.006765868980437517, 0.07325270026922226, 0.036439448595047, -0.015761621296405792, 0.021122336387634277, -0.0011417802888900042, -0.07397232949733734, 0.04892192780971527, -0.010136629454791546, -0.0067984964698553085, -0.0021008949261158705, 0.046684496104717255, 0.00093... |
1,065 | 1,065 | ['Caglar Gulcehre', 'Sarath Chandar', 'Yoshua Bengio'] | 1701.08718v1 | Recent empirical results on long-term dependency tasks have shown that neural
networks augmented with an external memory can learn the long-term dependency
tasks more easily and achieve better generalization than vanilla recurrent
neural networks (RNN). We suggest that memory augmented neural networks can
reduce the ef... | Memory Augmented Neural Networks with Wormhole Connections | 2,017 | http://arxiv.org/pdf/1701.08718v1 | Title Memory Augmented Neural Networks Wormhole Connections Summary Recent empirical result longterm dependency task shown neural network augmented external memory learn longterm dependency task easily achieve better generalization vanilla recurrent neural network RNN suggest memory augmented neural network reduce effe... | [0.020384853705763817, 0.043414779007434845, -0.019371645525097847, 0.037320446223020554, -0.029785962775349617, -0.017390504479408264, -0.00362427718937397, -0.021455192938447, 0.0033462168648838997, -0.011203029192984104, -0.021561110392212868, -0.01146629173308611, 0.007640272844582796, 0.05182391032576561, 0.029674... |
1,066 | 1,066 | ['Andrew Sohn', 'Randal S. Olson', 'Jason H. Moore'] | 1702.01780v1 | Machine learning has been gaining traction in recent years to meet the demand
for tools that can efficiently analyze and make sense of the ever-growing
databases of biomedical data in health care systems around the world. However,
effectively using machine learning methods requires considerable domain
expertise, which ... | Toward the automated analysis of complex diseases in genome-wide
association studies using genetic programming | 2,017 | http://arxiv.org/pdf/1702.01780v1 | Title Toward automated analysis complex disease genomewide association study using genetic programming Summary Machine learning gaining traction recent year meet demand tool efficiently analyze make sense evergrowing database biomedical data health care system around world However effectively using machine learning met... | [-0.01747054234147072, 0.040198031812906265, -0.04613858461380005, -0.049972712993621826, 0.018341965973377228, 0.049574196338653564, 0.03016352467238903, 0.02071346528828144, 0.048469722270965576, 0.023042185232043266, 0.06908728182315826, 0.0017413890454918146, 0.03552968055009842, 0.0758456364274025, -0.011088464409... |
1,067 | 1,067 | ['Moshe Looks', 'Marcello Herreshoff', 'DeLesley Hutchins', 'Peter Norvig'] | 1702.02181v2 | Neural networks that compute over graph structures are a natural fit for
problems in a variety of domains, including natural language (parse trees) and
cheminformatics (molecular graphs). However, since the computation graph has a
different shape and size for every input, such networks do not directly support
batched t... | Deep Learning with Dynamic Computation Graphs | 2,017 | http://arxiv.org/pdf/1702.02181v2 | Title Deep Learning Dynamic Computation Graphs Summary Neural network compute graph structure natural fit problem variety domain including natural language parse tree cheminformatics molecular graph However since computation graph different shape size every input network directly support batched training inference also... | [-0.008267956785857677, -0.014635797590017319, -0.04825250059366226, 0.005312778986990452, -0.026782169938087463, -0.029162375256419182, -0.00019441884069237858, -0.012170019559562206, 0.03338523954153061, 0.028249187394976616, 0.012643994763493538, 0.00818469375371933, 0.012616272084414959, 0.13440857827663422, 0.0190... |
1,068 | 1,068 | ['Michael Kampffmeyer', 'Sigurd Løkse', 'Filippo Maria Bianchi', 'Robert Jenssen', 'Lorenzo Livi'] | 1702.02526v1 | In this paper we introduce the deep kernelized autoencoder, a neural network
model that allows an explicit approximation of (i) the mapping from an input
space to an arbitrary, user-specified kernel space and (ii) the back-projection
from such a kernel space to input space. The proposed method is based on
traditional a... | Deep Kernelized Autoencoders | 2,017 | http://arxiv.org/pdf/1702.02526v1 | Title Deep Kernelized Autoencoders Summary paper introduce deep kernelized autoencoder neural network model allows explicit approximation mapping input space arbitrary userspecified kernel space ii backprojection kernel space input space proposed method based traditional autoencoders trained new unsupervised loss funct... | [-0.0424896776676178, 0.040260255336761475, -0.009203486144542694, 0.07529452443122864, 0.01783420331776142, -0.02568083629012108, 0.03321047127246857, 0.010553828440606594, -0.045716144144535065, 0.019435163587331772, -0.014130617491900921, 0.0237068273127079, 0.004100973252207041, 0.07678387314081192, 0.0412604734301... |
1,069 | 1,069 | ['Eric W. Tramel', 'Marylou Gabrié', 'Andre Manoel', 'Francesco Caltagirone', 'Florent Krzakala'] | 1702.03260v2 | Restricted Boltzmann machines (RBMs) are energy-based neural-networks which
are commonly used as the building blocks for deep architectures neural
architectures. In this work, we derive a deterministic framework for the
training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer
(TAP) mean-field appro... | A Deterministic and Generalized Framework for Unsupervised Learning with
Restricted Boltzmann Machines | 2,017 | http://arxiv.org/pdf/1702.03260v2 | Title Deterministic Generalized Framework Unsupervised Learning Restricted Boltzmann Machines Summary Restricted Boltzmann machine RBMs energybased neuralnetworks commonly used building block deep architecture neural architecture work derive deterministic framework training evaluation use RBMs based upon ThoulessAnders... | [-0.04032556712627411, -0.0022950677666813135, -0.042117245495319366, 0.031403448432683945, 0.010676312260329723, 0.009829383343458176, 0.029624799266457558, -0.019647827371954918, -0.01837093010544777, 0.04781458154320717, -0.015097693540155888, 0.017857804894447327, -0.013851631432771683, 0.07567187398672104, 0.02860... |
1,070 | 1,070 | ['Dianhui Wang', 'Ming Li'] | 1702.04459v2 | Neural networks have been widely used as predictive models to fit data
distribution, and they could be implemented through learning a collection of
samples. In many applications, however, the given dataset may contain noisy
samples or outliers which may result in a poor learner model in terms of
generalization. This pa... | Robust Stochastic Configuration Networks with Kernel Density Estimation | 2,017 | http://arxiv.org/pdf/1702.04459v2 | Title Robust Stochastic Configuration Networks Kernel Density Estimation Summary Neural network widely used predictive model fit data distribution could implemented learning collection sample many application however given dataset may contain noisy sample outlier may result poor learner model term generalization paper ... | [-0.02971598505973816, -0.005998740904033184, -0.03633934631943703, 0.008485712110996246, 0.014882173389196396, -0.05329795554280281, -0.0026674116961658, -0.04438336566090584, -0.08371355384588242, 0.035479944199323654, 0.028966840356588364, 0.025127308443188667, 0.037924639880657196, 0.08133848756551743, 0.0146772274... |
1,071 | 1,071 | ['Mevlana Gemici', 'Chia-Chun Hung', 'Adam Santoro', 'Greg Wayne', 'Shakir Mohamed', 'Danilo J. Rezende', 'David Amos', 'Timothy Lillicrap'] | 1702.04649v2 | We consider the general problem of modeling temporal data with long-range
dependencies, wherein new observations are fully or partially predictable based
on temporally-distant, past observations. A sufficiently powerful temporal
model should separate predictable elements of the sequence from unpredictable
elements, exp... | Generative Temporal Models with Memory | 2,017 | http://arxiv.org/pdf/1702.04649v2 | Title Generative Temporal Models Memory Summary consider general problem modeling temporal data longrange dependency wherein new observation fully partially predictable based temporallydistant past observation sufficiently powerful temporal model separate predictable element sequence unpredictable element express uncer... | [0.01757582649588585, 0.0632890909910202, -0.023872578516602516, -0.017164133489131927, -0.03139231353998184, -0.03345639631152153, -0.009081427939236164, 0.004312305711209774, -0.006534235551953316, -0.002540197456255555, 0.07283607870340347, -0.029338544234633446, -0.00865310337394476, 0.0681542456150055, -0.00308079... |
1,072 | 1,072 | ['Haohan Wang', 'Bhiksha Raj'] | 1702.07800v4 | This paper is a review of the evolutionary history of deep learning models.
It covers from the genesis of neural networks when associationism modeling of
the brain is studied, to the models that dominate the last decade of research
in deep learning like convolutional neural networks, deep belief networks, and
recurrent... | On the Origin of Deep Learning | 2,017 | http://arxiv.org/pdf/1702.07800v4 | Title Origin Deep Learning Summary paper review evolutionary history deep learning model cover genesis neural network associationism modeling brain studied model dominate last decade research deep learning like convolutional neural network deep belief network recurrent neural network addition review model paper primari... | [0.022658944129943848, 0.04412216320633888, -0.045865725725889206, -0.013182910159230232, -0.007671241648495197, 0.004469872917979956, 0.0490889810025692, 0.0064223092049360275, -0.051274076104164124, -0.00012445323227439076, 0.009608177468180656, -0.021364765241742134, 0.01883193850517273, 0.0534035786986351, 0.026474... |
1,073 | 1,073 | ['David Balduzzi', 'Marcus Frean', 'Lennox Leary', 'JP Lewis', 'Kurt Wan-Duo Ma', 'Brian McWilliams'] | 1702.08591v1 | A long-standing obstacle to progress in deep learning is the problem of
vanishing and exploding gradients. The problem has largely been overcome
through the introduction of carefully constructed initializations and batch
normalization. Nevertheless, architectures incorporating skip-connections such
as resnets perform m... | The Shattered Gradients Problem: If resnets are the answer, then what is
the question? | 2,017 | http://arxiv.org/pdf/1702.08591v1 | Title Shattered Gradients Problem resnets answer question Summary longstanding obstacle progress deep learning problem vanishing exploding gradient problem largely overcome introduction carefully constructed initialization batch normalization Nevertheless architecture incorporating skipconnections resnets perform much ... | [-0.05306291580200195, 0.08462988585233688, 0.003910399042069912, 0.05466694384813309, 0.015272182412445545, -0.02508697472512722, 0.036428868770599365, -0.03013664484024048, -0.06501620262861252, 0.04085691273212433, 0.0043370164930820465, -0.0031224077101796865, 0.021023709326982498, 0.0664471983909607, 0.01241520419... |
1,074 | 1,074 | ['Sanjeev Arora', 'Rong Ge', 'Yingyu Liang', 'Tengyu Ma', 'Yi Zhang'] | 1703.00573v5 | We show that training of generative adversarial network (GAN) may not have
good generalization properties; e.g., training may appear successful but the
trained distribution may be far from target distribution in standard metrics.
However, generalization does occur for a weaker metric called neural net
distance. It is a... | Generalization and Equilibrium in Generative Adversarial Nets (GANs) | 2,017 | http://arxiv.org/pdf/1703.00573v5 | Title Generalization Equilibrium Generative Adversarial Nets GANs Summary show training generative adversarial network GAN may good generalization property eg training may appear successful trained distribution may far target distribution standard metric However generalization occur weaker metric called neural net dist... | [0.0004752659588120878, 0.09058456122875214, -0.02210065722465515, -0.002524065785109997, -0.0011906065046787262, -0.01998322270810604, 0.018323440104722977, 0.012895728461444378, -0.05703878402709961, 0.03898852691054344, -0.003697628853842616, 0.004836162086576223, -0.021246090531349182, 0.0032145746517926455, 0.0695... |
1,075 | 1,075 | ['Antti Tarvainen', 'Harri Valpola'] | 1703.01780v5 | The recently proposed Temporal Ensembling has achieved state-of-the-art
results in several semi-supervised learning benchmarks. It maintains an
exponential moving average of label predictions on each training example, and
penalizes predictions that are inconsistent with this target. However, because
the targets change ... | Mean teachers are better role models: Weight-averaged consistency
targets improve semi-supervised deep learning results | 2,017 | http://arxiv.org/pdf/1703.01780v5 | Title Mean teacher better role model Weightaveraged consistency target improve semisupervised deep learning result Summary recently proposed Temporal Ensembling achieved stateoftheart result several semisupervised learning benchmark maintains exponential moving average label prediction training example penalizes predic... | [0.011877510696649551, 0.06664077937602997, -0.023936349898576736, 0.02185123786330223, 0.03708719089627266, -0.013958770781755447, 0.04595377296209335, -0.04740389809012413, -0.0678393617272377, 0.002637114142999053, -0.017652131617069244, -0.02702568843960762, 0.004606575705111027, 0.06541435420513153, 0.007885558530... |
1,076 | 1,076 | ['Or Sharir', 'Amnon Shashua'] | 1703.02065v4 | Expressive efficiency refers to the relation between two architectures A and
B, whereby any function realized by B could be replicated by A, but there
exists functions realized by A, which cannot be replicated by B unless its size
grows significantly larger. For example, it is known that deep networks are
exponentially... | On the Expressive Power of Overlapping Architectures of Deep Learning | 2,017 | http://arxiv.org/pdf/1703.02065v4 | Title Expressive Power Overlapping Architectures Deep Learning Summary Expressive efficiency refers relation two architecture B whereby function realized B could replicated exists function realized cannot replicated B unless size grows significantly larger example known deep network exponentially efficient respect shal... | [-0.028094440698623657, 0.05080948397517204, -0.019131144508719444, 0.07281231880187988, -0.017669323831796646, -0.03467448428273201, 0.038127392530441284, -0.02683134563267231, -0.04156096652150154, 0.01800176128745079, -0.05253916233778, 0.008035933598876, 0.011621705256402493, 0.0717770978808403, 0.02769280970096588... |
1,077 | 1,077 | ['Olga Wichrowska', 'Niru Maheswaranathan', 'Matthew W. Hoffman', 'Sergio Gomez Colmenarejo', 'Misha Denil', 'Nando de Freitas', 'Jascha Sohl-Dickstein'] | 1703.04813v4 | Learning to learn has emerged as an important direction for achieving
artificial intelligence. Two of the primary barriers to its adoption are an
inability to scale to larger problems and a limited ability to generalize to
new tasks. We introduce a learned gradient descent optimizer that generalizes
well to new tasks, ... | Learned Optimizers that Scale and Generalize | 2,017 | http://arxiv.org/pdf/1703.04813v4 | Title Learned Optimizers Scale Generalize Summary Learning learn emerged important direction achieving artificial intelligence Two primary barrier adoption inability scale larger problem limited ability generalize new task introduce learned gradient descent optimizer generalizes well new task significantly reduced memo... | [0.00554680498316884, 0.02597377635538578, -0.018615232780575752, 0.03551197424530983, 0.004616288002580404, -0.007247465662658215, 0.0568012073636055, 0.007626159116625786, -0.08842956274747849, -0.014408163726329803, -0.03654739633202553, 0.056028690189123154, 0.025739187374711037, 0.012816272675991058, -0.0009048736... |
1,078 | 1,078 | ['Ramin M. Hasani', 'Guodong Wang', 'Radu Grosu'] | 1703.06272v1 | This paper studies an intelligent ultimate technique for health-monitoring
and prognostic of common rotary machine components, particularly bearings.
During a run-to-failure experiment, rich unsupervised features from vibration
sensory data are extracted by a trained sparse auto-encoder. Then, the
correlation of the ex... | An Automated Auto-encoder Correlation-based Health-Monitoring and
Prognostic Method for Machine Bearings | 2,017 | http://arxiv.org/pdf/1703.06272v1 | Title Automated Autoencoder Correlationbased HealthMonitoring Prognostic Method Machine Bearings Summary paper study intelligent ultimate technique healthmonitoring prognostic common rotary machine component particularly bearing runtofailure experiment rich unsupervised feature vibration sensory data extracted trained ... | [-0.058137550950050354, 0.04175381734967232, -0.016325121745467186, 0.039435975253582, 0.056581854820251465, 0.0016908240504562855, -0.011529015377163887, 0.050247449427843094, -0.06116427481174469, 0.007793418597429991, 0.06878956407308578, 0.05727796256542206, 0.021545078605413437, 0.04878242686390877, -0.00956003926... |
1,079 | 1,079 | ['William La Cava', 'Jason H. Moore'] | 1703.06934v3 | Recently we proposed a general, ensemble-based feature engineering wrapper
(FEW) that was paired with a number of machine learning methods to solve
regression problems. Here, we adapt FEW for supervised classification and
perform a thorough analysis of fitness and survival methods within this
framework. Our tests demon... | Ensemble representation learning: an analysis of fitness and survival
for wrapper-based genetic programming methods | 2,017 | http://arxiv.org/pdf/1703.06934v3 | Title Ensemble representation learning analysis fitness survival wrapperbased genetic programming method Summary Recently proposed general ensemblebased feature engineering wrapper paired number machine learning method solve regression problem adapt supervised classification perform thorough analysis fitness survival m... | [-0.003239601617679, 0.025448648259043694, -0.04197628051042557, -0.03391646221280098, 0.0317525789141655, 0.0031980187632143497, 0.013548492453992367, 0.023654265329241753, -0.007564590312540531, -0.003999680746346712, 0.05780627205967903, -0.0185535941272974, 0.017537806183099747, 0.03596587851643562, -0.017810583114... |
1,080 | 1,080 | ['Shai Shalev-Shwartz', 'Ohad Shamir', 'Shaked Shammah'] | 1703.07950v2 | In recent years, Deep Learning has become the go-to solution for a broad
range of applications, often outperforming state-of-the-art. However, it is
important, for both theoreticians and practitioners, to gain a deeper
understanding of the difficulties and limitations associated with common
approaches and algorithms. W... | Failures of Gradient-Based Deep Learning | 2,017 | http://arxiv.org/pdf/1703.07950v2 | Title Failures GradientBased Deep Learning Summary recent year Deep Learning become goto solution broad range application often outperforming stateoftheart However important theoretician practitioner gain deeper understanding difficulty limitation associated common approach algorithm describe four type simple problem g... | [-0.004683353938162327, 0.049813274294137955, -0.009041251614689827, 0.026926519349217415, 0.002914546988904476, -0.011987301521003246, 0.05571373924612999, 0.013933539390563965, -0.014835190027952194, -0.013096559792757034, 0.030153730884194374, -0.012392301112413406, 0.018408115953207016, 0.0473383292555809, 0.010164... |
1,081 | 1,081 | ['David Ha', 'Douglas Eck'] | 1704.03477v4 | We present sketch-rnn, a recurrent neural network (RNN) able to construct
stroke-based drawings of common objects. The model is trained on thousands of
crude human-drawn images representing hundreds of classes. We outline a
framework for conditional and unconditional sketch generation, and describe new
robust training ... | A Neural Representation of Sketch Drawings | 2,017 | http://arxiv.org/pdf/1704.03477v4 | Title Neural Representation Sketch Drawings Summary present sketchrnn recurrent neural network RNN able construct strokebased drawing common object model trained thousand crude humandrawn image representing hundred class outline framework conditional unconditional sketch generation describe new robust training method g... | [0.002072283998131752, 0.001426812494173646, -0.007050015963613987, 0.02355545572936535, -0.01717188209295273, -0.014916708692908287, 0.010072977282106876, -0.006182495038956404, -0.08580172806978226, 0.010229522362351418, 0.006240080110728741, -0.05838477611541748, 0.0029572630301117897, 0.029146021232008934, 0.008644... |
1,082 | 1,082 | ['Y. Cem Subakan', 'Paris Smaragdis'] | 1704.05420v2 | In this paper, we propose a new Recurrent Neural Network (RNN) architecture.
The novelty is simple: We use diagonal recurrent matrices instead of full. This
results in better test likelihood and faster convergence compared to regular
full RNNs in most of our experiments. We show the benefits of using diagonal
recurrent... | Diagonal RNNs in Symbolic Music Modeling | 2,017 | http://arxiv.org/pdf/1704.05420v2 | Title Diagonal RNNs Symbolic Music Modeling Summary paper propose new Recurrent Neural Network RNN architecture novelty simple use diagonal recurrent matrix instead full result better test likelihood faster convergence compared regular full RNNs experiment show benefit using diagonal recurrent matrix popularly used LST... | [-0.010554762557148933, 0.020659727975726128, -0.0253631379455328, 0.060117144137620926, -0.05250813812017441, -0.022121572867035866, 0.04386484995484352, 0.016779771074652672, -0.04991058260202408, 0.013138765469193459, -0.05485602840781212, 0.007838954217731953, 0.033968374133110046, -0.010870550759136677, 0.02885793... |
1,083 | 1,083 | ['Davide Bacciu', 'Francesco Crecchi', 'Davide Morelli'] | 1705.02643v1 | The paper presents a novel, principled approach to train recurrent neural
networks from the Reservoir Computing family that are robust to missing part of
the input features at prediction time. By building on the ensembling properties
of Dropout regularization, we propose a methodology, named DropIn, which
efficiently t... | DropIn: Making Reservoir Computing Neural Networks Robust to Missing
Inputs by Dropout | 2,017 | http://arxiv.org/pdf/1705.02643v1 | Title DropIn Making Reservoir Computing Neural Networks Robust Missing Inputs Dropout Summary paper present novel principled approach train recurrent neural network Reservoir Computing family robust missing part input feature prediction time building ensembling property Dropout regularization propose methodology named ... | [-0.022154180333018303, 0.01173417828977108, -0.008322242647409439, -0.005620358511805534, 0.0121510224416852, -0.017016639932990074, 0.001384788192808628, -0.045691341161727905, -0.007102271541953087, 0.004455183167010546, 0.05254826694726944, 0.020588377490639687, 0.02012738212943077, 0.06040531024336815, 0.015332243... |
1,084 | 1,084 | ['Tayfun Gokmen', 'O. Murat Onen', 'Wilfried Haensch'] | 1705.08014v1 | In a previous work we have detailed the requirements to obtain a maximal
performance benefit by implementing fully connected deep neural networks (DNN)
in form of arrays of resistive devices for deep learning. This concept of
Resistive Processing Unit (RPU) devices we extend here towards convolutional
neural networks (... | Training Deep Convolutional Neural Networks with Resistive Cross-Point
Devices | 2,017 | http://arxiv.org/pdf/1705.08014v1 | Title Training Deep Convolutional Neural Networks Resistive CrossPoint Devices Summary previous work detailed requirement obtain maximal performance benefit implementing fully connected deep neural network DNN form array resistive device deep learning concept Resistive Processing Unit RPU device extend towards convolut... | [-0.003952740225940943, -0.004424635320901871, -0.03168492391705513, 0.08012476563453674, 0.02246415615081787, -0.03856043890118599, 0.08240853995084763, -0.04155397787690163, 0.028687527403235435, 0.0005882265395484865, -0.05793306976556778, 0.02462966926395893, 0.021634044125676155, 0.0019402351463213563, 0.026766458... |
1,085 | 1,085 | ['Friedemann Zenke', 'Surya Ganguli'] | 1705.11146v2 | A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico.... | SuperSpike: Supervised learning in multi-layer spiking neural networks | 2,017 | http://arxiv.org/pdf/1705.11146v2 | Title SuperSpike Supervised learning multilayer spiking neural network Summary vast majority computation brain performed spiking neural network Despite ubiquity spiking currently lack understanding biological spiking neural circuit learn compute invivo well instantiate capability artificial spiking circuit insilico rev... | [-0.02741864323616028, -0.054659098386764526, -0.024781418964266777, 0.014508717693388462, 0.05291152000427246, -0.0021210929844528437, 0.01844301074743271, -0.026208391413092613, -0.0036614658311009407, 0.010912706144154072, -0.027361368760466576, 0.0462682880461216, -0.011503471061587334, 0.07688760757446289, 0.04937... |
1,086 | 1,086 | ['Adepu Ravi Sankar', 'Vineeth N Balasubramanian'] | 1706.02052v1 | Recent years have seen a growing interest in understanding deep neural
networks from an optimization perspective. It is understood now that converging
to low-cost local minima is sufficient for such models to become effective in
practice. However, in this work, we propose a new hypothesis based on recent
theoretical fi... | Are Saddles Good Enough for Deep Learning? | 2,017 | http://arxiv.org/pdf/1706.02052v1 | Title Saddles Good Enough Deep Learning Summary Recent year seen growing interest understanding deep neural network optimization perspective understood converging lowcost local minimum sufficient model become effective practice However work propose new hypothesis based recent theoretical finding empirical study deep ne... | [-0.01636373996734619, 0.03703011944890022, -0.008653861470520496, 0.008967648260295391, 0.003082389011979103, -0.0404864139854908, 0.02635808289051056, -0.00867127813398838, -0.03875653073191643, 0.03163686767220497, -0.018598459661006927, -0.02413289062678814, 0.004278802312910557, 0.033016450703144073, 0.03131746873... |
1,087 | 1,087 | ['Li Jing', 'Caglar Gulcehre', 'John Peurifoy', 'Yichen Shen', 'Max Tegmark', 'Marin Soljačić', 'Yoshua Bengio'] | 1706.02761v3 | We present a novel recurrent neural network (RNN) based model that combines
the remembering ability of unitary RNNs with the ability of gated RNNs to
effectively forget redundant/irrelevant information in its memory. We achieve
this by extending unitary RNNs with a gating mechanism. Our model is able to
outperform LSTM... | Gated Orthogonal Recurrent Units: On Learning to Forget | 2,017 | http://arxiv.org/pdf/1706.02761v3 | Title Gated Orthogonal Recurrent Units Learning Forget Summary present novel recurrent neural network RNN based model combine remembering ability unitary RNNs ability gated RNNs effectively forget redundantirrelevant information memory achieve extending unitary RNNs gating mechanism model able outperform LSTMs GRUs Uni... | [-0.007480069994926453, 0.04770495370030403, 0.023244548588991165, 0.015302649699151516, -0.033076025545597076, -0.005800533574074507, -0.00015856995014473796, -0.01573568396270275, -0.018688447773456573, -0.014689544215798378, -0.02922867238521576, -0.04847094789147377, 0.018516138195991516, -0.01492219790816307, 0.00... |
1,088 | 1,088 | ['Matus Telgarsky'] | 1706.03301v1 | Neural networks and rational functions efficiently approximate each other. In
more detail, it is shown here that for any ReLU network, there exists a
rational function of degree $O(\text{polylog}(1/\epsilon))$ which is
$\epsilon$-close, and similarly for any rational function there exists a ReLU
network of size $O(\tex... | Neural networks and rational functions | 2,017 | http://arxiv.org/pdf/1706.03301v1 | Title Neural network rational function Summary Neural network rational function efficiently approximate detail shown ReLU network exists rational function degree Otextpolylog1epsilon epsilonclose similarly rational function exists ReLU network size Otextpolylog1epsilon epsilonclose contrast polynomial need degree Omega... | [-0.07772735506296158, 0.051522862166166306, 0.012671001255512238, 0.06254872679710388, 0.01926160417497158, -0.020529543980956078, 0.009075514040887356, 0.012158327735960484, -0.009596889838576317, -0.02108626812696457, 0.03329392895102501, 0.009260141290724277, 0.006060292944312096, 0.055068351328372955, 0.0224348120... |
1,089 | 1,089 | ['Timoleon Moraitis', 'Abu Sebastian', 'Irem Boybat', 'Manuel Le Gallo', 'Tomas Tuma', 'Evangelos Eleftheriou'] | 1706.05563v1 | Spiking neural networks (SNNs) could play a key role in unsupervised machine
learning applications, by virtue of strengths related to learning from the fine
temporal structure of event-based signals. However, some spike-timing-related
strengths of SNNs are hindered by the sensitivity of spike-timing-dependent
plasticit... | Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate
Codes | 2,017 | http://arxiv.org/pdf/1706.05563v1 | Title Fatiguing STDP Learning SpikeTiming Codes Presence Rate Codes Summary Spiking neural network SNNs could play key role unsupervised machine learning application virtue strength related learning fine temporal structure eventbased signal However spiketimingrelated strength SNNs hindered sensitivity spiketimingdepend... | [-0.08710033446550369, -0.05305293947458267, -0.030626080930233, 0.07477153837680817, 0.029601221904158592, -0.015349716879427433, 0.011585241183638573, -0.0382562018930912, 0.027124693617224693, 0.02741517499089241, -0.010895184241235256, 0.03911459818482399, 0.002717064693570137, 0.08500304073095322, 0.01383508555591... |
1,090 | 1,090 | ['Alfred Bourely', 'John Patrick Boueri', 'Krzysztof Choromonski'] | 1706.05683v1 | We propose Sparse Neural Network architectures that are based on random or
structured bipartite graph topologies. Sparse architectures provide compression
of the models learned and speed-ups of computations, they can also surpass
their unstructured or fully connected counterparts. As we show, even more
compact topologi... | Sparse Neural Networks Topologies | 2,017 | http://arxiv.org/pdf/1706.05683v1 | Title Sparse Neural Networks Topologies Summary propose Sparse Neural Network architecture based random structured bipartite graph topology Sparse architecture provide compression model learned speedup computation also surpass unstructured fully connected counterpart show even compact topology socalled SNN Sparse Neura... | [-0.020375169813632965, 0.01855674386024475, -0.01683023013174534, 0.05763872340321541, -0.032457973808050156, -0.07128623127937317, 3.731029573827982e-05, -0.0336407795548439, 0.013529010117053986, 0.023449791595339775, -0.007047354243695736, -0.017873087897896767, 0.01164446584880352, 0.0521371029317379, 0.0239701885... |
1,091 | 1,091 | ['Aleksander Madry', 'Aleksandar Makelov', 'Ludwig Schmidt', 'Dimitris Tsipras', 'Adrian Vladu'] | 1706.06083v3 | Recent work has demonstrated that neural networks are vulnerable to
adversarial examples, i.e., inputs that are almost indistinguishable from
natural data and yet classified incorrectly by the network. In fact, some of
the latest findings suggest that the existence of adversarial attacks may be an
inherent weakness of ... | Towards Deep Learning Models Resistant to Adversarial Attacks | 2,017 | http://arxiv.org/pdf/1706.06083v3 | Title Towards Deep Learning Models Resistant Adversarial Attacks Summary Recent work demonstrated neural network vulnerable adversarial example ie input almost indistinguishable natural data yet classified incorrectly network fact latest finding suggest existence adversarial attack may inherent weakness deep learning m... | [0.014816870912909508, 0.05214658007025719, -0.018002264201641083, 0.04595234990119934, -0.010541584342718124, -0.02386871539056301, 0.04798188805580139, -0.03780365735292435, -0.000369595451047644, -0.026287145912647247, -0.018617894500494003, 0.0027225359808653593, -0.0018221413483843207, 0.04532580077648163, 0.06297... |
1,092 | 1,092 | ['Peter Bartlett', 'Dylan J. Foster', 'Matus Telgarsky'] | 1706.08498v2 | This paper presents a margin-based multiclass generalization bound for neural
networks that scales with their margin-normalized "spectral complexity": their
Lipschitz constant, meaning the product of the spectral norms of the weight
matrices, times a certain correction factor. This bound is empirically
investigated for... | Spectrally-normalized margin bounds for neural networks | 2,017 | http://arxiv.org/pdf/1706.08498v2 | Title Spectrallynormalized margin bound neural network Summary paper present marginbased multiclass generalization bound neural network scale marginnormalized spectral complexity Lipschitz constant meaning product spectral norm weight matrix time certain correction factor bound empirically investigated standard AlexNet... | [-0.04155050963163376, -0.008558375760912895, -0.033014170825481415, -0.004344085231423378, 0.013449542224407196, -0.02681571990251541, 0.03941807523369789, -0.01090234238654375, -0.0007514691678807139, 0.04652928188443184, -0.0039003698620945215, 0.0069671496748924255, -0.025340158492326736, 0.03138614073395729, -0.00... |
1,093 | 1,093 | ['Miguel Á. Carreira-Perpiñán'] | 1707.01209v1 | Compressing neural nets is an active research problem, given the large size
of state-of-the-art nets for tasks such as object recognition, and the
computational limits imposed by mobile devices. We give a general formulation
of model compression as constrained optimization. This includes many types of
compression: quan... | Model compression as constrained optimization, with application to
neural nets. Part I: general framework | 2,017 | http://arxiv.org/pdf/1707.01209v1 | Title Model compression constrained optimization application neural net Part general framework Summary Compressing neural net active research problem given large size stateoftheart net task object recognition computational limit imposed mobile device give general formulation model compression constrained optimization i... | [-0.01731552556157112, 0.04729483649134636, 0.0009190910495817661, 0.031873706728219986, 0.0026739088352769613, -0.020321691408753395, 0.021075960248708725, 0.047936223447322845, -0.05037736892700195, -0.003749072551727295, 0.0034149077255278826, 0.016629399731755257, 0.015772700309753418, -0.025452492758631706, 0.0174... |
1,094 | 1,094 | ['Miguel Á. Carreira-Perpiñán', 'Yerlan Idelbayev'] | 1707.04319v1 | We consider the problem of deep neural net compression by quantization: given
a large, reference net, we want to quantize its real-valued weights using a
codebook with $K$ entries so that the training loss of the quantized net is
minimal. The codebook can be optimally learned jointly with the net, or fixed,
as for bina... | Model compression as constrained optimization, with application to
neural nets. Part II: quantization | 2,017 | http://arxiv.org/pdf/1707.04319v1 | Title Model compression constrained optimization application neural net Part II quantization Summary consider problem deep neural net compression quantization given large reference net want quantize realvalued weight using codebook K entry training loss quantized net minimal codebook optimally learned jointly net fixed... | [-0.02916509099304676, 0.08302726596593857, -0.0007826504297554493, 0.006499247159808874, 0.012315145693719387, -0.012033206410706043, 0.007497633341699839, 0.0883617177605629, -0.08171931654214859, 0.012326371856033802, 0.0276323314756155, 0.005068651866167784, -0.025546912103891373, 0.018155327066779137, 0.0245215147... |
1,095 | 1,095 | ['Andrei Turkin'] | 1708.02979v1 | It is a well-known fact that adding noise to the input data often improves
network performance. While the dropout technique may be a cause of memory loss,
when it is applied to recurrent connections, Tikhonov regularization, which can
be regarded as the training with additive noise, avoids this issue naturally,
though ... | Tikhonov Regularization for Long Short-Term Memory Networks | 2,017 | http://arxiv.org/pdf/1708.02979v1 | Title Tikhonov Regularization Long ShortTerm Memory Networks Summary wellknown fact adding noise input data often improves network performance dropout technique may cause memory loss applied recurrent connection Tikhonov regularization regarded training additive noise avoids issue naturally though implies regularizer d... | [0.010209091939032078, 0.004556693136692047, 0.01598581112921238, 0.061207231134176254, 0.05045241490006447, -0.023160258308053017, 0.02056543156504631, -0.011972963809967041, -0.021164556965231895, -0.004597627557814121, -0.024111447855830193, -0.004807803779840469, 0.027682339772582054, 0.03633328154683113, 0.0296173... |
1,096 | 1,096 | ['Klaus Greff', 'Sjoerd van Steenkiste', 'Jürgen Schmidhuber'] | 1708.03498v2 | Many real world tasks such as reasoning and physical interaction require
identification and manipulation of conceptual entities. A first step towards
solving these tasks is the automated discovery of distributed symbol-like
representations. In this paper, we explicitly formalize this problem as
inference in a spatial m... | Neural Expectation Maximization | 2,017 | http://arxiv.org/pdf/1708.03498v2 | Title Neural Expectation Maximization Summary Many real world task reasoning physical interaction require identification manipulation conceptual entity first step towards solving task automated discovery distributed symbollike representation paper explicitly formalize problem inference spatial mixture model component p... | [-0.008433626964688301, 0.016918597742915154, -0.006526081822812557, 0.06968424469232559, 0.005588540807366371, -0.013017911463975906, 0.052101980894804, -0.029498402029275894, 0.0013138457434251904, -0.009275843389332294, 0.005174507852643728, 0.025917036458849907, 0.004535451997071505, -0.0054769571870565414, 0.04130... |
1,097 | 1,097 | ['Ashley Prater'] | 1708.07147v1 | This work introduces a tensor-based method to perform supervised
classification on spatiotemporal data processed in an echo state network.
Typically when performing supervised classification tasks on data processed in
an echo state network, the entire collection of hidden layer node states from
the training dataset is ... | Classification via Tensor Decompositions of Echo State Networks | 2,017 | http://arxiv.org/pdf/1708.07147v1 | Title Classification via Tensor Decompositions Echo State Networks Summary work introduces tensorbased method perform supervised classification spatiotemporal data processed echo state network Typically performing supervised classification task data processed echo state network entire collection hidden layer node state... | [-0.05399998649954796, 0.01863001100718975, -0.032564617693424225, 0.04404335096478462, -0.008139513432979584, 0.033061474561691284, 0.08037623763084412, 0.020166991278529167, -0.023139137774705887, -0.021639293059706688, -0.01467151753604412, 0.001805002335458994, 0.04165005311369896, -0.0005517308600246906, -0.007382... |
1,098 | 1,098 | ['Robin Tibor Schirrmeister', 'Lukas Gemein', 'Katharina Eggensperger', 'Frank Hutter', 'Tonio Ball'] | 1708.08012v3 | We apply convolutional neural networks (ConvNets) to the task of
distinguishing pathological from normal EEG recordings in the Temple University
Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet
architectures recently shown to decode task-related information from EEG at
least as well as establish... | Deep learning with convolutional neural networks for decoding and
visualization of EEG pathology | 2,017 | http://arxiv.org/pdf/1708.08012v3 | Title Deep learning convolutional neural network decoding visualization EEG pathology Summary apply convolutional neural network ConvNets task distinguishing pathological normal EEG recording Temple University Hospital EEG Abnormal Corpus use two basic shallow deep ConvNet architecture recently shown decode taskrelated... | [-0.020881209522485733, -0.0007849079556763172, 0.006975267082452774, 0.028392890468239784, 0.042344383895397186, 0.030427228659391403, 0.016356797888875008, 0.046211127191782, -0.045808255672454834, 0.04334313049912453, -0.025741882622241974, 0.013166657648980618, 0.007784973829984665, 0.07731813192367554, 0.019739378... |
1,099 | 1,099 | ['Peng-Bo Zhang'] | 1708.08282v2 | In school, a teacher plays an important role in various classroom teaching
patterns. Likewise to this human learning activity, the learning using
privileged information (LUPI) paradigm provides additional information
generated by the teacher to 'teach' learning algorithms during the training
stage. Therefore, this nove... | A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+ | 2,017 | http://arxiv.org/pdf/1708.08282v2 | Title New Learning Paradigm Random Vector FunctionalLink Network RVFL Summary school teacher play important role various classroom teaching pattern Likewise human learning activity learning using privileged information LUPI paradigm provides additional information generated teacher teach learning algorithm training sta... | [0.010176077485084534, -0.03794378042221069, -0.045590806752443314, 0.03265431895852089, 0.020587285980582237, -0.05481868237257004, 0.002195412525907159, -0.029551386833190918, 0.01054745726287365, -0.02192939631640911, -0.0003719379601534456, 0.0010894577717408538, 0.005972177255898714, 0.023160308599472046, 0.023875... |
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