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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200 | 200 | ['Chengxi Ye', 'Yezhou Yang', 'Cornelia Fermuller', 'Yiannis Aloimonos'] | 1708.00631v1 | We explain that the difficulties of training deep neural networks come from a
syndrome of three consistency issues. This paper describes our efforts in their
analysis and treatment. The first issue is the training speed inconsistency in
different layers. We propose to address it with an intuitive,
simple-to-implement, ... | On the Importance of Consistency in Training Deep Neural Networks | 2,017 | http://arxiv.org/pdf/1708.00631v1 | Title Importance Consistency Training Deep Neural Networks Summary explain difficulty training deep neural network come syndrome three consistency issue paper describes effort analysis treatment first issue training speed inconsistency different layer propose address intuitive simpletoimplement low footprint secondorde... | [0.0011005856795236468, 0.0342542938888073, -0.0016036214074119925, 0.037525322288274765, 0.017938777804374695, -0.04064643010497093, 0.04413624480366707, -0.01656109280884266, -0.01651647314429283, 0.01686471328139305, -0.02132304757833481, -0.029987270012497902, 0.030414137989282608, 0.04707800969481468, -0.003312838... |
201 | 201 | ['Mario Amrehn', 'Sven Gaube', 'Mathias Unberath', 'Frank Schebesch', 'Tim Horz', 'Maddalena Strumia', 'Stefan Steidl', 'Markus Kowarschik', 'Andreas Maier'] | 1709.03450v1 | For complex segmentation tasks, fully automatic systems are inherently
limited in their achievable accuracy for extracting relevant objects.
Especially in cases where only few data sets need to be processed for a highly
accurate result, semi-automatic segmentation techniques exhibit a clear benefit
for the user. One ar... | UI-Net: Interactive Artificial Neural Networks for Iterative Image
Segmentation Based on a User Model | 2,017 | http://arxiv.org/pdf/1709.03450v1 | Title UINet Interactive Artificial Neural Networks Iterative Image Segmentation Based User Model Summary complex segmentation task fully automatic system inherently limited achievable accuracy extracting relevant object Especially case data set need processed highly accurate result semiautomatic segmentation technique ... | [-0.011895904317498207, -0.016781369224190712, 0.003017455106601119, 0.028427323326468468, -0.04797236993908882, -0.002457182854413986, 0.04309859871864319, 0.0015853755176067352, -0.03841213136911392, 0.05920279026031494, -0.023175353184342384, -0.01464101392775774, -0.007778484839946032, 0.029599057510495186, -0.0185... |
202 | 202 | ['Altaf H. Khan'] | 1712.05695v1 | Most of the weights in a Lightweight Neural Network have a value of zero,
while the remaining ones are either +1 or -1. These universal approximators
require approximately 1.1 bits/weight of storage, posses a quick forward pass
and achieve classification accuracies similar to conventional continuous-weight
networks. Th... | Lightweight Neural Networks | 2,017 | http://arxiv.org/pdf/1712.05695v1 | Title Lightweight Neural Networks Summary weight Lightweight Neural Network value zero remaining one either 1 1 universal approximators require approximately 11 bitsweight storage posse quick forward pas achieve classification accuracy similar conventional continuousweight network training regimen focus error reduction... | [-0.006239911075681448, 0.05687817186117172, -0.03179732337594032, 0.02979792095720768, 0.020963774994015694, 0.005468358751386404, 0.07468367367982864, 0.052600882947444916, 0.012772627174854279, 0.03470863029360771, 0.02001088112592697, 0.02225230261683464, 0.024142583832144737, 0.02948911115527153, 0.019600821658968... |
203 | 203 | ['Nathaniel Thomas', 'Tess Smidt', 'Steven Kearnes', 'Lusann Yang', 'Li Li', 'Kai Kohlhoff', 'Patrick Riley'] | 1802.08219v2 | We introduce tensor field networks, which are locally equivariant to 3D
rotations, translations, and permutations of points at every layer. 3D rotation
equivariance removes the need for data augmentation to identify features in
arbitrary orientations. Our network uses filters built from spherical
harmonics; due to the ... | Tensor Field Networks: Rotation- and Translation-Equivariant Neural
Networks for 3D Point Clouds | 2,018 | http://arxiv.org/pdf/1802.08219v2 | Title Tensor Field Networks Rotation TranslationEquivariant Neural Networks 3D Point Clouds Summary introduce tensor field network locally equivariant 3D rotation translation permutation point every layer 3D rotation equivariance remove need data augmentation identify feature arbitrary orientation network us filter bui... | [-0.001138135907240212, 0.023233946412801743, 0.017776718363165855, 0.006724439561367035, -0.0014553532237187028, -0.0018856192473322153, 0.04718993976712227, -0.01338037196546793, -0.03774923458695412, 0.0066506462171673775, -0.013602137565612793, 0.02383286878466606, 0.002643703017383814, 0.029266733676195145, 0.0519... |
204 | 204 | ['Çağlar Gülçehre', 'Yoshua Bengio'] | 1301.4083v6 | We explore the effect of introducing prior information into the intermediate
level of neural networks for a learning task on which all the state-of-the-art
machine learning algorithms tested failed to learn. We motivate our work from
the hypothesis that humans learn such intermediate concepts from other
individuals via... | Knowledge Matters: Importance of Prior Information for Optimization | 2,013 | http://arxiv.org/pdf/1301.4083v6 | Title Knowledge Matters Importance Prior Information Optimization Summary explore effect introducing prior information intermediate level neural network learning task stateoftheart machine learning algorithm tested failed learn motivate work hypothesis human learn intermediate concept individual via form supervision gu... | [0.014172586612403393, 0.009406535886228085, -0.023537883535027504, 0.02201433666050434, -0.001226184656843543, -0.014183464460074902, 0.04456879198551178, -0.0022789237555116415, -0.04931991919875145, 0.0088122533634305, 0.004124582279473543, 0.0367431603372097, 0.00808875821530819, 0.038832150399684906, 0.01729504764... |
205 | 205 | ['Kishore Konda', 'Roland Memisevic', 'David Krueger'] | 1402.3337v5 | Regularized training of an autoencoder typically results in hidden unit
biases that take on large negative values. We show that negative biases are a
natural result of using a hidden layer whose responsibility is to both
represent the input data and act as a selection mechanism that ensures sparsity
of the representati... | Zero-bias autoencoders and the benefits of co-adapting features | 2,014 | http://arxiv.org/pdf/1402.3337v5 | Title Zerobias autoencoders benefit coadapting feature Summary Regularized training autoencoder typically result hidden unit bias take large negative value show negative bias natural result using hidden layer whose responsibility represent input data act selection mechanism ensures sparsity representation show negative... | [-0.03741852939128876, 0.048517659306526184, -0.026960469782352448, 0.02670970931649208, 0.013095607049763203, 0.008037182502448559, 0.09301012754440308, -0.00367228826507926, -0.032445278018713, -0.014952633529901505, -0.021101243793964386, 0.03696267679333687, 0.009924480691552162, 0.08767807483673096, 0.040551718324... |
206 | 206 | ['Bodo Rueckauer', 'Iulia-Alexandra Lungu', 'Yuhuang Hu', 'Michael Pfeiffer'] | 1612.04052v1 | Deep convolutional neural networks (CNNs) have shown great potential for
numerous real-world machine learning applications, but performing inference in
large CNNs in real-time remains a challenge. We have previously demonstrated
that traditional CNNs can be converted into deep spiking neural networks
(SNNs), which exhi... | Theory and Tools for the Conversion of Analog to Spiking Convolutional
Neural Networks | 2,016 | http://arxiv.org/pdf/1612.04052v1 | Title Theory Tools Conversion Analog Spiking Convolutional Neural Networks Summary Deep convolutional neural network CNNs shown great potential numerous realworld machine learning application performing inference large CNNs realtime remains challenge previously demonstrated traditional CNNs converted deep spiking neura... | [-0.047626595944166183, -0.008519168011844158, -0.010641636326909065, 0.08292718231678009, 0.028569580987095833, -0.0191702451556921, 0.03178006783127785, -0.02696431241929531, -0.02704492025077343, -0.024147793650627136, -0.06486588716506958, 0.07105593383312225, -0.038517773151397705, 0.12279143184423447, 0.015830203... |
207 | 207 | ['Xun Huang', 'Yixuan Li', 'Omid Poursaeed', 'John Hopcroft', 'Serge Belongie'] | 1612.04357v4 | In this paper, we propose a novel generative model named Stacked Generative
Adversarial Networks (SGAN), which is trained to invert the hierarchical
representations of a bottom-up discriminative network. Our model consists of a
top-down stack of GANs, each learned to generate lower-level representations
conditioned on ... | Stacked Generative Adversarial Networks | 2,016 | http://arxiv.org/pdf/1612.04357v4 | Title Stacked Generative Adversarial Networks Summary paper propose novel generative model named Stacked Generative Adversarial Networks SGAN trained invert hierarchical representation bottomup discriminative network model consists topdown stack GANs learned generate lowerlevel representation conditioned higherlevel re... | [-0.026028765365481377, 0.08551148325204849, -0.008710755966603756, 0.03697074577212334, 0.01247104536741972, 0.01192604098469019, 0.03986614570021629, -0.004626457113772631, -0.027626855298876762, 0.012442037463188171, -0.04514726251363754, 0.003601840464398265, -0.02068101428449154, -0.004156286362558603, 0.069662697... |
208 | 208 | ['David Warde-Farley', 'Andrew Rabinovich', 'Dragomir Anguelov'] | 1412.6563v2 | We study the problem of large scale, multi-label visual recognition with a
large number of possible classes. We propose a method for augmenting a trained
neural network classifier with auxiliary capacity in a manner designed to
significantly improve upon an already well-performing model, while minimally
impacting its c... | Self-informed neural network structure learning | 2,014 | http://arxiv.org/pdf/1412.6563v2 | Title Selfinformed neural network structure learning Summary study problem large scale multilabel visual recognition large number possible class propose method augmenting trained neural network classifier auxiliary capacity manner designed significantly improve upon already wellperforming model minimally impacting comp... | [0.00026361903292126954, 0.018375858664512634, -0.0031296100933104753, 0.06841722130775452, 0.018402837216854095, -0.0008843803079798818, 0.05183177813887596, 0.012944181449711323, 0.01937745325267315, -0.02353578247129917, -0.06006372347474098, 0.030863041058182716, -0.04926510155200958, 0.015997005626559258, 0.026619... |
209 | 209 | ['Forest Agostinelli', 'Matthew Hoffman', 'Peter Sadowski', 'Pierre Baldi'] | 1412.6830v3 | Artificial neural networks typically have a fixed, non-linear activation
function at each neuron. We have designed a novel form of piecewise linear
activation function that is learned independently for each neuron using
gradient descent. With this adaptive activation function, we are able to
improve upon deep neural ne... | Learning Activation Functions to Improve Deep Neural Networks | 2,014 | http://arxiv.org/pdf/1412.6830v3 | Title Learning Activation Functions Improve Deep Neural Networks Summary Artificial neural network typically fixed nonlinear activation function neuron designed novel form piecewise linear activation function learned independently neuron using gradient descent adaptive activation function able improve upon deep neural ... | [-0.021282952278852463, 0.04590875282883644, -0.005355850327759981, 0.05935748293995857, 0.04437795281410217, -0.0030491596553474665, 0.044773463159799576, 0.004834912717342377, 0.023217419162392616, -0.012477260082960129, -0.025427183136343956, 0.021910693496465683, -0.029006775468587875, 0.10003077983856201, -0.00213... |
210 | 210 | ['Antti Rasmus', 'Tapani Raiko', 'Harri Valpola'] | 1412.7210v4 | Suitable lateral connections between encoder and decoder are shown to allow
higher layers of a denoising autoencoder (dAE) to focus on invariant
representations. In regular autoencoders, detailed information needs to be
carried through the highest layers but lateral connections from encoder to
decoder relieve this pres... | Denoising autoencoder with modulated lateral connections learns
invariant representations of natural images | 2,014 | http://arxiv.org/pdf/1412.7210v4 | Title Denoising autoencoder modulated lateral connection learns invariant representation natural image Summary Suitable lateral connection encoder decoder shown allow higher layer denoising autoencoder dAE focus invariant representation regular autoencoders detailed information need carried highest layer lateral connec... | [-0.039212666451931, 0.05988015606999397, -0.007838346995413303, 0.056840647011995316, 0.028272325173020363, -0.026427801698446274, 0.03760572895407677, -0.01976924017071724, -0.054584842175245285, -0.03468039631843567, -0.006670383736491203, 0.026429450139403343, 0.02657223306596279, 0.0709182396531105, 0.022801244631... |
211 | 211 | ['Ankit B. Patel', 'Tan Nguyen', 'Richard G. Baraniuk'] | 1504.00641v1 | A grand challenge in machine learning is the development of computational
algorithms that match or outperform humans in perceptual inference tasks that
are complicated by nuisance variation. For instance, visual object recognition
involves the unknown object position, orientation, and scale in object
recognition while ... | A Probabilistic Theory of Deep Learning | 2,015 | http://arxiv.org/pdf/1504.00641v1 | Title Probabilistic Theory Deep Learning Summary grand challenge machine learning development computational algorithm match outperform human perceptual inference task complicated nuisance variation instance visual object recognition involves unknown object position orientation scale object recognition speech recognitio... | [0.002162026474252343, 0.07437891513109207, -0.009023087099194527, 0.010961651802062988, 0.002599551109597087, -0.02297329716384411, 0.01750197820365429, -0.0030190821271389723, -0.08056651055812836, 0.012798402458429337, 0.020903363823890686, -0.02697143144905567, 0.03993939235806465, 0.08382690697908401, 0.0273410584... |
212 | 212 | ['Rein Houthooft', 'Filip De Turck'] | 1508.00451v4 | Tackling pattern recognition problems in areas such as computer vision,
bioinformatics, speech or text recognition is often done best by taking into
account task-specific statistical relations between output variables. In
structured prediction, this internal structure is used to predict multiple
outputs simultaneously,... | Integrated Inference and Learning of Neural Factors in Structural
Support Vector Machines | 2,015 | http://arxiv.org/pdf/1508.00451v4 | Title Integrated Inference Learning Neural Factors Structural Support Vector Machines Summary Tackling pattern recognition problem area computer vision bioinformatics speech text recognition often done best taking account taskspecific statistical relation output variable structured prediction internal structure used pr... | [0.020924311131238937, -0.03649092838168144, 0.01964947022497654, 0.0556345209479332, 0.016335025429725647, -0.016980592161417007, 0.026135742664337158, 0.028232399374246597, -0.0035930343437939882, -0.027327626943588257, -0.0528823547065258, -0.0008015804341994226, 0.036273837089538574, 0.052221789956092834, 0.0007711... |
213 | 213 | ['Patrick W. Gallagher', 'Shuai Tang', 'Zhuowen Tu'] | 1511.07125v1 | Top-down information plays a central role in human perception, but plays
relatively little role in many current state-of-the-art deep networks, such as
Convolutional Neural Networks (CNNs). This work seeks to explore a path by
which top-down information can have a direct impact within current deep
networks. We explore ... | What Happened to My Dog in That Network: Unraveling Top-down Generators
in Convolutional Neural Networks | 2,015 | http://arxiv.org/pdf/1511.07125v1 | Title Happened Dog Network Unraveling Topdown Generators Convolutional Neural Networks Summary Topdown information play central role human perception play relatively little role many current stateoftheart deep network Convolutional Neural Networks CNNs work seek explore path topdown information direct impact within cur... | [0.030013343319296837, 0.030576882883906364, -0.03516053408384323, 0.025222107768058777, 0.00966761913150549, 0.00573092931881547, 0.07170960307121277, 0.004153324291110039, -0.08175349980592728, -0.008145200088620186, -0.00856097973883152, 0.07306088507175446, 0.005758746527135372, 0.0571817010641098, 0.06772801280021... |
214 | 214 | ['Adrien Gaidon', 'Qiao Wang', 'Yohann Cabon', 'Eleonora Vig'] | 1605.06457v1 | Modern computer vision algorithms typically require expensive data
acquisition and accurate manual labeling. In this work, we instead leverage the
recent progress in computer graphics to generate fully labeled, dynamic, and
photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual
world cloning meth... | Virtual Worlds as Proxy for Multi-Object Tracking Analysis | 2,016 | http://arxiv.org/pdf/1605.06457v1 | Title Virtual Worlds Proxy MultiObject Tracking Analysis Summary Modern computer vision algorithm typically require expensive data acquisition accurate manual labeling work instead leverage recent progress computer graphic generate fully labeled dynamic photorealistic proxy virtual world propose efficient realtovirtual... | [-0.015725428238511086, 0.02745204232633114, 0.021197417750954628, 0.052757520228624344, -0.005117174703627825, -0.014248081482946873, 0.05115975812077522, -0.015235783532261848, -0.019489137455821037, 0.016809707507491112, 0.03321458399295807, -0.01713690720498562, -0.0009854338131844997, 0.05634380877017975, 0.051976... |
215 | 215 | ['Jianwen Xie', 'Song-Chun Zhu', 'Ying Nian Wu'] | 1606.00972v2 | Video sequences contain rich dynamic patterns, such as dynamic texture
patterns that exhibit stationarity in the temporal domain, and action patterns
that are non-stationary in either spatial or temporal domain. We show that a
spatial-temporal generative ConvNet can be used to model and synthesize dynamic
patterns. The... | Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet | 2,016 | http://arxiv.org/pdf/1606.00972v2 | Title Synthesizing Dynamic Patterns SpatialTemporal Generative ConvNet Summary Video sequence contain rich dynamic pattern dynamic texture pattern exhibit stationarity temporal domain action pattern nonstationary either spatial temporal domain show spatialtemporal generative ConvNet used model synthesize dynamic patter... | [-0.009735777042806149, 0.033621497452259064, -0.019019458442926407, 0.02915627881884575, -0.018329793587327003, -0.009356766007840633, 0.018577085807919502, -7.509900024160743e-05, -0.11104785650968552, -0.01958169788122177, 0.046330247074365616, -0.02671165019273758, -0.0072829932905733585, 0.08923191577196121, 0.025... |
216 | 216 | ['Mohammad Javad Shafiee', 'Akshaya Mishra', 'Alexander Wong'] | 1606.04393v3 | Taking inspiration from biological evolution, we explore the idea of "Can
deep neural networks evolve naturally over successive generations into highly
efficient deep neural networks?" by introducing the notion of synthesizing new
highly efficient, yet powerful deep neural networks over successive generations
via an ev... | Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural
Networks | 2,016 | http://arxiv.org/pdf/1606.04393v3 | Title Deep Learning Darwin Evolutionary Synthesis Deep Neural Networks Summary Taking inspiration biological evolution explore idea deep neural network evolve naturally successive generation highly efficient deep neural network introducing notion synthesizing new highly efficient yet powerful deep neural network succes... | [-0.023068277165293694, 0.04342826083302498, -0.05288401246070862, -0.008777724578976631, 0.010167833417654037, 0.0057924832217395306, 0.022848917171359062, -0.00561692425981164, -0.04466056451201439, 0.048386696726083755, -0.019895227625966072, 0.03785804286599159, -0.008577093482017517, 0.04098708555102348, 0.0276984... |
217 | 217 | ['Tian Han', 'Yang Lu', 'Song-Chun Zhu', 'Ying Nian Wu'] | 1606.08571v4 | This paper proposes an alternating back-propagation algorithm for learning
the generator network model. The model is a non-linear generalization of factor
analysis. In this model, the mapping from the continuous latent factors to the
observed signal is parametrized by a convolutional neural network. The
alternating bac... | Alternating Back-Propagation for Generator Network | 2,016 | http://arxiv.org/pdf/1606.08571v4 | Title Alternating BackPropagation Generator Network Summary paper proposes alternating backpropagation algorithm learning generator network model model nonlinear generalization factor analysis model mapping continuous latent factor observed signal parametrized convolutional neural network alternating backpropagation al... | [-0.01800454966723919, 0.04055950045585632, -0.0014842418022453785, 0.009536906145513058, -0.0030878777615725994, -0.026116665452718735, -0.016958091408014297, 0.00174429127946496, -0.07229884713888168, 0.016639191657304764, -0.015200141817331314, 0.00015803637506905943, -0.020448176190257072, 0.05180133134126663, 0.08... |
218 | 218 | ['Ilija Ilievski', 'Jiashi Feng'] | 1608.00218v1 | Recently, several optimization methods have been successfully applied to the
hyperparameter optimization of deep neural networks (DNNs). The methods work by
modeling the joint distribution of hyperparameter values and corresponding
error. Those methods become less practical when applied to modern DNNs whose
training ma... | Hyperparameter Transfer Learning through Surrogate Alignment for
Efficient Deep Neural Network Training | 2,016 | http://arxiv.org/pdf/1608.00218v1 | Title Hyperparameter Transfer Learning Surrogate Alignment Efficient Deep Neural Network Training Summary Recently several optimization method successfully applied hyperparameter optimization deep neural network DNNs method work modeling joint distribution hyperparameter value corresponding error method become le pract... | [-0.023147162050008774, 0.0573740117251873, -0.00635602418333292, 0.004228698089718819, 0.026486214250326157, -0.012647579424083233, 0.038649626076221466, -0.004795538727194071, -0.03584451228380203, 0.009933757595717907, -0.06142236664891243, 0.045735571533441544, 0.011374056339263916, 0.02665100060403347, -2.98173436... |
219 | 219 | ['Hao Wang', 'Dit-Yan Yeung'] | 1608.06884v2 | While perception tasks such as visual object recognition and text
understanding play an important role in human intelligence, the subsequent
tasks that involve inference, reasoning and planning require an even higher
level of intelligence. The past few years have seen major advances in many
perception tasks using deep ... | Towards Bayesian Deep Learning: A Framework and Some Existing Methods | 2,016 | http://arxiv.org/pdf/1608.06884v2 | Title Towards Bayesian Deep Learning Framework Existing Methods Summary perception task visual object recognition text understanding play important role human intelligence subsequent task involve inference reasoning planning require even higher level intelligence past year seen major advance many perception task using ... | [0.007974247448146343, 0.036423783749341965, 0.013382895849645138, 0.04231448471546173, -0.04866444692015648, 0.03920255973935127, 0.04171886667609215, 0.03536500409245491, -0.019592175260186195, -0.03266230970621109, 0.0006695681368000805, -0.012984338216483593, 0.03467118740081787, 0.08165556192398071, -0.01498672831... |
220 | 220 | ['Mason McGill', 'Pietro Perona'] | 1703.06217v2 | We propose and systematically evaluate three strategies for training
dynamically-routed artificial neural networks: graphs of learned
transformations through which different input signals may take different paths.
Though some approaches have advantages over others, the resulting networks are
often qualitatively similar... | Deciding How to Decide: Dynamic Routing in Artificial Neural Networks | 2,017 | http://arxiv.org/pdf/1703.06217v2 | Title Deciding Decide Dynamic Routing Artificial Neural Networks Summary propose systematically evaluate three strategy training dynamicallyrouted artificial neural network graph learned transformation different input signal may take different path Though approach advantage others resulting network often qualitatively ... | [0.006757006980478764, -0.015304154716432095, -0.039194539189338684, -0.02484578639268875, -0.05993489548563957, -0.046978265047073364, 0.016341058537364006, -0.04097360372543335, -0.03397383168339729, -0.011854507029056549, 0.023937376216053963, 0.025907054543495178, 0.015508369542658329, 0.050955552607774734, 0.04291... |
221 | 221 | ['Hongyang Gao', 'Hao Yuan', 'Zhengyang Wang', 'Shuiwang Ji'] | 1705.06820v4 | Deconvolutional layers have been widely used in a variety of deep models for
up-sampling, including encoder-decoder networks for semantic segmentation and
deep generative models for unsupervised learning. One of the key limitations of
deconvolutional operations is that they result in the so-called checkerboard
problem.... | Pixel Deconvolutional Networks | 2,017 | http://arxiv.org/pdf/1705.06820v4 | Title Pixel Deconvolutional Networks Summary Deconvolutional layer widely used variety deep model upsampling including encoderdecoder network semantic segmentation deep generative model unsupervised learning One key limitation deconvolutional operation result socalled checkerboard problem caused fact direct relationshi... | [-0.015216377563774586, 0.05134040117263794, 0.012215763330459595, 0.08516170084476471, -0.01683502085506916, -0.018124151974916458, 0.04815717786550522, 0.004856148734688759, -0.057793211191892624, 0.047311946749687195, 0.03756553307175636, 0.08593137562274933, 0.004859064240008593, 0.028719542548060417, -0.0064530260... |
222 | 222 | ['Stanislav Fort'] | 1708.02735v1 | We propose a novel architecture for $k$-shot classification on the Omniglot
dataset. Building on prototypical networks, we extend their architecture to
what we call Gaussian prototypical networks. Prototypical networks learn a map
between images and embedding vectors, and use their clustering for
classification. In our... | Gaussian Prototypical Networks for Few-Shot Learning on Omniglot | 2,017 | http://arxiv.org/pdf/1708.02735v1 | Title Gaussian Prototypical Networks FewShot Learning Omniglot Summary propose novel architecture kshot classification Omniglot dataset Building prototypical network extend architecture call Gaussian prototypical network Prototypical network learn map image embedding vector use clustering classification model part enco... | [-0.03872193396091461, 0.011896251700818539, -0.024651411920785904, 0.04198518395423889, 0.008748532272875309, -0.0013317536795511842, 0.05101098492741585, 0.01363440416753292, -0.016359755769371986, 0.015828387811779976, -0.0021112554240971804, 0.02167893573641777, 0.012797352857887745, 0.057533640414476395, 0.0296211... |
223 | 223 | ['Leslie N. Smith', 'Nicholay Topin'] | 1708.07120v2 | In this paper, we show a phenomenon, which we named "super-convergence",
where residual networks can be trained using an order of magnitude fewer
iterations than is used with standard training methods. The existence of
super-convergence is relevant to understanding why deep networks generalize
well. One of the key elem... | Super-Convergence: Very Fast Training of Residual Networks Using Large
Learning Rates | 2,017 | http://arxiv.org/pdf/1708.07120v2 | Title SuperConvergence Fast Training Residual Networks Using Large Learning Rates Summary paper show phenomenon named superconvergence residual network trained using order magnitude fewer iteration used standard training method existence superconvergence relevant understanding deep network generalize well One key eleme... | [0.004243234172463417, 0.01020099688321352, 0.0030279129277914762, 0.05753330886363983, 0.005873274523764849, 0.014268917962908745, -0.003872480010613799, 0.014631052501499653, -0.010071934200823307, 0.021705912426114082, 0.0007730689249001443, 0.019592784345149994, -0.04117584973573685, 0.009131697937846184, -0.002717... |
224 | 224 | ['Boris Flach', 'Alexander Shekhovtsov', 'Ondrej Fikar'] | 1709.08524v1 | Learning, taking into account full distribution of the data, referred to as
generative, is not feasible with deep neural networks (DNNs) because they model
only the conditional distribution of the outputs given the inputs. Current
solutions are either based on joint probability models facing difficult
estimation proble... | Generative learning for deep networks | 2,017 | http://arxiv.org/pdf/1709.08524v1 | Title Generative learning deep network Summary Learning taking account full distribution data referred generative feasible deep neural network DNNs model conditional distribution output given input Current solution either based joint probability model facing difficult estimation problem learn two separate network mappi... | [-0.013764153234660625, 0.06498578935861588, -0.03143179789185524, 0.03569284453988075, 0.021829647943377495, -0.028896350413560867, 0.060644734650850296, -0.018914001062512398, -0.024976620450615883, 0.03555653989315033, 0.02034589648246765, 0.012766139581799507, -0.009108430705964565, 0.05768049135804176, 0.034216392... |
225 | 225 | ['Hanxiao Liu', 'Karen Simonyan', 'Oriol Vinyals', 'Chrisantha Fernando', 'Koray Kavukcuoglu'] | 1711.00436v2 | We explore efficient neural architecture search methods and show that a
simple yet powerful evolutionary algorithm can discover new architectures with
excellent performance. Our approach combines a novel hierarchical genetic
representation scheme that imitates the modularized design pattern commonly
adopted by human ex... | Hierarchical Representations for Efficient Architecture Search | 2,017 | http://arxiv.org/pdf/1711.00436v2 | Title Hierarchical Representations Efficient Architecture Search Summary explore efficient neural architecture search method show simple yet powerful evolutionary algorithm discover new architecture excellent performance approach combine novel hierarchical genetic representation scheme imitates modularized design patte... | [0.01517266221344471, 0.0689282938838005, -0.05876625329256058, 0.060064010322093964, -0.0023158686235547066, 0.006358189973980188, 0.02171948365867138, 0.008171390742063522, -0.0018332767067477107, 0.005735564511269331, -0.04318080097436905, -0.0018145412905141711, 0.005118620116263628, 0.02851330302655697, 0.01081409... |
226 | 226 | ['Antreas Antoniou', 'Amos Storkey', 'Harrison Edwards'] | 1711.04340v3 | Effective training of neural networks requires much data. In the low-data
regime, parameters are underdetermined, and learnt networks generalise poorly.
Data Augmentation alleviates this by using existing data more effectively.
However standard data augmentation produces only limited plausible alternative
data. Given t... | Data Augmentation Generative Adversarial Networks | 2,017 | http://arxiv.org/pdf/1711.04340v3 | Title Data Augmentation Generative Adversarial Networks Summary Effective training neural network requires much data lowdata regime parameter underdetermined learnt network generalise poorly Data Augmentation alleviates using existing data effectively However standard data augmentation produce limited plausible alterna... | [0.007798369042575359, 0.1127941906452179, -0.008351664058864117, 0.01784515753388405, 0.036803483963012695, -0.015771817415952682, 0.04350386559963226, -0.01760702207684517, -0.021776631474494934, 0.010825569741427898, -0.015450343489646912, 0.040628865361213684, -0.019412493333220482, 0.06117817386984825, 0.075924500... |
227 | 227 | ['Dror Sholomon', 'Eli David', 'Nathan S. Netanyahu'] | 1711.08762v1 | This paper introduces the first deep neural network-based estimation metric
for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network
predicts whether or not they should be adjacent in the correct assembly of the
puzzle, using nothing but the pixels of each piece. The proposed metric
exhibits an e... | DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the
Jigsaw Puzzle Problem | 2,017 | http://arxiv.org/pdf/1711.08762v1 | Title DNNBuddies Deep Neural NetworkBased Estimation Metric Jigsaw Puzzle Problem Summary paper introduces first deep neural networkbased estimation metric jigsaw puzzle problem Given two puzzle piece edge neural network predicts whether adjacent correct assembly puzzle using nothing pixel piece proposed metric exhibit... | [-0.005605415441095829, 0.06549376994371414, -0.022269506007432938, 0.0740916058421135, -0.05972287803888321, -0.02298656292259693, 0.0314687080681324, -0.01741977035999298, -0.07045616209506989, 0.03507894277572632, 0.03322844207286835, 0.02667774073779583, 0.012714301235973835, 0.06369805335998535, 0.0132814226672053... |
228 | 228 | ['Eli David', 'Nathan S. Netanyahu'] | 1711.08763v1 | In this paper we describe the problem of painter classification, and propose
a novel approach based on deep convolutional autoencoder neural networks. While
previous approaches relied on image processing and manual feature extraction
from paintings, our approach operates on the raw pixel level, without any
preprocessin... | DeepPainter: Painter Classification Using Deep Convolutional
Autoencoders | 2,017 | http://arxiv.org/pdf/1711.08763v1 | Title DeepPainter Painter Classification Using Deep Convolutional Autoencoders Summary paper describe problem painter classification propose novel approach based deep convolutional autoencoder neural network previous approach relied image processing manual feature extraction painting approach operates raw pixel level w... | [0.006494715344160795, 0.06416041404008865, -0.014872116968035698, 0.0733407512307167, 0.014861884526908398, -0.002644152147695422, 0.036904990673065186, -0.017610760405659676, -0.013382405042648315, 0.0001574601628817618, -0.01190575398504734, -0.010635052807629108, 0.007070810999721289, 0.012400463223457336, -0.00093... |
229 | 229 | ['Ido Cohen', 'Eli David', 'Nathan S. Netanyahu', 'Noa Liscovitch', 'Gal Chechik'] | 1711.09663v1 | This paper presents a novel deep learning-based method for learning a
functional representation of mammalian neural images. The method uses a deep
convolutional denoising autoencoder (CDAE) for generating an invariant, compact
representation of in situ hybridization (ISH) images. While most existing
methods for bio-ima... | DeepBrain: Functional Representation of Neural In-Situ Hybridization
Images for Gene Ontology Classification Using Deep Convolutional Autoencoders | 2,017 | http://arxiv.org/pdf/1711.09663v1 | Title DeepBrain Functional Representation Neural InSitu Hybridization Images Gene Ontology Classification Using Deep Convolutional Autoencoders Summary paper present novel deep learningbased method learning functional representation mammalian neural image method us deep convolutional denoising autoencoder CDAE generati... | [-0.03399789705872536, 0.010101009160280228, -0.017971951514482498, 0.019560527056455612, 0.02207835391163826, 0.023933110758662224, 0.04776512458920479, 0.05417383462190628, 0.009116754867136478, 0.037622109055519104, -0.024749767035245895, -0.01833522878587246, 0.022730596363544464, 0.09499692916870117, 0.02136012539... |
230 | 230 | ['Omid Poursaeed', 'Isay Katsman', 'Bicheng Gao', 'Serge Belongie'] | 1712.02328v1 | In this paper, we propose novel generative models for creating adversarial
examples, slightly perturbed images resembling natural images but maliciously
crafted to fool pre-trained models. We present trainable deep neural networks
for transforming images to adversarial perturbations. Our proposed models can
produce ima... | Generative Adversarial Perturbations | 2,017 | http://arxiv.org/pdf/1712.02328v1 | Title Generative Adversarial Perturbations Summary paper propose novel generative model creating adversarial example slightly perturbed image resembling natural image maliciously crafted fool pretrained model present trainable deep neural network transforming image adversarial perturbation proposed model produce imagea... | [0.008410746231675148, 0.05454821512103081, -0.03014814667403698, 0.049116428941488266, -0.024745067581534386, -0.013837946578860283, 0.023260800167918205, -0.024695586413145065, -0.037630245089530945, -0.0020487233996391296, -0.014552706852555275, 0.057706501334905624, -0.012826532125473022, 0.026701834052801132, 0.06... |
231 | 231 | ['Logan Engstrom', 'Brandon Tran', 'Dimitris Tsipras', 'Ludwig Schmidt', 'Aleksander Madry'] | 1712.02779v3 | We show that simple transformations, namely translations and rotations alone,
are sufficient to fool neural network-based vision models on a significant
fraction of inputs. This is in sharp contrast to previous work that relied on
more complicated optimization approaches that are unlikely to appear outside of
a truly a... | A Rotation and a Translation Suffice: Fooling CNNs with Simple
Transformations | 2,017 | http://arxiv.org/pdf/1712.02779v3 | Title Rotation Translation Suffice Fooling CNNs Simple Transformations Summary show simple transformation namely translation rotation alone sufficient fool neural networkbased vision model significant fraction input sharp contrast previous work relied complicated optimization approach unlikely appear outside truly adve... | [0.017787711694836617, 0.006879817694425583, -0.024337947368621826, 0.025793012231588364, -0.02040625736117363, 0.012200893834233284, 0.06180109456181526, 0.015304679051041603, -0.06062167510390282, -0.036799050867557526, -0.0036219065077602863, 0.06119444593787193, 0.0380178801715374, 0.016379501670598984, 0.055030435... |
232 | 232 | ['Boyang Deng', 'Junjie Yan', 'Dahua Lin'] | 1712.03351v1 | The quest for performant networks has been a significant force that drives
the advancements of deep learning in recent years. While rewarding, improving
network design has never been an easy journey. The large design space combined
with the tremendous cost required for network training poses a major obstacle
to this en... | Peephole: Predicting Network Performance Before Training | 2,017 | http://arxiv.org/pdf/1712.03351v1 | Title Peephole Predicting Network Performance Training Summary quest performant network significant force drive advancement deep learning recent year rewarding improving network design never easy journey large design space combined tremendous cost required network training pose major obstacle endeavor work propose new ... | [-0.03332606703042984, 0.054034698754549026, -0.0035848221741616726, 0.03292200341820717, 0.03933834657073021, -0.04411165416240692, 0.021326975896954536, 0.0023866964038461447, -0.033627405762672424, -0.038252171128988266, -0.02949833869934082, 0.009029288776218891, 0.017469746991991997, 0.0657353550195694, 0.05195369... |
233 | 233 | ['Abien Fred Agarap'] | 1712.03541v1 | Convolutional neural networks (CNNs) are similar to "ordinary" neural
networks in the sense that they are made up of hidden layers consisting of
neurons with "learnable" parameters. These neurons receive inputs, performs a
dot product, and then follows it with a non-linearity. The whole network
expresses the mapping be... | An Architecture Combining Convolutional Neural Network (CNN) and Support
Vector Machine (SVM) for Image Classification | 2,017 | http://arxiv.org/pdf/1712.03541v1 | Title Architecture Combining Convolutional Neural Network CNN Support Vector Machine SVM Image Classification Summary Convolutional neural network CNNs similar ordinary neural network sense made hidden layer consisting neuron learnable parameter neuron receive input performs dot product follows nonlinearity whole netwo... | [0.04178604111075401, 0.0010077209444716573, -0.018232231959700584, 0.07323478162288666, 6.074137854739092e-05, -0.007431807462126017, 0.059503935277462006, -0.007657527457922697, -0.03318963572382927, -0.013424609787762165, -0.03081115521490574, 0.043146539479494095, 0.007727096788585186, 0.06916404515504837, 0.053730... |
234 | 234 | ['Ekaba Bisong'] | 1712.08314v2 | Artifical Neural Networks are a particular class of learning systems modeled
after biological neural functions with an interesting penchant for Hebbian
learning, that is "neurons that wire together, fire together". However, unlike
their natural counterparts, artificial neural networks have a close and
stringent couplin... | Benchmarking Decoupled Neural Interfaces with Synthetic Gradients | 2,017 | http://arxiv.org/pdf/1712.08314v2 | Title Benchmarking Decoupled Neural Interfaces Synthetic Gradients Summary Artifical Neural Networks particular class learning system modeled biological neural function interesting penchant Hebbian learning neuron wire together fire together However unlike natural counterpart artificial neural network close stringent c... | [-0.03697482869029045, 0.05663694441318512, -0.02497054450213909, 0.029748650267720222, -0.012712767347693443, -0.03279449790716171, 0.08903144299983978, -0.0162457674741745, 0.020958641543984413, 0.002326270332559943, -0.049318134784698486, 0.04366159811615944, 0.03583143278956413, 0.025675173848867416, 0.015638167038... |
235 | 235 | ['Amin Fehri', 'Santiago Velasco-Forero', 'Fernand Meyer'] | 1802.07008v1 | Image segmentation is the process of partitioning an image into a set of
meaningful regions according to some criteria. Hierarchical segmentation has
emerged as a major trend in this regard as it favors the emergence of important
regions at different scales. On the other hand, many methods allow us to have
prior inform... | Segmentation hiérarchique faiblement supervisée | 2,018 | http://arxiv.org/pdf/1802.07008v1 | Title Segmentation hiérarchique faiblement supervisée Summary Image segmentation process partitioning image set meaningful region according criterion Hierarchical segmentation emerged major trend regard favor emergence important region different scale hand many method allow u prior information position structure intere... | [-0.006895685568451881, -0.011677310802042484, -0.013283872045576572, 0.05941111221909523, -0.05897188186645508, 0.004648934584110975, 0.019284600391983986, 0.018355773761868477, 0.00807526521384716, 0.01541792880743742, 0.008606784977018833, 0.02819819375872612, 0.04107086732983589, 0.0046898671425879, -0.020383853465... |
236 | 236 | ['Mark D. McDonnell'] | 1802.08530v1 | For fast and energy-efficient deployment of trained deep neural networks on
resource-constrained embedded hardware, each learned weight parameter should
ideally be represented and stored using a single bit. Error-rates usually
increase when this requirement is imposed. Here, we report large improvements
in error rates ... | Training wide residual networks for deployment using a single bit for
each weight | 2,018 | http://arxiv.org/pdf/1802.08530v1 | Title Training wide residual network deployment using single bit weight Summary fast energyefficient deployment trained deep neural network resourceconstrained embedded hardware learned weight parameter ideally represented stored using single bit Errorrates usually increase requirement imposed report large improvement ... | [-0.007621029857546091, 0.013809921219944954, -0.005200904794037342, 0.04447505995631218, 0.028674913570284843, -0.02496200241148472, 0.0607050396502018, -0.001097885426133871, -0.025637412443757057, 0.030242057517170906, -0.005632867105305195, 0.027116065844893456, -0.018702542409300804, 0.03824552521109581, 0.0272639... |
237 | 237 | ['Abien Fred Agarap'] | 1803.08375v1 | We introduce the use of rectified linear units (ReLU) as the classification
function in a deep neural network (DNN). Conventionally, ReLU is used as an
activation function in DNNs, with Softmax function as their classification
function. However, there have been several studies on using a classification
function other t... | Deep Learning using Rectified Linear Units (ReLU) | 2,018 | http://arxiv.org/pdf/1803.08375v1 | Title Deep Learning using Rectified Linear Units ReLU Summary introduce use rectified linear unit ReLU classification function deep neural network DNN Conventionally ReLU used activation function DNNs Softmax function classification function However several study using classification function Softmax study addition acc... | [-0.017851971089839935, -0.02014479786157608, -0.007213265169411898, 0.04017939046025276, 0.026483051478862762, -0.00466179521754384, 0.06296517699956894, -0.010629304684698582, -0.008163356222212315, -0.0031884177587926388, 0.005745685659348965, -0.008078278973698616, -0.009909066371619701, 0.07852396368980408, 0.0115... |
238 | 238 | ['Djork-Arné Clevert', 'Andreas Mayr', 'Thomas Unterthiner', 'Sepp Hochreiter'] | 1502.06464v2 | We propose rectified factor networks (RFNs) to efficiently construct very
sparse, non-linear, high-dimensional representations of the input. RFN models
identify rare and small events in the input, have a low interference between
code units, have a small reconstruction error, and explain the data covariance
structure. R... | Rectified Factor Networks | 2,015 | http://arxiv.org/pdf/1502.06464v2 | Title Rectified Factor Networks Summary propose rectified factor network RFNs efficiently construct sparse nonlinear highdimensional representation input RFN model identify rare small event input low interference code unit small reconstruction error explain data covariance structure RFN learning generalized alternating... | [-0.046232495456933975, 0.013117842376232147, -0.02872827835381031, 0.03222096711397171, 0.050006262958049774, 0.0023814458400011063, 0.029582221060991287, 0.04177924618124962, -0.0456358976662159, 0.03354101628065109, 0.00545511906966567, 0.015595695935189724, 0.017880761995911598, 0.12493440508842468, -0.022241465747... |
239 | 239 | ['Qi Wang', 'Joseph JaJa'] | 1312.1909v1 | Motivated by an important insight from neural science, we propose a new
framework for understanding the success of the recently proposed "maxout"
networks. The framework is based on encoding information on sparse pathways and
recognizing the correct pathway at inference time. Elaborating further on this
insight, we pro... | From Maxout to Channel-Out: Encoding Information on Sparse Pathways | 2,013 | http://arxiv.org/pdf/1312.1909v1 | Title Maxout ChannelOut Encoding Information Sparse Pathways Summary Motivated important insight neural science propose new framework understanding success recently proposed maxout network framework based encoding information sparse pathway recognizing correct pathway inference time Elaborating insight propose novel de... | [-0.003104336326941848, 0.007492174860090017, 0.005086570046842098, 0.03221803531050682, 0.019866492599248886, -0.014069817960262299, 0.01960849016904831, 0.008129559457302094, -0.0948764905333519, 0.01597270369529724, -0.014996765181422234, 0.024849604815244675, -0.02412574365735054, 0.0745856985449791, 0.008558634668... |
240 | 240 | ['Takashi Shinozaki', 'Yasushi Naruse'] | 1312.5845v7 | We propose a novel learning method for multilayered neural networks which
uses feedforward supervisory signal and associates classification of a new
input with that of pre-trained input. The proposed method effectively uses rich
input information in the earlier layer for robust leaning and revising internal
representat... | Competitive Learning with Feedforward Supervisory Signal for Pre-trained
Multilayered Networks | 2,013 | http://arxiv.org/pdf/1312.5845v7 | Title Competitive Learning Feedforward Supervisory Signal Pretrained Multilayered Networks Summary propose novel learning method multilayered neural network us feedforward supervisory signal associate classification new input pretrained input proposed method effectively us rich input information earlier layer robust le... | [-0.04009803384542465, 0.009164806455373764, -0.011097167618572712, -0.0011576570104807615, 0.014742846600711346, 0.006361402105540037, 0.035141341388225555, -0.01559604611247778, -0.006365400273352861, -0.01850414089858532, -0.03009050153195858, 0.004498535301536322, -0.019216449931263924, 0.01816696859896183, 0.01619... |
241 | 241 | ['Chen-Yu Lee', 'Saining Xie', 'Patrick Gallagher', 'Zhengyou Zhang', 'Zhuowen Tu'] | 1409.5185v2 | Our proposed deeply-supervised nets (DSN) method simultaneously minimizes
classification error while making the learning process of hidden layers direct
and transparent. We make an attempt to boost the classification performance by
studying a new formulation in deep networks. Three aspects in convolutional
neural netwo... | Deeply-Supervised Nets | 2,014 | http://arxiv.org/pdf/1409.5185v2 | Title DeeplySupervised Nets Summary proposed deeplysupervised net DSN method simultaneously minimizes classification error making learning process hidden layer direct transparent make attempt boost classification performance studying new formulation deep network Three aspect convolutional neural network CNN style archi... | [-0.009686863049864769, 0.0631462037563324, -0.02084679901599884, 0.06011141091585159, 0.02666478045284748, -0.025617189705371857, 0.03203669562935829, -0.014180340804159641, -0.014078167267143726, 0.010133367963135242, -0.021181421354413033, 0.033930275589227676, -0.015635931864380836, 0.07227247953414917, -0.01105375... |
242 | 242 | ['Behnam Neyshabur', 'Ruslan Salakhutdinov', 'Nathan Srebro'] | 1506.02617v1 | We revisit the choice of SGD for training deep neural networks by
reconsidering the appropriate geometry in which to optimize the weights. We
argue for a geometry invariant to rescaling of weights that does not affect the
output of the network, and suggest Path-SGD, which is an approximate steepest
descent method with ... | Path-SGD: Path-Normalized Optimization in Deep Neural Networks | 2,015 | http://arxiv.org/pdf/1506.02617v1 | Title PathSGD PathNormalized Optimization Deep Neural Networks Summary revisit choice SGD training deep neural network reconsidering appropriate geometry optimize weight argue geometry invariant rescaling weight affect output network suggest PathSGD approximate steepest descent method respect pathwise regularizer relat... | [-0.02088894322514534, 0.03726065903902054, -0.01929231360554695, 0.04762108996510506, 0.017126496881246567, -0.037848442792892456, 0.01982071064412594, -0.011975996196269989, -0.031421370804309845, 0.03843139484524727, 0.02079521119594574, 0.01623847894370556, -0.02488737367093563, 0.015691833570599556, 0.030969014391... |
243 | 243 | ['Alan Mosca', 'George D. Magoulas'] | 1509.04612v2 | The Resilient Propagation (Rprop) algorithm has been very popular for
backpropagation training of multilayer feed-forward neural networks in various
applications. The standard Rprop however encounters difficulties in the context
of deep neural networks as typically happens with gradient-based learning
algorithms. In th... | Adapting Resilient Propagation for Deep Learning | 2,015 | http://arxiv.org/pdf/1509.04612v2 | Title Adapting Resilient Propagation Deep Learning Summary Resilient Propagation Rprop algorithm popular backpropagation training multilayer feedforward neural network various application standard Rprop however encounter difficulty context deep neural network typically happens gradientbased learning algorithm paper pro... | [-0.016168491914868355, 0.019840942695736885, -0.029285850003361702, 0.01610058918595314, 5.419364242698066e-05, -0.03379685431718826, -0.0014425793197005987, -0.01248942967504263, -0.06541429460048676, 0.00048020516987890005, 0.03383493795990944, 0.019835278391838074, 0.02872784622013569, 0.0385395772755146, 0.0108206... |
244 | 244 | ['Nastaran Mohammadian Rad', 'Andrea Bizzego', 'Seyed Mostafa Kia', 'Giuseppe Jurman', 'Paola Venuti', 'Cesare Furlanello'] | 1511.01865v3 | Autism Spectrum Disorders (ASDs) are often associated with specific atypical
postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have
a specific visibility. While the identification and the quantification of SMM
patterns remain complex, its automation would provide support to accurate
tuning of t... | Convolutional Neural Network for Stereotypical Motor Movement Detection
in Autism | 2,015 | http://arxiv.org/pdf/1511.01865v3 | Title Convolutional Neural Network Stereotypical Motor Movement Detection Autism Summary Autism Spectrum Disorders ASDs often associated specific atypical postural motor behavior Stereotypical Motor Movements SMMs specific visibility identification quantification SMM pattern remain complex automation would provide supp... | [-0.0202474445104599, 0.0006815855740569532, -0.04242274537682533, 0.05192091315984726, 0.06507916748523712, 0.019127126783132553, 0.02270711585879326, -0.017676804214715958, -0.054764408618211746, -0.029056217521429062, 0.018322328105568886, -0.011970349587500095, 0.025815501809120178, 0.06343507766723633, -0.00378999... |
245 | 245 | ['Sasha Targ', 'Diogo Almeida', 'Kevin Lyman'] | 1603.08029v1 | Residual networks (ResNets) have recently achieved state-of-the-art on
challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep
dual-stream architecture that generalizes ResNets and standard CNNs and is
easily implemented with no computational overhead. RiR consistently improves
performance over R... | Resnet in Resnet: Generalizing Residual Architectures | 2,016 | http://arxiv.org/pdf/1603.08029v1 | Title Resnet Resnet Generalizing Residual Architectures Summary Residual network ResNets recently achieved stateoftheart challenging computer vision task introduce Resnet Resnet RiR deep dualstream architecture generalizes ResNets standard CNNs easily implemented computational overhead RiR consistently improves perform... | [-0.023636987432837486, 0.03913691267371178, -0.018584342673420906, 0.06586231291294098, -0.008258814923465252, 0.0023474704939872026, 0.003197903512045741, -0.003969330340623856, -0.05937030166387558, 0.02191881462931633, 0.022512001916766167, 0.0013895828742533922, 0.0011546164751052856, 0.02555547095835209, 0.003185... |
246 | 246 | ['Mohammad Javad Shafiee', 'Alexander Wong'] | 1609.01360v2 | There has been significant recent interest towards achieving highly efficient
deep neural network architectures. A promising paradigm for achieving this is
the concept of evolutionary deep intelligence, which attempts to mimic
biological evolution processes to synthesize highly-efficient deep neural
networks over succe... | Evolutionary Synthesis of Deep Neural Networks via Synaptic
Cluster-driven Genetic Encoding | 2,016 | http://arxiv.org/pdf/1609.01360v2 | Title Evolutionary Synthesis Deep Neural Networks via Synaptic Clusterdriven Genetic Encoding Summary significant recent interest towards achieving highly efficient deep neural network architecture promising paradigm achieving concept evolutionary deep intelligence attempt mimic biological evolution process synthesize ... | [-0.04819837585091591, 0.023699864745140076, -0.05558415502309799, 0.017693307250738144, 0.007717323023825884, 0.010449408553540707, 0.0286715030670166, 0.011082939803600311, -0.016567587852478027, 0.03959182649850845, -0.029942557215690613, 0.009146512486040592, -0.020443174988031387, 0.03525388240814209, 0.0398170538... |
247 | 247 | ['Andrew Brock', 'Theodore Lim', 'J. M. Ritchie', 'Nick Weston'] | 1609.07093v3 | The increasingly photorealistic sample quality of generative image models
suggests their feasibility in applications beyond image generation. We present
the Neural Photo Editor, an interface that leverages the power of generative
neural networks to make large, semantically coherent changes to existing
images. To tackle... | Neural Photo Editing with Introspective Adversarial Networks | 2,016 | http://arxiv.org/pdf/1609.07093v3 | Title Neural Photo Editing Introspective Adversarial Networks Summary increasingly photorealistic sample quality generative image model suggests feasibility application beyond image generation present Neural Photo Editor interface leverage power generative neural network make large semantically coherent change existing... | [0.016065798699855804, 0.09007365256547928, 0.015057975426316261, 0.026203274726867676, 0.00218421733006835, -0.05233510583639145, 0.018378846347332, -0.02055145613849163, -0.07738371193408966, 0.00926798302680254, 0.03939371556043625, -0.024948840960860252, 0.01631634496152401, 0.01621488854289055, 0.07300959527492523... |
248 | 248 | ['Tolga Bolukbasi', 'Joseph Wang', 'Ofer Dekel', 'Venkatesh Saligrama'] | 1702.07811v2 | We present an approach to adaptively utilize deep neural networks in order to
reduce the evaluation time on new examples without loss of accuracy. Rather
than attempting to redesign or approximate existing networks, we propose two
schemes that adaptively utilize networks. We first pose an adaptive network
evaluation sc... | Adaptive Neural Networks for Efficient Inference | 2,017 | http://arxiv.org/pdf/1702.07811v2 | Title Adaptive Neural Networks Efficient Inference Summary present approach adaptively utilize deep neural network order reduce evaluation time new example without loss accuracy Rather attempting redesign approximate existing network propose two scheme adaptively utilize network first pose adaptive network evaluation s... | [-0.010655594058334827, 0.07083895057439804, -0.02189578115940094, 0.040964525192976, 0.016899876296520233, 0.003200060222297907, 0.08283265680074692, 0.020197181031107903, 0.009742124937474728, -0.023342343047261238, -0.02673979662358761, 0.03044191189110279, 0.019908875226974487, 0.0055004204623401165, -0.00521785300... |
249 | 249 | ['Zhengyang Wang', 'Hao Yuan', 'Shuiwang Ji'] | 1705.06821v1 | The key idea of variational auto-encoders (VAEs) resembles that of
traditional auto-encoder models in which spatial information is supposed to be
explicitly encoded in the latent space. However, the latent variables in VAEs
are vectors, which are commonly interpreted as multiple feature maps of size
1x1. Such represent... | Spatial Variational Auto-Encoding via Matrix-Variate Normal
Distributions | 2,017 | http://arxiv.org/pdf/1705.06821v1 | Title Spatial Variational AutoEncoding via MatrixVariate Normal Distributions Summary key idea variational autoencoders VAEs resembles traditional autoencoder model spatial information supposed explicitly encoded latent space However latent variable VAEs vector commonly interpreted multiple feature map size 1x1 represe... | [-0.02362840436398983, 0.03753163293004036, -0.016020672395825386, 0.003944622352719307, -0.008386004716157913, 0.0237281396985054, 0.019690774381160736, -0.05245620012283325, -0.06290556490421295, 0.030429089441895485, -0.014068732969462872, 0.04389841482043266, 0.030763179063796997, 0.09631757438182831, 0.05527981743... |
250 | 250 | ['Jun Li', 'Yongjun Chen', 'Lei Cai', 'Ian Davidson', 'Shuiwang Ji'] | 1705.08881v2 | The key idea of current deep learning methods for dense prediction is to
apply a model on a regular patch centered on each pixel to make pixel-wise
predictions. These methods are limited in the sense that the patches are
determined by network architecture instead of learned from data. In this work,
we propose the dense... | Dense Transformer Networks | 2,017 | http://arxiv.org/pdf/1705.08881v2 | Title Dense Transformer Networks Summary key idea current deep learning method dense prediction apply model regular patch centered pixel make pixelwise prediction method limited sense patch determined network architecture instead learned data work propose dense transformer network learn shape size patch data dense tran... | [-0.019880279898643494, 0.033221058547496796, 0.0034778385888785124, 0.018752625212073326, 0.01973993517458439, -0.04496455937623978, 0.022053755819797516, -0.01864602044224739, -0.06360097229480743, 0.0651354119181633, 0.0012996755540370941, 0.031202536076307297, 0.02019202895462513, 0.08069653064012527, 0.01318674813... |
251 | 251 | ['Saikat Chatterjee', 'Alireza M. Javid', 'Mostafa Sadeghi', 'Partha P. Mitra', 'Mikael Skoglund'] | 1710.08177v1 | We develop an algorithm for systematic design of a large artificial neural
network using a progression property. We find that some non-linear functions,
such as the rectifier linear unit and its derivatives, hold the property. The
systematic design addresses the choice of network size and regularization of
parameters. ... | Progressive Learning for Systematic Design of Large Neural Networks | 2,017 | http://arxiv.org/pdf/1710.08177v1 | Title Progressive Learning Systematic Design Large Neural Networks Summary develop algorithm systematic design large artificial neural network using progression property find nonlinear function rectifier linear unit derivative hold property systematic design address choice network size regularization parameter number n... | [-0.007100499235093594, 0.07661841809749603, -0.014514010399580002, -0.022471878677606583, 0.006745586637407541, -0.035336531698703766, 0.032387640327215195, 0.004059568513184786, -0.03514965623617172, 0.020913951098918915, 0.02031024731695652, 0.04010375961661339, 0.03836868703365326, 0.037399277091026306, 0.014155317... |
252 | 252 | ['Shibani Santurkar', 'Ludwig Schmidt', 'Aleksander Mądry'] | 1711.00970v3 | A fundamental, and still largely unanswered, question in the context of
Generative Adversarial Networks (GANs) is whether GANs are actually able to
capture the key characteristics of the datasets they are trained on. The
current approaches to examining this issue require significant human
supervision, such as visual in... | A Classification-Based Perspective on GAN Distributions | 2,017 | http://arxiv.org/pdf/1711.00970v3 | Title ClassificationBased Perspective GAN Distributions Summary fundamental still largely unanswered question context Generative Adversarial Networks GANs whether GANs actually able capture key characteristic datasets trained current approach examining issue require significant human supervision visual inspection sampl... | [0.0021763378754258156, 0.08285621553659439, -0.018773389980196953, 0.022689875215291977, 0.017496628686785698, -0.018213102594017982, 0.02479293756186962, -0.008787611499428749, -0.05918903276324272, 0.026047758758068085, -0.026144003495573997, -0.00343594909645617, -0.003952491097152233, 0.014672715216875076, 0.07179... |
253 | 253 | ['Ethan Perez', 'Harm de Vries', 'Florian Strub', 'Vincent Dumoulin', 'Aaron Courville'] | 1707.03017v5 | Achieving artificial visual reasoning - the ability to answer image-related
questions which require a multi-step, high-level process - is an important step
towards artificial general intelligence. This multi-modal task requires
learning a question-dependent, structured reasoning process over images from
language. Stand... | Learning Visual Reasoning Without Strong Priors | 2,017 | http://arxiv.org/pdf/1707.03017v5 | Title Learning Visual Reasoning Without Strong Priors Summary Achieving artificial visual reasoning ability answer imagerelated question require multistep highlevel process important step towards artificial general intelligence multimodal task requires learning questiondependent structured reasoning process image langu... | [0.007242904976010323, 0.037822578102350235, -0.0016457148594781756, 0.03268323093652725, -0.01724429428577423, 0.04981609061360359, 0.03430554270744324, 0.0010658128885552287, 0.019291743636131287, -0.017107194289565086, -0.0006256354390643537, 0.02180442027747631, -0.00687773572281003, 0.06499188393354416, 0.02795973... |
254 | 254 | ['Jieyu Zhao', 'Tianlu Wang', 'Mark Yatskar', 'Vicente Ordonez', 'Kai-Wei Chang'] | 1707.09457v1 | Language is increasingly being used to define rich visual recognition
problems with supporting image collections sourced from the web. Structured
prediction models are used in these tasks to take advantage of correlations
between co-occurring labels and visual input but risk inadvertently encoding
social biases found i... | Men Also Like Shopping: Reducing Gender Bias Amplification using
Corpus-level Constraints | 2,017 | http://arxiv.org/pdf/1707.09457v1 | Title Men Also Like Shopping Reducing Gender Bias Amplification using Corpuslevel Constraints Summary Language increasingly used define rich visual recognition problem supporting image collection sourced web Structured prediction model used task take advantage correlation cooccurring label visual input risk inadvertent... | [0.06009983643889427, 0.07471739500761032, -0.027074970304965973, -0.010839974507689476, 0.028910618275403976, 0.056949615478515625, 0.07157381623983383, -0.00790663156658411, 0.015195939689874649, -0.09310241043567657, 0.0076153394766151905, -0.01762606017291546, 0.028808385133743286, 0.02023264765739441, 0.0086589902... |
255 | 255 | ['Guillem Collell', 'Luc Van Gool', 'Marie-Francine Moens'] | 1711.06821v2 | Spatial understanding is a fundamental problem with wide-reaching real-world
applications. The representation of spatial knowledge is often modeled with
spatial templates, i.e., regions of acceptability of two objects under an
explicit spatial relationship (e.g., "on", "below", etc.). In contrast with
prior work that r... | Acquiring Common Sense Spatial Knowledge through Implicit Spatial
Templates | 2,017 | http://arxiv.org/pdf/1711.06821v2 | Title Acquiring Common Sense Spatial Knowledge Implicit Spatial Templates Summary Spatial understanding fundamental problem widereaching realworld application representation spatial knowledge often modeled spatial template ie region acceptability two object explicit spatial relationship eg etc contrast prior work restr... | [0.0390472486615181, 0.05449872091412544, -0.01978192664682865, 0.0235537551343441, -0.015112494118511677, 0.026040440425276756, 0.02543070539832115, -0.03768523409962654, -0.0028930793050676584, -0.043474871665239334, -0.021396949887275696, 0.044973064213991165, 0.0420638769865036, 0.05469481647014618, 0.0506098046898... |
256 | 256 | ['Ethan Perez', 'Florian Strub', 'Harm de Vries', 'Vincent Dumoulin', 'Aaron Courville'] | 1709.07871v2 | We introduce a general-purpose conditioning method for neural networks called
FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network
computation via a simple, feature-wise affine transformation based on
conditioning information. We show that FiLM layers are highly effective for
visual reasoning - an... | FiLM: Visual Reasoning with a General Conditioning Layer | 2,017 | http://arxiv.org/pdf/1709.07871v2 | Title FiLM Visual Reasoning General Conditioning Layer Summary introduce generalpurpose conditioning method neural network called FiLM Featurewise Linear Modulation FiLM layer influence neural network computation via simple featurewise affine transformation based conditioning information show FiLM layer highly effectiv... | [-0.020262116566300392, 0.026702584698796272, -0.0076394337229430676, 0.020431069657206535, 0.0009277117205783725, -0.03338836506009102, 0.07610216736793518, -0.00010991925955750048, -0.07093799859285355, 0.004698887001723051, 0.0132906474173069, -0.0012317874934524298, 0.02308536134660244, 0.07724828273057938, -0.0047... |
257 | 257 | ['Ivan Titov', 'Ehsan Khoddam'] | 1412.2812v1 | We introduce a new approach to unsupervised estimation of feature-rich
semantic role labeling models. Our model consists of two components: (1) an
encoding component: a semantic role labeling model which predicts roles given a
rich set of syntactic and lexical features; (2) a reconstruction component: a
tensor factoriz... | Unsupervised Induction of Semantic Roles within a Reconstruction-Error
Minimization Framework | 2,014 | http://arxiv.org/pdf/1412.2812v1 | Title Unsupervised Induction Semantic Roles within ReconstructionError Minimization Framework Summary introduce new approach unsupervised estimation featurerich semantic role labeling model model consists two component 1 encoding component semantic role labeling model predicts role given rich set syntactic lexical feat... | [0.04606027156114578, 0.030625415965914726, -0.01615452580153942, 0.07971205562353134, -0.030639758333563805, 0.01852208562195301, -0.02940760925412178, -0.02171035297214985, -0.03535516560077667, -0.07344570010900497, -0.006927483715116978, -0.0360574871301651, -0.00732341967523098, 0.021620342507958412, -0.0053366902... |
258 | 258 | ['Tolga Bolukbasi', 'Kai-Wei Chang', 'James Zou', 'Venkatesh Saligrama', 'Adam Kalai'] | 1607.06520v1 | The blind application of machine learning runs the risk of amplifying biases
present in data. Such a danger is facing us with word embedding, a popular
framework to represent text data as vectors which has been used in many machine
learning and natural language processing tasks. We show that even word
embeddings traine... | Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word
Embeddings | 2,016 | http://arxiv.org/pdf/1607.06520v1 | Title Man Computer Programmer Woman Homemaker Debiasing Word Embeddings Summary blind application machine learning run risk amplifying bias present data danger facing u word embedding popular framework represent text data vector used many machine learning natural language processing task show even word embeddings train... | [0.044939227402210236, 0.07646720111370087, -0.02984785847365856, 0.019330870360136032, 0.0043589952401816845, 0.020647117868065834, 0.034630268812179565, -0.02673092857003212, 0.02662590891122818, -0.046371929347515106, 0.04187776893377304, 0.0023317565210163593, 0.06847120821475983, 0.012187430635094643, 0.0187245048... |
259 | 259 | ['Adji B. Dieng', 'Chong Wang', 'Jianfeng Gao', 'John Paisley'] | 1611.01702v2 | In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based
language model designed to directly capture the global semantic meaning
relating words in a document via latent topics. Because of their sequential
nature, RNNs are good at capturing the local structure of a word sequence -
both semantic and syn... | TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency | 2,016 | http://arxiv.org/pdf/1611.01702v2 | Title TopicRNN Recurrent Neural Network LongRange Semantic Dependency Summary paper propose TopicRNN recurrent neural network RNNbased language model designed directly capture global semantic meaning relating word document via latent topic sequential nature RNNs good capturing local structure word sequence semantic syn... | [0.039330221712589264, 0.03902608901262283, 0.005862101446837187, 0.07468004524707794, -0.04729980230331421, -0.017305465415120125, -0.018174050375819206, -0.0029418659396469593, -0.039891425520181656, -0.05232084542512894, -0.0013909466797485948, -0.00482860067859292, 0.02484303154051304, 0.03141802176833153, -0.02759... |
260 | 260 | ['Liwen Zhang', 'John Winn', 'Ryota Tomioka'] | 1611.02266v2 | We propose the Gaussian attention model for content-based neural memory
access. With the proposed attention model, a neural network has the additional
degree of freedom to control the focus of its attention from a laser sharp
attention to a broad attention. It is applicable whenever we can assume that
the distance in t... | Gaussian Attention Model and Its Application to Knowledge Base Embedding
and Question Answering | 2,016 | http://arxiv.org/pdf/1611.02266v2 | Title Gaussian Attention Model Application Knowledge Base Embedding Question Answering Summary propose Gaussian attention model contentbased neural memory access proposed attention model neural network additional degree freedom control focus attention laser sharp attention broad attention applicable whenever assume dis... | [0.04573822021484375, 0.021794361993670464, -8.420786798524205e-06, 0.05959530919790268, 0.01120956614613533, 0.0024439350236207247, -0.02130891941487789, -0.012029629200696945, -0.016816748306155205, -0.056387417018413544, 0.042499374598264694, 0.006019329186528921, -0.014441725797951221, 0.04128998890519142, 0.034159... |
261 | 261 | ['Yacine Jernite', 'Edouard Grave', 'Armand Joulin', 'Tomas Mikolov'] | 1611.06188v2 | Recurrent neural networks (RNNs) have been used extensively and with
increasing success to model various types of sequential data. Much of this
progress has been achieved through devising recurrent units and architectures
with the flexibility to capture complex statistics in the data, such as long
range dependency or l... | Variable Computation in Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1611.06188v2 | Title Variable Computation Recurrent Neural Networks Summary Recurrent neural network RNNs used extensively increasing success model various type sequential data Much progress achieved devising recurrent unit architecture flexibility capture complex statistic data long range dependency localized attention phenomenon Ho... | [0.024442000314593315, 0.02478458732366562, 0.0005417542415671051, 0.04417232424020767, -0.0052021867595613, -0.016433410346508026, 0.03130257874727249, -0.02177743799984455, -0.04024537280201912, -0.01409372128546238, 0.008027873933315277, -0.03966596722602844, 0.045676253736019135, 0.061685752123594284, 0.01568374037... |
262 | 262 | ['Mostafa Dehghani', 'Aliaksei Severyn', 'Sascha Rothe', 'Jaap Kamps'] | 1711.11383v1 | In this paper, we propose a method for training neural networks when we have
a large set of data with weak labels and a small amount of data with true
labels. In our proposed model, we train two neural networks: a target network,
the learner and a confidence network, the meta-learner. The target network is
optimized to... | Learning to Learn from Weak Supervision by Full Supervision | 2,017 | http://arxiv.org/pdf/1711.11383v1 | Title Learning Learn Weak Supervision Full Supervision Summary paper propose method training neural network large set data weak label small amount data true label proposed model train two neural network target network learner confidence network metalearner target network optimized perform given task trained using large... | [0.016199544072151184, 0.008898280560970306, -0.0032587472815066576, 0.010760168544948101, 0.01855023205280304, -0.011139057576656342, 0.03655419871211052, -0.025033380836248398, 0.0042014648206532, 0.010688869282603264, -0.06303218752145767, 0.0256331916898489, -0.014091835357248783, 0.023681145161390305, 0.0366879180... |
263 | 263 | ['Garrett B. Goh', 'Nathan O. Hodas', 'Charles Siegel', 'Abhinav Vishnu'] | 1712.02034v2 | Chemical databases store information in text representations, and the SMILES
format is a universal standard used in many cheminformatics software. Encoded
in each SMILES string is structural information that can be used to predict
complex chemical properties. In this work, we develop SMILES2vec, a deep RNN
that automat... | SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for
Predicting Chemical Properties | 2,017 | http://arxiv.org/pdf/1712.02034v2 | Title SMILES2Vec Interpretable GeneralPurpose Deep Neural Network Predicting Chemical Properties Summary Chemical database store information text representation SMILES format universal standard used many cheminformatics software Encoded SMILES string structural information used predict complex chemical property work de... | [-0.0056990343146026134, 0.05438602343201637, -0.007343196775764227, 0.008709163405001163, 0.026958413422107697, -0.030311936512589455, 0.012894507497549057, -0.0001822587801143527, 0.09065743535757065, 0.030944425612688065, 0.026158733293414116, 0.02760491706430912, -0.022660505026578903, 0.0733989030122757, 0.0333320... |
264 | 264 | ['Gellért Weisz', 'Paweł Budzianowski', 'Pei-Hao Su', 'Milica Gašić'] | 1802.03753v1 | In spoken dialogue systems, we aim to deploy artificial intelligence to build
automated dialogue agents that can converse with humans. A part of this effort
is the policy optimisation task, which attempts to find a policy describing how
to respond to humans, in the form of a function taking the current state of the
dia... | Sample Efficient Deep Reinforcement Learning for Dialogue Systems with
Large Action Spaces | 2,018 | http://arxiv.org/pdf/1802.03753v1 | Title Sample Efficient Deep Reinforcement Learning Dialogue Systems Large Action Spaces Summary spoken dialogue system aim deploy artificial intelligence build automated dialogue agent converse human part effort policy optimisation task attempt find policy describing respond human form function taking current state dia... | [0.05258924514055252, 0.025957388803362846, -0.007112140301615, 0.03299039974808693, 0.010600725188851357, 0.018305521458387375, 0.01092015765607357, -0.007585285231471062, 0.00495365634560585, -0.03693164512515068, -0.041278038173913956, -0.018762703984975815, -0.02209930121898651, 0.08973554521799088, 0.0083509301766... |
265 | 265 | ['M. Andrecut'] | 1802.09914v1 | In this paper we explore the "vector semantics" problem from the perspective
of "almost orthogonal" property of high-dimensional random vectors. We show
that this intriguing property can be used to "memorize" random vectors by
simply adding them, and we provide an efficient probabilistic solution to the
set membership ... | High-Dimensional Vector Semantics | 2,018 | http://arxiv.org/pdf/1802.09914v1 | Title HighDimensional Vector Semantics Summary paper explore vector semantics problem perspective almost orthogonal property highdimensional random vector show intriguing property used memorize random vector simply adding provide efficient probabilistic solution set membership problem Also discus several application wo... | [0.013657531701028347, 0.033309757709503174, 0.00046756744268350303, 0.05040276050567627, -0.02798558957874775, 0.0247584767639637, 0.0012990129180252552, 0.016715247184038162, -0.04514794796705246, -0.06318659335374832, 0.025398975238204002, 0.00190466339699924, -0.007011611945927143, -0.006851550191640854, 0.00626556... |
266 | 266 | ['Ashutosh Modi', 'Ivan Titov'] | 1312.5198v4 | Induction of common sense knowledge about prototypical sequences of events
has recently received much attention. Instead of inducing this knowledge in the
form of graphs, as in much of the previous work, in our method, distributed
representations of event realizations are computed based on distributed
representations o... | Learning Semantic Script Knowledge with Event Embeddings | 2,013 | http://arxiv.org/pdf/1312.5198v4 | Title Learning Semantic Script Knowledge Event Embeddings Summary Induction common sense knowledge prototypical sequence event recently received much attention Instead inducing knowledge form graph much previous work method distributed representation event realization computed based distributed representation predicate... | [0.014707188121974468, -0.023915736004710197, 0.013688293285667896, 0.07964979857206345, -0.02585529536008835, 0.004732577595859766, -0.030672112479805946, 0.012466762214899063, 0.07346168160438538, -0.06675880402326584, 0.052225515246391296, 0.0369463786482811, 0.009310407564043999, 0.10182614624500275, 0.020551547408... |
267 | 267 | ['Andrew S. Lan', 'Divyanshu Vats', 'Andrew E. Waters', 'Richard G. Baraniuk'] | 1501.04346v1 | While computer and communication technologies have provided effective means
to scale up many aspects of education, the submission and grading of
assessments such as homework assignments and tests remains a weak link. In this
paper, we study the problem of automatically grading the kinds of open response
mathematical qu... | Mathematical Language Processing: Automatic Grading and Feedback for
Open Response Mathematical Questions | 2,015 | http://arxiv.org/pdf/1501.04346v1 | Title Mathematical Language Processing Automatic Grading Feedback Open Response Mathematical Questions Summary computer communication technology provided effective mean scale many aspect education submission grading assessment homework assignment test remains weak link paper study problem automatically grading kind ope... | [0.0008942689746618271, -0.012581785209476948, -0.051359623670578, 0.012524507008492947, 0.013330218382179737, 0.012910638935863972, 0.03785356134176254, 0.01906597800552845, 0.007265756372362375, -0.06543463468551636, -0.006016953848302364, 0.05648420751094818, 0.012943807989358902, 0.07430504262447357, -0.00021407756... |
268 | 268 | ['Tadahiro Taniguchi', 'Ryo Nakashima', 'Shogo Nagasaka'] | 1506.06646v2 | Human infants can discover words directly from unsegmented speech signals
without any explicitly labeled data. In this paper, we develop a novel machine
learning method called nonparametric Bayesian double articulation analyzer
(NPB-DAA) that can directly acquire language and acoustic models from observed
continuous sp... | Nonparametric Bayesian Double Articulation Analyzer for Direct Language
Acquisition from Continuous Speech Signals | 2,015 | http://arxiv.org/pdf/1506.06646v2 | Title Nonparametric Bayesian Double Articulation Analyzer Direct Language Acquisition Continuous Speech Signals Summary Human infant discover word directly unsegmented speech signal without explicitly labeled data paper develop novel machine learning method called nonparametric Bayesian double articulation analyzer NPB... | [-0.021494179964065552, 0.0935753658413887, -0.003326444188132882, -0.005437909159809351, -0.004162050783634186, 0.05506052076816559, 0.05543316528201103, 0.009482024237513542, -0.03410143777728081, -0.019834361970424652, -0.029655329883098602, -0.017773011699318886, 0.10199090093374252, 0.019770469516515732, 0.0108447... |
269 | 269 | ['Zhiting Hu', 'Xuezhe Ma', 'Zhengzhong Liu', 'Eduard Hovy', 'Eric Xing'] | 1603.06318v4 | Combining deep neural networks with structured logic rules is desirable to
harness flexibility and reduce uninterpretability of the neural models. We
propose a general framework capable of enhancing various types of neural
networks (e.g., CNNs and RNNs) with declarative first-order logic rules.
Specifically, we develop... | Harnessing Deep Neural Networks with Logic Rules | 2,016 | http://arxiv.org/pdf/1603.06318v4 | Title Harnessing Deep Neural Networks Logic Rules Summary Combining deep neural network structured logic rule desirable harness flexibility reduce uninterpretability neural model propose general framework capable enhancing various type neural network eg CNNs RNNs declarative firstorder logic rule Specifically develop i... | [0.033003341406583786, 0.07190505415201187, 0.010013608261942863, 0.056163910776376724, -0.04210459440946579, -0.0017273036064580083, -0.03845634311437607, 0.023358173668384552, 0.025619158521294594, -0.03538328781723976, -0.00015137832087930292, 0.009299815632402897, -0.017805354669690132, 0.055462513118982315, -0.041... |
270 | 270 | ['Zhiting Hu', 'Zichao Yang', 'Xiaodan Liang', 'Ruslan Salakhutdinov', 'Eric P. Xing'] | 1703.00955v3 | Generic generation and manipulation of text is challenging and has limited
success compared to recent deep generative modeling in visual domain. This
paper aims at generating plausible natural language sentences, whose attributes
are dynamically controlled by learning disentangled latent representations with
designated... | Toward Controlled Generation of Text | 2,017 | http://arxiv.org/pdf/1703.00955v3 | Title Toward Controlled Generation Text Summary Generic generation manipulation text challenging limited success compared recent deep generative modeling visual domain paper aim generating plausible natural language sentence whose attribute dynamically controlled learning disentangled latent representation designated s... | [0.041591014713048935, 0.049242641776800156, -0.02687974087893963, 0.017289403825998306, -0.020910058170557022, 0.0009405180462636054, 0.013665645383298397, -0.03590340167284012, -0.02819523774087429, -0.01796075887978077, 0.03623415157198906, -0.027817292138934135, -0.002657985780388117, 0.08267983794212341, 0.0315427... |
271 | 271 | ['Lianhui Qin', 'Zhisong Zhang', 'Hai Zhao', 'Zhiting Hu', 'Eric P. Xing'] | 1704.00217v1 | Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another ... | Adversarial Connective-exploiting Networks for Implicit Discourse
Relation Classification | 2,017 | http://arxiv.org/pdf/1704.00217v1 | Title Adversarial Connectiveexploiting Networks Implicit Discourse Relation Classification Summary Implicit discourse relation classification great challenge due lack connective strong linguistic cue motivates use annotated implicit connective improve recognition propose feature imitation framework implicit relation ne... | [0.05799024552106857, 0.04870559275150299, 0.001366019481793046, 0.07373739033937454, 0.00565086305141449, 0.004908737726509571, 0.01874467544257641, 0.010964848101139069, 0.016286183148622513, -0.06822407990694046, -0.02528931386768818, 0.026924440637230873, 0.01248467992991209, 0.008275067433714867, -0.02954594418406... |
272 | 272 | ['Maxim Rabinovich', 'Mitchell Stern', 'Dan Klein'] | 1704.07535v1 | Tasks like code generation and semantic parsing require mapping unstructured
(or partially structured) inputs to well-formed, executable outputs. We
introduce abstract syntax networks, a modeling framework for these problems.
The outputs are represented as abstract syntax trees (ASTs) and constructed by
a decoder with ... | Abstract Syntax Networks for Code Generation and Semantic Parsing | 2,017 | http://arxiv.org/pdf/1704.07535v1 | Title Abstract Syntax Networks Code Generation Semantic Parsing Summary Tasks like code generation semantic parsing require mapping unstructured partially structured input wellformed executable output introduce abstract syntax network modeling framework problem output represented abstract syntax tree ASTs constructed d... | [0.0036666709929704666, 0.03017038106918335, -0.044676270335912704, 0.04405936226248741, -0.030508369207382202, 0.012179131619632244, -0.027734987437725067, -0.0032433553133159876, -0.0009128220262937248, -0.03829493373632431, -0.005977168213576078, 0.0694354772567749, 0.005467509850859642, 0.0688248798251152, 0.004465... |
273 | 273 | ['Ben Athiwaratkun', 'Andrew Gordon Wilson'] | 1704.08424v1 | Word embeddings provide point representations of words containing useful
semantic information. We introduce multimodal word distributions formed from
Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty
information. To learn these distributions, we propose an energy-based
max-margin objective... | Multimodal Word Distributions | 2,017 | http://arxiv.org/pdf/1704.08424v1 | Title Multimodal Word Distributions Summary Word embeddings provide point representation word containing useful semantic information introduce multimodal word distribution formed Gaussian mixture multiple word meaning entailment rich uncertainty information learn distribution propose energybased maxmargin objective sho... | [0.014139062725007534, 0.06031809747219086, -0.00011749863915611058, 0.06753607839345932, -0.00906281266361475, 0.006855569314211607, -0.04722219705581665, -0.0014130757190287113, -0.03933229297399521, -0.053519390523433685, -0.0067403726279735565, -0.009325833059847355, 0.03750859573483467, 0.04316749796271324, 0.0529... |
274 | 274 | ['Brent Harrison', 'Upol Ehsan', 'Mark O. Riedl'] | 1707.08616v2 | In this work we present a technique to use natural language to help
reinforcement learning generalize to unseen environments. This technique uses
neural machine translation, specifically the use of encoder-decoder networks,
to learn associations between natural language behavior descriptions and
state-action informatio... | Guiding Reinforcement Learning Exploration Using Natural Language | 2,017 | http://arxiv.org/pdf/1707.08616v2 | Title Guiding Reinforcement Learning Exploration Using Natural Language Summary work present technique use natural language help reinforcement learning generalize unseen environment technique us neural machine translation specifically use encoderdecoder network learn association natural language behavior description st... | [0.06082983687520027, 0.014656160026788712, -0.011895433068275452, 0.02202833816409111, 0.005228497087955475, -0.004309078212827444, -0.026899201795458794, 0.007668856997042894, -0.026616493239998817, -0.03102383390069008, -0.04038817435503006, 0.020689476281404495, 0.0008628653013147414, 0.06435170024633408, 0.0113265... |
275 | 275 | ['Mo Yu', 'Xiaoxiao Guo', 'Jinfeng Yi', 'Shiyu Chang', 'Saloni Potdar', 'Gerald Tesauro', 'Haoyu Wang', 'Bowen Zhou'] | 1708.07918v1 | We investigate task clustering for deep-learning based multi-task and
few-shot learning in a many-task setting. We propose a new method to measure
task similarities with cross-task transfer performance matrix for the deep
learning scenario. Although this matrix provides us critical information
regarding similarity betw... | Robust Task Clustering for Deep Many-Task Learning | 2,017 | http://arxiv.org/pdf/1708.07918v1 | Title Robust Task Clustering Deep ManyTask Learning Summary investigate task clustering deeplearning based multitask fewshot learning manytask setting propose new method measure task similarity crosstask transfer performance matrix deep learning scenario Although matrix provides u critical information regarding similar... | [0.001757272519171238, -0.024962512776255608, -0.03047266975045204, 0.02561763860285282, -0.007608948275446892, 0.012431913986802101, 0.026778310537338257, -0.007478294428437948, 0.05867559835314751, -0.025957508012652397, -0.06346814334392548, -0.019597521051764488, -0.03317791223526001, 0.009456802159547806, 0.005325... |
276 | 276 | ['Gino Brunner', 'Yuyi Wang', 'Roger Wattenhofer', 'Michael Weigelt'] | 1801.06024v1 | We train multi-task autoencoders on linguistic tasks and analyze the learned
hidden sentence representations. The representations change significantly when
translation and part-of-speech decoders are added. The more decoders a model
employs, the better it clusters sentences according to their syntactic
similarity, as t... | Natural Language Multitasking: Analyzing and Improving Syntactic
Saliency of Hidden Representations | 2,018 | http://arxiv.org/pdf/1801.06024v1 | Title Natural Language Multitasking Analyzing Improving Syntactic Saliency Hidden Representations Summary train multitask autoencoders linguistic task analyze learned hidden sentence representation representation change significantly translation partofspeech decoder added decoder model employ better cluster sentence ac... | [0.04328468441963196, -0.0018625339725986123, -0.042040757834911346, 0.04902634024620056, -0.023598480969667435, 0.047014687210321426, 0.014851320534944534, -0.03033214807510376, 0.029052147641777992, -0.08073584735393524, -0.05591224506497383, 0.0034719135146588087, -0.025080954656004906, 0.036438219249248505, 0.03869... |
277 | 277 | ['Minghai Chen', 'Sen Wang', 'Paul Pu Liang', 'Tadas Baltrušaitis', 'Amir Zadeh', 'Louis-Philippe Morency'] | 1802.00924v1 | With the increasing popularity of video sharing websites such as YouTube and
Facebook, multimodal sentiment analysis has received increasing attention from
the scientific community. Contrary to previous works in multimodal sentiment
analysis which focus on holistic information in speech segments such as bag of
words re... | Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement
Learning | 2,018 | http://arxiv.org/pdf/1802.00924v1 | Title Multimodal Sentiment Analysis WordLevel Fusion Reinforcement Learning Summary increasing popularity video sharing website YouTube Facebook multimodal sentiment analysis received increasing attention scientific community Contrary previous work multimodal sentiment analysis focus holistic information speech segment... | [0.023880409076809883, 0.06874074786901474, 0.0020064199343323708, 0.023797376081347466, -0.007718656212091446, -0.020262226462364197, 0.002905611414462328, -0.02502617985010147, -0.028708036988973618, -0.04761388897895813, -0.03299633041024208, -0.03164663165807724, 0.022039808332920074, 0.0894528478384018, 0.02580103... |
278 | 278 | ['Ed Collins', 'Isabelle Augenstein', 'Sebastian Riedel'] | 1706.03946v1 | Automatic summarisation is a popular approach to reduce a document to its
main arguments. Recent research in the area has focused on neural approaches to
summarisation, which can be very data-hungry. However, few large datasets exist
and none for the traditionally popular domain of scientific publications, which
opens ... | A Supervised Approach to Extractive Summarisation of Scientific Papers | 2,017 | http://arxiv.org/pdf/1706.03946v1 | Title Supervised Approach Extractive Summarisation Scientific Papers Summary Automatic summarisation popular approach reduce document main argument Recent research area focused neural approach summarisation datahungry However large datasets exist none traditionally popular domain scientific publication open challenging... | [0.05546897277235985, 0.036916639655828476, 0.012769097462296486, 0.038231298327445984, -0.03908950835466385, 0.005155544728040695, -0.010568211786448956, -0.020524710416793823, -0.009354954585433006, -0.04994983226060867, 0.004804883152246475, 0.02872115932404995, 0.02255595661699772, 0.021443190053105354, 0.009356690... |
279 | 279 | ['Jacob Devlin', 'Hao Cheng', 'Hao Fang', 'Saurabh Gupta', 'Li Deng', 'Xiaodong He', 'Geoffrey Zweig', 'Margaret Mitchell'] | 1505.01809v3 | Two recent approaches have achieved state-of-the-art results in image
captioning. The first uses a pipelined process where a set of candidate words
is generated by a convolutional neural network (CNN) trained on images, and
then a maximum entropy (ME) language model is used to arrange these words into
a coherent senten... | Language Models for Image Captioning: The Quirks and What Works | 2,015 | http://arxiv.org/pdf/1505.01809v3 | Title Language Models Image Captioning Quirks Works Summary Two recent approach achieved stateoftheart result image captioning first us pipelined process set candidate word generated convolutional neural network CNN trained image maximum entropy language model used arrange word coherent sentence second us penultimate a... | [0.07051143050193787, 0.06679592281579971, -0.003020326839759946, 0.06557819247245789, -0.020720146596431732, 0.008877604268491268, 0.01993243210017681, -0.010685762390494347, -0.011696897447109222, -0.034674759954214096, -0.002281500492244959, -0.023624075576663017, 0.03912442922592163, 0.045678701251745224, 0.0103616... |
280 | 280 | ['Mengye Ren', 'Ryan Kiros', 'Richard Zemel'] | 1505.02074v4 | This work aims to address the problem of image-based question-answering (QA)
with new models and datasets. In our work, we propose to use neural networks
and visual semantic embeddings, without intermediate stages such as object
detection and image segmentation, to predict answers to simple questions about
images. Our ... | Exploring Models and Data for Image Question Answering | 2,015 | http://arxiv.org/pdf/1505.02074v4 | Title Exploring Models Data Image Question Answering Summary work aim address problem imagebased questionanswering QA new model datasets work propose use neural network visual semantic embeddings without intermediate stage object detection image segmentation predict answer simple question image model performs 18 time b... | [0.04875091835856438, 0.021046742796897888, -0.011336416006088257, 0.07555010914802551, 0.008658998645842075, 0.026764843612909317, -0.008618188090622425, 0.01862267404794693, -0.006723479367792606, -0.019414395093917847, 0.023782672360539436, 0.028598297387361526, -0.047281406819820404, 0.038023315370082855, 0.0242913... |
281 | 281 | ['Yash Goyal', 'Tejas Khot', 'Douglas Summers-Stay', 'Dhruv Batra', 'Devi Parikh'] | 1612.00837v3 | Problems at the intersection of vision and language are of significant
importance both as challenging research questions and for the rich set of
applications they enable. However, inherent structure in our world and bias in
our language tend to be a simpler signal for learning than visual modalities,
resulting in model... | Making the V in VQA Matter: Elevating the Role of Image Understanding in
Visual Question Answering | 2,016 | http://arxiv.org/pdf/1612.00837v3 | Title Making V VQA Matter Elevating Role Image Understanding Visual Question Answering Summary Problems intersection vision language significant importance challenging research question rich set application enable However inherent structure world bias language tend simpler signal learning visual modality resulting mode... | [0.03509814292192459, 0.05680400878190994, -0.034543465822935104, 0.030327793210744858, -0.030562257394194603, 0.017595887184143066, 0.025898054242134094, -0.017597490921616554, -0.03156403824687004, -0.01465595606714487, 0.026172084733843803, 0.03404659032821655, 0.010982564650475979, 0.09113752841949463, 0.0225955285... |
282 | 282 | ['Mateusz Malinowski', 'Mario Fritz'] | 1410.0210v4 | We propose a method for automatically answering questions about images by
bringing together recent advances from natural language processing and computer
vision. We combine discrete reasoning with uncertain predictions by a
multi-world approach that represents uncertainty about the perceived world in a
bayesian framewo... | A Multi-World Approach to Question Answering about Real-World Scenes
based on Uncertain Input | 2,014 | http://arxiv.org/pdf/1410.0210v4 | Title MultiWorld Approach Question Answering RealWorld Scenes based Uncertain Input Summary propose method automatically answering question image bringing together recent advance natural language processing computer vision combine discrete reasoning uncertain prediction multiworld approach represents uncertainty percei... | [0.05978573113679886, 0.04490907862782478, 0.010872947052121162, 0.043480806052684784, -0.014554851688444614, -0.009198376908898354, -0.0010114817414432764, 0.014343124814331532, 0.01916860230267048, -0.05219611898064613, 0.025891276076436043, -0.031279269605875015, 0.009066601283848286, 0.06506037712097168, 0.02125419... |
283 | 283 | ['Mateusz Malinowski', 'Mario Fritz'] | 1501.03302v2 | Progress in language and image understanding by machines has sparkled the
interest of the research community in more open-ended, holistic tasks, and
refueled an old AI dream of building intelligent machines. We discuss a few
prominent challenges that characterize such holistic tasks and argue for
"question answering ab... | Hard to Cheat: A Turing Test based on Answering Questions about Images | 2,015 | http://arxiv.org/pdf/1501.03302v2 | Title Hard Cheat Turing Test based Answering Questions Images Summary Progress language image understanding machine sparkled interest research community openended holistic task refueled old AI dream building intelligent machine discus prominent challenge characterize holistic task argue question answering image particu... | [0.03784540668129921, 0.00692099379375577, -0.03702068701386452, 0.00965532474219799, -0.02058044821023941, -0.002799171954393387, 0.016656283289194107, 0.013406004756689072, -0.00946202501654625, -0.016794269904494286, -0.003985284361988306, 0.05363457649946213, -0.005288075655698776, 0.08110769838094711, 0.0064461608... |
284 | 284 | ['Aishwarya Agrawal', 'Dhruv Batra', 'Devi Parikh'] | 1606.07356v2 | Recently, a number of deep-learning based models have been proposed for the
task of Visual Question Answering (VQA). The performance of most models is
clustered around 60-70%. In this paper we propose systematic methods to analyze
the behavior of these models as a first step towards recognizing their
strengths and weak... | Analyzing the Behavior of Visual Question Answering Models | 2,016 | http://arxiv.org/pdf/1606.07356v2 | Title Analyzing Behavior Visual Question Answering Models Summary Recently number deeplearning based model proposed task Visual Question Answering VQA performance model clustered around 6070 paper propose systematic method analyze behavior model first step towards recognizing strength weakness identifying fruitful dire... | [0.07380562275648117, 0.0181230790913105, -0.0345357283949852, 0.05666319280862808, 0.006163184065371752, 0.025825493037700653, -0.007961368188261986, 0.0340099073946476, -0.014782741665840149, -0.021929217502474785, 0.012460102327167988, -0.015488985925912857, -0.008923979476094246, 0.042523205280303955, 0.05546228215... |
285 | 285 | ['Harsh Agrawal', 'Arjun Chandrasekaran', 'Dhruv Batra', 'Devi Parikh', 'Mohit Bansal'] | 1606.07493v5 | Temporal common sense has applications in AI tasks such as QA, multi-document
summarization, and human-AI communication. We propose the task of sequencing --
given a jumbled set of aligned image-caption pairs that belong to a story, the
task is to sort them such that the output sequence forms a coherent story. We
prese... | Sort Story: Sorting Jumbled Images and Captions into Stories | 2,016 | http://arxiv.org/pdf/1606.07493v5 | Title Sort Story Sorting Jumbled Images Captions Stories Summary Temporal common sense application AI task QA multidocument summarization humanAI communication propose task sequencing given jumbled set aligned imagecaption pair belong story task sort output sequence form coherent story present multiple approach via una... | [0.025620480999350548, 0.0587407611310482, -0.011837073601782322, 0.02782978117465973, -0.0032393806613981724, 0.0478534922003746, 0.00614558719098568, 0.0035160628613084555, -0.00953300204128027, -0.03974258154630661, 0.051692184060811996, -0.025561995804309845, 0.058960385620594025, 0.10357807576656342, -0.0180671922... |
286 | 286 | ['Ashkan Mokarian', 'Mateusz Malinowski', 'Mario Fritz'] | 1608.02717v1 | We present Mean Box Pooling, a novel visual representation that pools over
CNN representations of a large number, highly overlapping object proposals. We
show that such representation together with nCCA, a successful multimodal
embedding technique, achieves state-of-the-art performance on the Visual
Madlibs task. Moreo... | Mean Box Pooling: A Rich Image Representation and Output Embedding for
the Visual Madlibs Task | 2,016 | http://arxiv.org/pdf/1608.02717v1 | Title Mean Box Pooling Rich Image Representation Output Embedding Visual Madlibs Task Summary present Mean Box Pooling novel visual representation pool CNN representation large number highly overlapping object proposal show representation together nCCA successful multimodal embedding technique achieves stateoftheart pe... | [-0.029553566128015518, -0.008898820728063583, -0.0002850211749318987, 0.07411832362413406, 0.008108250796794891, 0.0261395126581192, 0.04738902673125267, -0.003120200941339135, -0.006630031857639551, -0.025454875081777573, -0.011117050424218178, 0.026201127097010612, -0.03696085512638092, 0.006584473419934511, 0.04462... |
287 | 287 | ['Yuval Atzmon', 'Jonathan Berant', 'Vahid Kezami', 'Amir Globerson', 'Gal Chechik'] | 1608.07639v1 | Recurrent neural networks have recently been used for learning to describe
images using natural language. However, it has been observed that these models
generalize poorly to scenes that were not observed during training, possibly
depending too strongly on the statistics of the text in the training data. Here
we propos... | Learning to generalize to new compositions in image understanding | 2,016 | http://arxiv.org/pdf/1608.07639v1 | Title Learning generalize new composition image understanding Summary Recurrent neural network recently used learning describe image using natural language However observed model generalize poorly scene observed training possibly depending strongly statistic text training data propose describe image using short structu... | [0.03118283301591873, 0.03637874871492386, 0.0151326023042202, 0.06296464055776596, -0.012003778479993343, 0.008699464611709118, 0.001413799123838544, 0.003256863448768854, -0.03573073446750641, -0.05352947860956192, -0.0009998248424381018, -0.030531948432326317, 0.026165850460529327, 0.07841352373361588, 0.02739800326... |
288 | 288 | ['C. Lawrence Zitnick', 'Aishwarya Agrawal', 'Stanislaw Antol', 'Margaret Mitchell', 'Dhruv Batra', 'Devi Parikh'] | 1608.08716v1 | As machines have become more intelligent, there has been a renewed interest
in methods for measuring their intelligence. A common approach is to propose
tasks for which a human excels, but one which machines find difficult. However,
an ideal task should also be easy to evaluate and not be easily gameable. We
begin with... | Measuring Machine Intelligence Through Visual Question Answering | 2,016 | http://arxiv.org/pdf/1608.08716v1 | Title Measuring Machine Intelligence Visual Question Answering Summary machine become intelligent renewed interest method measuring intelligence common approach propose task human excels one machine find difficult However ideal task also easy evaluate easily gameable begin case study exploring recently popular task ima... | [0.03843073919415474, 0.020461248233914375, -0.04631388559937477, 0.03540738672018051, -0.014663448557257652, 0.004288766533136368, 0.01788918487727642, 0.031028596684336662, 0.023628931492567062, 0.0022359101567417383, 0.016585979610681534, 0.018988551571965218, -0.004398867953568697, 0.06177018955349922, 0.0302237439... |
289 | 289 | ['Yash Goyal', 'Akrit Mohapatra', 'Devi Parikh', 'Dhruv Batra'] | 1608.08974v2 | Deep neural networks have shown striking progress and obtained
state-of-the-art results in many AI research fields in the recent years.
However, it is often unsatisfying to not know why they predict what they do. In
this paper, we address the problem of interpreting Visual Question Answering
(VQA) models. Specifically,... | Towards Transparent AI Systems: Interpreting Visual Question Answering
Models | 2,016 | http://arxiv.org/pdf/1608.08974v2 | Title Towards Transparent AI Systems Interpreting Visual Question Answering Models Summary Deep neural network shown striking progress obtained stateoftheart result many AI research field recent year However often unsatisfying know predict paper address problem interpreting Visual Question Answering VQA model Specifica... | [0.03860742226243019, 0.013975169509649277, -0.03445684537291527, 0.07084664702415466, 0.0009075008565559983, -0.0041730678640306, -0.010166934691369534, 0.024501441046595573, -0.01296352781355381, -0.028722422197461128, 0.011036978103220463, 0.0017562838038429618, 0.003150589531287551, 0.0756709948182106, 0.0370296724... |
290 | 290 | ['Abhishek Das', 'Satwik Kottur', 'Khushi Gupta', 'Avi Singh', 'Deshraj Yadav', 'José M. F. Moura', 'Devi Parikh', 'Dhruv Batra'] | 1611.08669v5 | We introduce the task of Visual Dialog, which requires an AI agent to hold a
meaningful dialog with humans in natural, conversational language about visual
content. Specifically, given an image, a dialog history, and a question about
the image, the agent has to ground the question in image, infer context from
history, ... | Visual Dialog | 2,016 | http://arxiv.org/pdf/1611.08669v5 | Title Visual Dialog Summary introduce task Visual Dialog requires AI agent hold meaningful dialog human natural conversational language visual content Specifically given image dialog history question image agent ground question image infer context history answer question accurately Visual Dialog disentangled enough spe... | [0.0699768140912056, 0.07259771972894669, -0.01006864383816719, 0.03940192237496376, -0.013206160627305508, 0.022668281570076942, 0.014168682508170605, 0.013024978339672089, 0.03724847361445427, -0.034664757549762726, -0.01568761095404625, 0.004153894260525703, -0.008535664528608322, 0.1040937602519989, -0.004536944907... |
291 | 291 | ['Abhinav Thanda', 'Shankar M Venkatesan'] | 1701.02477v1 | Multi-task learning (MTL) involves the simultaneous training of two or more
related tasks over shared representations. In this work, we apply MTL to
audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn
a mapping between audio-visual fused features and frame labels obtained from
acoustic GMM/H... | Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic
Speech Recognition | 2,017 | http://arxiv.org/pdf/1701.02477v1 | Title Multitask Learning Deep Neural Networks Audio Visual Automatic Speech Recognition Summary Multitask learning MTL involves simultaneous training two related task shared representation work apply MTL audiovisual automatic speech recognitionAVASR primary task learn mapping audiovisual fused feature frame label obtai... | [-0.03141818195581436, -0.0007812197436578572, -0.01270153746008873, 0.00644897622987628, 0.014467687346041203, 0.014353024773299694, 0.055697157979011536, -0.027531344443559647, -0.03224589303135872, -0.014968904666602612, -0.11036159843206406, -0.02765566296875477, 0.021632378920912743, 0.051266107708215714, 0.031522... |
292 | 292 | ['Abhishek Das', 'Satwik Kottur', 'José M. F. Moura', 'Stefan Lee', 'Dhruv Batra'] | 1703.06585v2 | We introduce the first goal-driven training for visual question answering and
dialog agents. Specifically, we pose a cooperative 'image guessing' game
between two agents -- Qbot and Abot -- who communicate in natural language
dialog so that Qbot can select an unseen image from a lineup of images. We use
deep reinforcem... | Learning Cooperative Visual Dialog Agents with Deep Reinforcement
Learning | 2,017 | http://arxiv.org/pdf/1703.06585v2 | Title Learning Cooperative Visual Dialog Agents Deep Reinforcement Learning Summary introduce first goaldriven training visual question answering dialog agent Specifically pose cooperative image guessing game two agent Qbot Abot communicate natural language dialog Qbot select unseen image lineup image use deep reinforc... | [0.062454722821712494, 0.03200775757431984, -0.008456087671220303, 0.0404348187148571, -0.02340024895966053, 0.014208480715751648, 0.028155341744422913, -0.009489491581916809, 0.003380730515345931, -0.038527462631464005, -0.03748941421508789, 0.007519615348428488, -0.032869283109903336, 0.11186755448579788, -0.00782845... |
293 | 293 | ['Wei-Lun Chao', 'Hexiang Hu', 'Fei Sha'] | 1704.07121v1 | Visual question answering (QA) has attracted a lot of attention lately, seen
essentially as a form of (visual) Turing test that artificial intelligence
should strive to achieve. In this paper, we study a crucial component of this
task: how can we design good datasets for the task? We focus on the design of
multiple-cho... | Being Negative but Constructively: Lessons Learnt from Creating Better
Visual Question Answering Datasets | 2,017 | http://arxiv.org/pdf/1704.07121v1 | Title Negative Constructively Lessons Learnt Creating Better Visual Question Answering Datasets Summary Visual question answering QA attracted lot attention lately seen essentially form visual Turing test artificial intelligence strive achieve paper study crucial component task design good datasets task focus design mu... | [0.05095381662249565, 0.022796915844082832, -0.03355911746621132, 0.041267335414886475, 0.0018429163610562682, 0.02651730179786682, -0.004608791787177324, 0.025015152990818024, -0.004312430042773485, 0.012326233088970184, -0.006936265155673027, 0.030817486345767975, -0.015086743980646133, 0.024741847068071365, 0.036706... |
294 | 294 | ['Aishwarya Agrawal', 'Aniruddha Kembhavi', 'Dhruv Batra', 'Devi Parikh'] | 1704.08243v1 | Visual Question Answering (VQA) has received a lot of attention over the past
couple of years. A number of deep learning models have been proposed for this
task. However, it has been shown that these models are heavily driven by
superficial correlations in the training data and lack compositionality -- the
ability to a... | C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0
Dataset | 2,017 | http://arxiv.org/pdf/1704.08243v1 | Title CVQA Compositional Split Visual Question Answering VQA v10 Dataset Summary Visual Question Answering VQA received lot attention past couple year number deep learning model proposed task However shown model heavily driven superficial correlation training data lack compositionality ability answer question unseen co... | [0.07020939886569977, 0.033546529710292816, -0.020312199369072914, 0.04286683723330498, 0.0024580531753599644, 0.03000464476644993, 0.012141934596002102, -0.0004130439192522317, -0.03183414787054062, -0.0023541348055005074, 0.011007928289473057, -0.03912682458758354, -0.007061128504574299, 0.049503810703754425, 0.04201... |
295 | 295 | ['Alexander Kuhnle', 'Ann Copestake'] | 1706.01322v1 | We discuss problems with the standard approaches to evaluation for tasks like
visual question answering, and argue that artificial data can be used to
address these as a complement to current practice. We demonstrate that with the
help of existing 'deep' linguistic processing technology we are able to create
challengin... | Deep learning evaluation using deep linguistic processing | 2,017 | http://arxiv.org/pdf/1706.01322v1 | Title Deep learning evaluation using deep linguistic processing Summary discus problem standard approach evaluation task like visual question answering argue artificial data used address complement current practice demonstrate help existing deep linguistic processing technology able create challenging abstract datasets... | [0.04702436551451683, 0.007313648238778114, -0.003427966730669141, 0.06499689072370529, -0.037067756056785583, 0.0241558700799942, 0.03907600790262222, -0.012462311424314976, -0.007517700549215078, -0.03356023132801056, -0.012995629571378231, -0.03819983825087547, 0.007236033212393522, 0.04533670097589493, 0.0185821466... |
296 | 296 | ['Xu Sun', 'Xuancheng Ren', 'Shuming Ma', 'Houfeng Wang'] | 1706.06197v4 | We propose a simple yet effective technique for neural network learning. The
forward propagation is computed as usual. In back propagation, only a small
subset of the full gradient is computed to update the model parameters. The
gradient vectors are sparsified in such a way that only the top-$k$ elements
(in terms of m... | meProp: Sparsified Back Propagation for Accelerated Deep Learning with
Reduced Overfitting | 2,017 | http://arxiv.org/pdf/1706.06197v4 | Title meProp Sparsified Back Propagation Accelerated Deep Learning Reduced Overfitting Summary propose simple yet effective technique neural network learning forward propagation computed usual back propagation small subset full gradient computed update model parameter gradient vector sparsified way topk element term ma... | [-0.034714024513959885, 0.04026816785335541, -0.029872296378016472, 0.025489047169685364, 0.05376351252198219, -0.028138553723692894, 0.012318890541791916, 0.019436581060290337, -0.0021193919237703085, 0.017988935112953186, 0.012787655927240849, 0.007434160448610783, 0.004233662039041519, 0.05216285213828087, 0.0209126... |
297 | 297 | ['Suranjana Samanta', 'Sameep Mehta'] | 1707.02812v1 | Adversarial samples are strategically modified samples, which are crafted
with the purpose of fooling a classifier at hand. An attacker introduces
specially crafted adversarial samples to a deployed classifier, which are being
mis-classified by the classifier. However, the samples are perceived to be
drawn from entirel... | Towards Crafting Text Adversarial Samples | 2,017 | http://arxiv.org/pdf/1707.02812v1 | Title Towards Crafting Text Adversarial Samples Summary Adversarial sample strategically modified sample crafted purpose fooling classifier hand attacker introduces specially crafted adversarial sample deployed classifier misclassified classifier However sample perceived drawn entirely different class thus becomes hard... | [0.08556494861841202, 0.06461843848228455, -0.014933346770703793, 0.049989040940999985, -0.04509786143898964, -0.01245724968612194, 0.026351122185587883, 0.02328794077038765, 0.009678495116531849, -0.08863590657711029, 0.025523070245981216, 0.02739601396024227, 0.015645092353224754, 0.04214196279644966, 0.0312421750277... |
298 | 298 | ['Ramakanth Pasunuru', 'Mohit Bansal'] | 1708.02300v1 | Sequence-to-sequence models have shown promising improvements on the temporal
task of video captioning, but they optimize word-level cross-entropy loss
during training. First, using policy gradient and mixed-loss methods for
reinforcement learning, we directly optimize sentence-level task-based metrics
(as rewards), ac... | Reinforced Video Captioning with Entailment Rewards | 2,017 | http://arxiv.org/pdf/1708.02300v1 | Title Reinforced Video Captioning Entailment Rewards Summary Sequencetosequence model shown promising improvement temporal task video captioning optimize wordlevel crossentropy loss training First using policy gradient mixedloss method reinforcement learning directly optimize sentencelevel taskbased metric reward achie... | [0.05354555323719978, 0.03832118585705757, 0.002761641051620245, 0.029181038960814476, -0.017482832074165344, 0.015165441669523716, -0.03006918355822563, -0.005761315114796162, -0.0414213165640831, -0.05080592632293701, -0.027974097058176994, -0.016205307096242905, 0.025298845022916794, 0.0386817567050457, -0.007077659... |
299 | 299 | ['Licheng Yu', 'Mohit Bansal', 'Tamara L. Berg'] | 1708.02977v1 | We address the problem of end-to-end visual storytelling. Given a photo
album, our model first selects the most representative (summary) photos, and
then composes a natural language story for the album. For this task, we make
use of the Visual Storytelling dataset and a model composed of three
hierarchically-attentive ... | Hierarchically-Attentive RNN for Album Summarization and Storytelling | 2,017 | http://arxiv.org/pdf/1708.02977v1 | Title HierarchicallyAttentive RNN Album Summarization Storytelling Summary address problem endtoend visual storytelling Given photo album model first selects representative summary photo composes natural language story album task make use Visual Storytelling dataset model composed three hierarchicallyattentive Recurren... | [0.042152877897024155, 0.08644861727952957, 0.003656974760815501, 0.05060679465532303, -0.015786796808242798, 0.03198721632361412, 0.027615293860435486, -0.021865271031856537, -0.04505283758044243, -0.022298071533441544, -0.0163447093218565, -0.017181573435664177, 0.02027195505797863, 0.06858467310667038, 0.01531388424... |
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