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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100 | 100 | ['Peter Wittek', 'Sándor Darányi', 'Efstratios Kontopoulos', 'Theodoros Moysiadis', 'Ioannis Kompatsiaris'] | 1502.01753v1 | Based on the Aristotelian concept of potentiality vs. actuality allowing for
the study of energy and dynamics in language, we propose a field approach to
lexical analysis. Falling back on the distributional hypothesis to
statistically model word meaning, we used evolving fields as a metaphor to
express time-dependent c... | Monitoring Term Drift Based on Semantic Consistency in an Evolving
Vector Field | 2,015 | http://arxiv.org/pdf/1502.01753v1 | Title Monitoring Term Drift Based Semantic Consistency Evolving Vector Field Summary Based Aristotelian concept potentiality v actuality allowing study energy dynamic language propose field approach lexical analysis Falling back distributional hypothesis statistically model word meaning used evolving field metaphor exp... | [0.030528267845511436, 0.04965444281697273, -0.031217029318213463, 0.022996678948402405, -0.03168976306915283, -0.0291778314858675, -0.01496312115341425, 0.028549542650580406, -0.07261703163385391, -0.051482826471328735, 0.042648691684007645, -0.005036256276071072, 0.02993091568350792, 0.008601434528827667, -0.00916272... |
101 | 101 | ['Jan Chorowski', 'Navdeep Jaitly'] | 1612.02695v1 | The recently proposed Sequence-to-Sequence (seq2seq) framework advocates
replacing complex data processing pipelines, such as an entire automatic speech
recognition system, with a single neural network trained in an end-to-end
fashion. In this contribution, we analyse an attention-based seq2seq speech
recognition syste... | Towards better decoding and language model integration in sequence to
sequence models | 2,016 | http://arxiv.org/pdf/1612.02695v1 | Title Towards better decoding language model integration sequence sequence model Summary recently proposed SequencetoSequence seq2seq framework advocate replacing complex data processing pipeline entire automatic speech recognition system single neural network trained endtoend fashion contribution analyse attentionbase... | [0.02097560651600361, 0.07567127794027328, 0.01599881425499916, 0.03510940074920654, -0.020172972232103348, 0.010558419860899448, -0.004460576921701431, -0.018668076023459435, -0.033789921551942825, -0.0422428734600544, -0.02375395968556404, -0.04202241078019142, 0.060631562024354935, 0.06385961920022964, 0.02806039713... |
102 | 102 | ['Dzmitry Bahdanau', 'Kyunghyun Cho', 'Yoshua Bengio'] | 1409.0473v7 | Neural machine translation is a recently proposed approach to machine
translation. Unlike the traditional statistical machine translation, the neural
machine translation aims at building a single neural network that can be
jointly tuned to maximize the translation performance. The models proposed
recently for neural ma... | Neural Machine Translation by Jointly Learning to Align and Translate | 2,014 | http://arxiv.org/pdf/1409.0473v7 | Title Neural Machine Translation Jointly Learning Align Translate Summary Neural machine translation recently proposed approach machine translation Unlike traditional statistical machine translation neural machine translation aim building single neural network jointly tuned maximize translation performance model propos... | [0.016232138499617577, 0.019071346148848534, -0.006837559398263693, 0.06399498879909515, -0.04284335672855377, 0.006947672460228205, 0.02146477811038494, -0.015255913138389587, -0.04788481444120407, -0.0093612689524889, -0.029340293258428574, -0.021650521084666252, 0.03916667401790619, 0.009066462516784668, 0.031225515... |
103 | 103 | ['Jean Pouget-Abadie', 'Dzmitry Bahdanau', 'Bart van Merrienboer', 'Kyunghyun Cho', 'Yoshua Bengio'] | 1409.1257v2 | The authors of (Cho et al., 2014a) have shown that the recently introduced
neural network translation systems suffer from a significant drop in
translation quality when translating long sentences, unlike existing
phrase-based translation systems. In this paper, we propose a way to address
this issue by automatically se... | Overcoming the Curse of Sentence Length for Neural Machine Translation
using Automatic Segmentation | 2,014 | http://arxiv.org/pdf/1409.1257v2 | Title Overcoming Curse Sentence Length Neural Machine Translation using Automatic Segmentation Summary author Cho et al 2014a shown recently introduced neural network translation system suffer significant drop translation quality translating long sentence unlike existing phrasebased translation system paper propose way... | [0.020197276026010513, 0.029057249426841736, -0.0055040884763002396, 0.05541546642780304, -0.04097885265946388, 0.0034373069647699594, 0.04737241938710213, 0.004094036296010017, -0.045474689453840256, -0.0030410634353756905, 0.008909204974770546, 0.008727390319108963, 0.03498250991106033, 0.0009199061314575374, 0.03728... |
104 | 104 | ['William Chan', 'Nan Rosemary Ke', 'Ian Lane'] | 1504.01483v1 | Deep Neural Network (DNN) acoustic models have yielded many state-of-the-art
results in Automatic Speech Recognition (ASR) tasks. More recently, Recurrent
Neural Network (RNN) models have been shown to outperform DNNs counterparts.
However, state-of-the-art DNN and RNN models tend to be impractical to deploy
on embedde... | Transferring Knowledge from a RNN to a DNN | 2,015 | http://arxiv.org/pdf/1504.01483v1 | Title Transferring Knowledge RNN DNN Summary Deep Neural Network DNN acoustic model yielded many stateoftheart result Automatic Speech Recognition ASR task recently Recurrent Neural Network RNN model shown outperform DNNs counterpart However stateoftheart DNN RNN model tend impractical deploy embedded system limited co... | [-0.015455097891390324, 0.028654413297772408, 0.007837031036615372, 0.03152240067720413, -0.02903357334434986, -0.03145992010831833, 0.044474970549345016, -0.016366124153137207, -0.04448847472667694, 0.003005929524078965, -0.03364250808954239, 0.010486699640750885, 0.061562370508909225, 0.026221472769975662, 0.01799595... |
105 | 105 | ['Sarath Chandar', 'Mitesh M. Khapra', 'Hugo Larochelle', 'Balaraman Ravindran'] | 1504.07225v3 | Common Representation Learning (CRL), wherein different descriptions (or
views) of the data are embedded in a common subspace, is receiving a lot of
attention recently. Two popular paradigms here are Canonical Correlation
Analysis (CCA) based approaches and Autoencoder (AE) based approaches. CCA
based approaches learn ... | Correlational Neural Networks | 2,015 | http://arxiv.org/pdf/1504.07225v3 | Title Correlational Neural Networks Summary Common Representation Learning CRL wherein different description view data embedded common subspace receiving lot attention recently Two popular paradigm Canonical Correlation Analysis CCA based approach Autoencoder AE based approach CCA based approach learn joint representat... | [-0.012877211906015873, 0.0465271919965744, -0.013225826434791088, 0.03985082358121872, -0.031307924538850784, 0.03361591696739197, 0.038085710257291794, -0.0033907159231603146, 0.014290286228060722, 0.005074954591691494, -0.06664880365133286, -0.001258045551367104, 0.051832761615514755, 0.008261256851255894, 0.0092008... |
106 | 106 | ['Jan Chorowski', 'Dzmitry Bahdanau', 'Dmitriy Serdyuk', 'Kyunghyun Cho', 'Yoshua Bengio'] | 1506.07503v1 | Recurrent sequence generators conditioned on input data through an attention
mechanism have recently shown very good performance on a range of tasks in-
cluding machine translation, handwriting synthesis and image caption gen-
eration. We extend the attention-mechanism with features needed for speech
recognition. We sh... | Attention-Based Models for Speech Recognition | 2,015 | http://arxiv.org/pdf/1506.07503v1 | Title AttentionBased Models Speech Recognition Summary Recurrent sequence generator conditioned input data attention mechanism recently shown good performance range task cluding machine translation handwriting synthesis image caption gen eration extend attentionmechanism feature needed speech recognition show adaptatio... | [0.019523048773407936, 0.0204450786113739, 0.008692066185176373, 0.04665157571434975, 0.0042520323768258095, -0.046689894050359726, 0.04492989555001259, -0.01992475427687168, -0.030953174456954002, -0.037043068557977676, -0.05595624819397926, -0.045688990503549576, 0.053314339369535446, 0.027763931080698967, 0.05054059... |
107 | 107 | ['Haşim Sak', 'Andrew Senior', 'Kanishka Rao', 'Françoise Beaufays'] | 1507.06947v1 | We have recently shown that deep Long Short-Term Memory (LSTM) recurrent
neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as
acoustic models for speech recognition. More recently, we have shown that the
performance of sequence trained context dependent (CD) hidden Markov model
(HMM) acoustic m... | Fast and Accurate Recurrent Neural Network Acoustic Models for Speech
Recognition | 2,015 | http://arxiv.org/pdf/1507.06947v1 | Title Fast Accurate Recurrent Neural Network Acoustic Models Speech Recognition Summary recently shown deep Long ShortTerm Memory LSTM recurrent neural network RNNs outperform feed forward deep neural network DNNs acoustic model speech recognition recently shown performance sequence trained context dependent CD hidden ... | [-0.008163961581885815, 0.01952042616903782, 0.02254086546599865, 0.06792142987251282, -0.011170904152095318, -0.03642510622739792, 0.03995650261640549, 0.010094176046550274, -0.057481907308101654, -0.03643953800201416, -0.05215294286608696, -0.04255299270153046, 0.07225282490253448, 0.014487023465335369, -0.0352041833... |
108 | 108 | ['William Chan', 'Navdeep Jaitly', 'Quoc V. Le', 'Oriol Vinyals'] | 1508.01211v2 | We present Listen, Attend and Spell (LAS), a neural network that learns to
transcribe speech utterances to characters. Unlike traditional DNN-HMM models,
this model learns all the components of a speech recognizer jointly. Our system
has two components: a listener and a speller. The listener is a pyramidal
recurrent ne... | Listen, Attend and Spell | 2,015 | http://arxiv.org/pdf/1508.01211v2 | Title Listen Attend Spell Summary present Listen Attend Spell LAS neural network learns transcribe speech utterance character Unlike traditional DNNHMM model model learns component speech recognizer jointly system two component listener speller listener pyramidal recurrent network encoder accepts filter bank spectrum i... | [-0.001111628138460219, 0.024756576865911484, 0.018808944150805473, 0.058023370802402496, -0.025091195479035378, -0.033588431775569916, 0.026153886690735817, -0.021866867318749428, -0.039743371307849884, -0.005591810680925846, -0.0414145402610302, -0.021855205297470093, 0.04940786957740784, 0.05602109432220459, 0.03506... |
109 | 109 | ['Shihao Ji', 'S. V. N. Vishwanathan', 'Nadathur Satish', 'Michael J. Anderson', 'Pradeep Dubey'] | 1511.06909v7 | We propose BlackOut, an approximation algorithm to efficiently train massive
recurrent neural network language models (RNNLMs) with million word
vocabularies. BlackOut is motivated by using a discriminative loss, and we
describe a new sampling strategy which significantly reduces computation while
improving stability, ... | BlackOut: Speeding up Recurrent Neural Network Language Models With Very
Large Vocabularies | 2,015 | http://arxiv.org/pdf/1511.06909v7 | Title BlackOut Speeding Recurrent Neural Network Language Models Large Vocabularies Summary propose BlackOut approximation algorithm efficiently train massive recurrent neural network language model RNNLMs million word vocabulary BlackOut motivated using discriminative loss describe new sampling strategy significantly ... | [-0.0006652222364209592, 0.041236214339733124, 0.009081205353140831, 0.0381535068154335, -0.011480504646897316, -0.007721998263150454, 0.034583013504743576, 0.014878233894705772, -0.025639204308390617, -0.028501413762569427, -0.01765325292944908, -0.05223629251122475, 0.01297624222934246, 0.04424270987510681, 0.0052630... |
110 | 110 | ['Marta R. Costa-Jussà', 'José A. R. Fonollosa'] | 1603.00810v3 | Neural Machine Translation (MT) has reached state-of-the-art results.
However, one of the main challenges that neural MT still faces is dealing with
very large vocabularies and morphologically rich languages. In this paper, we
propose a neural MT system using character-based embeddings in combination with
convolutional... | Character-based Neural Machine Translation | 2,016 | http://arxiv.org/pdf/1603.00810v3 | Title Characterbased Neural Machine Translation Summary Neural Machine Translation MT reached stateoftheart result However one main challenge neural MT still face dealing large vocabulary morphologically rich language paper propose neural MT system using characterbased embeddings combination convolutional highway layer... | [0.014114925637841225, 0.035114072263240814, 0.002446637023240328, 0.09347090870141983, -0.000986468861810863, -0.0038921234663575888, -0.020763492211699486, 0.016344992443919182, -0.04062526300549507, -0.023435117676854134, -0.0058680386282503605, -0.0912121906876564, 0.0344366617500782, 0.04694531857967377, 0.0838363... |
111 | 111 | ['Yangfeng Ji', 'Gholamreza Haffari', 'Jacob Eisenstein'] | 1603.01913v2 | This paper presents a novel latent variable recurrent neural network
architecture for jointly modeling sequences of words and (possibly latent)
discourse relations between adjacent sentences. A recurrent neural network
generates individual words, thus reaping the benefits of
discriminatively-trained vector representati... | A Latent Variable Recurrent Neural Network for Discourse Relation
Language Models | 2,016 | http://arxiv.org/pdf/1603.01913v2 | Title Latent Variable Recurrent Neural Network Discourse Relation Language Models Summary paper present novel latent variable recurrent neural network architecture jointly modeling sequence word possibly latent discourse relation adjacent sentence recurrent neural network generates individual word thus reaping benefit ... | [0.06390361487865448, 0.02962624467909336, 0.0004280621069483459, 0.055127594619989395, -0.02473086304962635, -0.014818601310253143, 0.012562375515699387, -0.007152600213885307, -0.023706721141934395, -0.07547377049922943, 0.010420695878565311, -0.022652819752693176, 0.023212887346744537, 0.028981022536754608, -0.00939... |
112 | 112 | ['Zhiyuan Tang', 'Lantian Li', 'Dong Wang'] | 1603.09643v4 | Although highly correlated, speech and speaker recognition have been regarded
as two independent tasks and studied by two communities. This is certainly not
the way that people behave: we decipher both speech content and speaker traits
at the same time. This paper presents a unified model to perform speech and
speaker ... | Multi-task Recurrent Model for Speech and Speaker Recognition | 2,016 | http://arxiv.org/pdf/1603.09643v4 | Title Multitask Recurrent Model Speech Speaker Recognition Summary Although highly correlated speech speaker recognition regarded two independent task studied two community certainly way people behave decipher speech content speaker trait time paper present unified model perform speech speaker recognition simultaneousl... | [-0.018811749294400215, 0.028306886553764343, -7.840494072297588e-05, 0.04581144079566002, -0.023841267451643944, 0.01702040806412697, 0.04916604980826378, -0.04716957360506058, -0.017879294231534004, -0.0043065547943115234, -0.07053326070308685, -0.05154119059443474, 0.05155247077345848, -0.006731683854013681, 0.00124... |
113 | 113 | ['Sarath Chandar', 'Sungjin Ahn', 'Hugo Larochelle', 'Pascal Vincent', 'Gerald Tesauro', 'Yoshua Bengio'] | 1605.07427v1 | Memory networks are neural networks with an explicit memory component that
can be both read and written to by the network. The memory is often addressed
in a soft way using a softmax function, making end-to-end training with
backpropagation possible. However, this is not computationally scalable for
applications which ... | Hierarchical Memory Networks | 2,016 | http://arxiv.org/pdf/1605.07427v1 | Title Hierarchical Memory Networks Summary Memory network neural network explicit memory component read written network memory often addressed soft way using softmax function making endtoend training backpropagation possible However computationally scalable application require network read extremely large memory hand w... | [0.008692226372659206, 0.014331907965242863, -0.02022092044353485, 0.03938573971390724, 0.021290449425578117, 0.004444582387804985, 0.00995647069066763, -0.019644591957330704, -0.015351303853094578, -0.05517832189798355, -0.03843724727630615, -0.0025149635039269924, -0.006596947554498911, 0.020143985748291016, 0.030484... |
114 | 114 | ['Sam Wiseman', 'Alexander M. Rush'] | 1606.02960v2 | Sequence-to-Sequence (seq2seq) modeling has rapidly become an important
general-purpose NLP tool that has proven effective for many text-generation and
sequence-labeling tasks. Seq2seq builds on deep neural language modeling and
inherits its remarkable accuracy in estimating local, next-word distributions.
In this work... | Sequence-to-Sequence Learning as Beam-Search Optimization | 2,016 | http://arxiv.org/pdf/1606.02960v2 | Title SequencetoSequence Learning BeamSearch Optimization Summary SequencetoSequence seq2seq modeling rapidly become important generalpurpose NLP tool proven effective many textgeneration sequencelabeling task Seq2seq build deep neural language modeling inherits remarkable accuracy estimating local nextword distributio... | [0.06399953365325928, 0.0633859634399414, 0.011333177797496319, 0.021156882867217064, -0.0031690301839262247, -0.010915356688201427, 0.008128847926855087, -0.021023651584982872, 0.016098977997899055, -0.030058465898036957, -0.016497142612934113, -0.021620193496346474, 0.010177547112107277, 0.08310149610042572, 0.015923... |
115 | 115 | ['Ankit Vani', 'Yacine Jernite', 'David Sontag'] | 1705.08557v1 | In this work, we present the Grounded Recurrent Neural Network (GRNN), a
recurrent neural network architecture for multi-label prediction which
explicitly ties labels to specific dimensions of the recurrent hidden state (we
call this process "grounding"). The approach is particularly well-suited for
extracting large nu... | Grounded Recurrent Neural Networks | 2,017 | http://arxiv.org/pdf/1705.08557v1 | Title Grounded Recurrent Neural Networks Summary work present Grounded Recurrent Neural Network GRNN recurrent neural network architecture multilabel prediction explicitly tie label specific dimension recurrent hidden state call process grounding approach particularly wellsuited extracting large number concept text app... | [0.03502779081463814, 0.003919562324881554, -0.008802095428109169, 0.014723682776093483, 0.0010392939439043403, 0.0014341771602630615, 0.022555751726031303, 0.002775089582428336, -0.010944842360913754, -0.02333691529929638, -0.006412681192159653, -0.02209273912012577, 0.03233269602060318, 0.08150706440210342, 0.0060337... |
116 | 116 | ['Tsung-Hsien Wen', 'Yishu Miao', 'Phil Blunsom', 'Steve Young'] | 1705.10229v1 | Developing a dialogue agent that is capable of making autonomous decisions
and communicating by natural language is one of the long-term goals of machine
learning research. Traditional approaches either rely on hand-crafting a small
state-action set for applying reinforcement learning that is not scalable or
constructi... | Latent Intention Dialogue Models | 2,017 | http://arxiv.org/pdf/1705.10229v1 | Title Latent Intention Dialogue Models Summary Developing dialogue agent capable making autonomous decision communicating natural language one longterm goal machine learning research Traditional approach either rely handcrafting small stateaction set applying reinforcement learning scalable constructing deterministic m... | [0.10098562389612198, 0.06708446890115738, -0.01680503971874714, 0.022988336160779, -0.03346974030137062, -0.0035950455348938704, -0.0012845752062276006, -0.02798912487924099, 0.008067675866186619, -0.06890923529863358, 0.009950743988156319, 0.010213034227490425, -0.046132877469062805, 0.0958106741309166, 0.00786982383... |
117 | 117 | ['Julius Kunze', 'Louis Kirsch', 'Ilia Kurenkov', 'Andreas Krug', 'Jens Johannsmeier', 'Sebastian Stober'] | 1706.00290v1 | End-to-end training of automated speech recognition (ASR) systems requires
massive data and compute resources. We explore transfer learning based on model
adaptation as an approach for training ASR models under constrained GPU memory,
throughput and training data. We conduct several systematic experiments
adapting a Wa... | Transfer Learning for Speech Recognition on a Budget | 2,017 | http://arxiv.org/pdf/1706.00290v1 | Title Transfer Learning Speech Recognition Budget Summary Endtoend training automated speech recognition ASR system requires massive data compute resource explore transfer learning based model adaptation approach training ASR model constrained GPU memory throughput training data conduct several systematic experiment ad... | [0.0016755897086113691, 0.05258483067154884, 0.018963608890771866, 0.055986594408750534, 0.007710160221904516, 0.02001683972775936, 0.020336255431175232, -0.012822264805436134, -0.012132210657000542, -0.04293149709701538, -0.052866131067276, 0.009552624076604843, 0.01511545479297638, 0.04265570268034935, 0.003736619371... |
118 | 118 | ['Matt Shannon'] | 1706.02776v1 | State-level minimum Bayes risk (sMBR) training has become the de facto
standard for sequence-level training of speech recognition acoustic models. It
has an elegant formulation using the expectation semiring, and gives large
improvements in word error rate (WER) over models trained solely using
cross-entropy (CE) or co... | Optimizing expected word error rate via sampling for speech recognition | 2,017 | http://arxiv.org/pdf/1706.02776v1 | Title Optimizing expected word error rate via sampling speech recognition Summary Statelevel minimum Bayes risk sMBR training become de facto standard sequencelevel training speech recognition acoustic model elegant formulation using expectation semiring give large improvement word error rate WER model trained solely u... | [-0.0015462484443560243, 0.019713643938302994, 0.03889480233192444, 0.007201068103313446, -0.020102335140109062, -0.019838565960526466, 0.021725941449403763, -0.027281509712338448, -0.018865812569856644, -0.038721613585948944, -0.04394925385713577, -0.04947885125875473, 0.07006166875362396, 0.01906268671154976, -0.0026... |
119 | 119 | ['Artem M. Grachev', 'Dmitry I. Ignatov', 'Andrey V. Savchenko'] | 1708.05963v1 | In this paper, we consider several compression techniques for the language
modeling problem based on recurrent neural networks (RNNs). It is known that
conventional RNNs, e.g, LSTM-based networks in language modeling, are
characterized with either high space complexity or substantial inference time.
This problem is esp... | Neural Networks Compression for Language Modeling | 2,017 | http://arxiv.org/pdf/1708.05963v1 | Title Neural Networks Compression Language Modeling Summary paper consider several compression technique language modeling problem based recurrent neural network RNNs known conventional RNNs eg LSTMbased network language modeling characterized either high space complexity substantial inference time problem especially c... | [0.022581350058317184, 0.021247537806630135, -0.0034962582867592573, 0.019048819318413734, -0.03415098786354065, -0.01466596033424139, -0.003558417782187462, 0.054771434515714645, -0.08111077547073364, -0.0035942259710282087, 0.018638819456100464, -0.05355372279882431, 0.038730550557374954, 0.00589417852461338, 0.02029... |
120 | 120 | ['Mostafa Dehghani', 'Aliaksei Severyn', 'Sascha Rothe', 'Jaap Kamps'] | 1711.00313v2 | Training deep neural networks requires massive amounts of training data, but
for many tasks only limited labeled data is available. This makes weak
supervision attractive, using weak or noisy signals like the output of
heuristic methods or user click-through data for training. In a semi-supervised
setting, we can use a... | Avoiding Your Teacher's Mistakes: Training Neural Networks with
Controlled Weak Supervision | 2,017 | http://arxiv.org/pdf/1711.00313v2 | Title Avoiding Teachers Mistakes Training Neural Networks Controlled Weak Supervision Summary Training deep neural network requires massive amount training data many task limited labeled data available make weak supervision attractive using weak noisy signal like output heuristic method user clickthrough data training ... | [0.0369996540248394, 0.051004763692617416, -0.01590188406407833, 0.018436508253216743, 0.024910999462008476, -0.00912522617727518, 0.03938376158475876, -0.03585285693407059, -0.0014833923196420074, -0.022313877940177917, -0.08250012993812561, 0.011987103149294853, -0.038493309170007706, 0.03639517351984978, 0.018426405... |
121 | 121 | ['Christopher Tegho', 'Paweł Budzianowski', 'Milica Gašić'] | 1711.11486v1 | In statistical dialogue management, the dialogue manager learns a policy that
maps a belief state to an action for the system to perform. Efficient
exploration is key to successful policy optimisation. Current deep
reinforcement learning methods are very promising but rely on epsilon-greedy
exploration, thus subjecting... | Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy
Optimisation | 2,017 | http://arxiv.org/pdf/1711.11486v1 | Title Uncertainty Estimates Efficient Neural Networkbased Dialogue Policy Optimisation Summary statistical dialogue management dialogue manager learns policy map belief state action system perform Efficient exploration key successful policy optimisation Current deep reinforcement learning method promising rely epsilong... | [0.05407075583934784, 0.06020825356245041, -0.009871243499219418, 0.03316120430827141, 0.0015843056607991457, 0.0019181536044925451, 0.0073473891243338585, -0.03800572082400322, -0.016431016847491264, -0.02771364524960518, -0.04231969267129898, -0.051028646528720856, -0.0011075044749304652, 0.08347871154546738, -0.0448... |
122 | 122 | ['Yuanhang Su', 'Yuzhong Huang', 'C. -C. Jay Kuo'] | 1803.01686v1 | In this work, we investigate the memory capability of recurrent neural
networks (RNNs), where this capability is defined as a function that maps an
element in a sequence to the current output. We first analyze the system
function of a recurrent neural network (RNN) cell, and provide analytical
results for three RNNs. T... | On Extended Long Short-term Memory and Dependent Bidirectional Recurrent
Neural Network | 2,018 | http://arxiv.org/pdf/1803.01686v1 | Title Extended Long Shortterm Memory Dependent Bidirectional Recurrent Neural Network Summary work investigate memory capability recurrent neural network RNNs capability defined function map element sequence current output first analyze system function recurrent neural network RNN cell provide analytical result three R... | [0.016818039119243622, -0.02590359002351761, -0.006911926437169313, 0.06631135195493698, -0.03752010688185692, -0.009794680401682854, 0.015172813087701797, -0.020624857395887375, 0.007288065273314714, -0.04691636934876442, -0.024906670674681664, -0.0552426353096962, 0.013229344971477985, 0.04268839582800865, 0.03988541... |
123 | 123 | ['Lin Ma', 'Zhengdong Lu', 'Hang Li'] | 1506.00333v2 | In this paper, we propose to employ the convolutional neural network (CNN)
for the image question answering (QA). Our proposed CNN provides an end-to-end
framework with convolutional architectures for learning not only the image and
question representations, but also their inter-modal interactions to produce
the answer... | Learning to Answer Questions From Image Using Convolutional Neural
Network | 2,015 | http://arxiv.org/pdf/1506.00333v2 | Title Learning Answer Questions Image Using Convolutional Neural Network Summary paper propose employ convolutional neural network CNN image question answering QA proposed CNN provides endtoend framework convolutional architecture learning image question representation also intermodal interaction produce answer specifi... | [0.0839354544878006, 0.060329996049404144, -0.008508297614753246, 0.08565450459718704, -0.015195291489362717, 0.00968173984438181, 0.025598539039492607, 0.0436222068965435, 0.002900107530876994, -0.03461875766515732, 0.015335976146161556, 0.011785490438342094, -0.03949568793177605, 0.04100176692008972, 0.01872228458523... |
124 | 124 | ['Zichao Yang', 'Xiaodong He', 'Jianfeng Gao', 'Li Deng', 'Alex Smola'] | 1511.02274v2 | This paper presents stacked attention networks (SANs) that learn to answer
natural language questions from images. SANs use semantic representation of a
question as query to search for the regions in an image that are related to the
answer. We argue that image question answering (QA) often requires multiple
steps of re... | Stacked Attention Networks for Image Question Answering | 2,015 | http://arxiv.org/pdf/1511.02274v2 | Title Stacked Attention Networks Image Question Answering Summary paper present stacked attention network SANs learn answer natural language question image SANs use semantic representation question query search region image related answer argue image question answering QA often requires multiple step reasoning Thus dev... | [-0.0011845617555081844, 0.009816315956413746, -0.022241875529289246, 0.03702593967318535, -0.0039842999540269375, -0.007120921742171049, 0.021945452317595482, -0.006195677910000086, 0.010406684130430222, -0.05124964937567711, -0.006271982099860907, 0.05718287080526352, -0.013278278522193432, 0.03345341607928276, 0.033... |
125 | 125 | ['Jacob Andreas', 'Marcus Rohrbach', 'Trevor Darrell', 'Dan Klein'] | 1511.02799v4 | Visual question answering is fundamentally compositional in nature---a
question like "where is the dog?" shares substructure with questions like "what
color is the dog?" and "where is the cat?" This paper seeks to simultaneously
exploit the representational capacity of deep networks and the compositional
linguistic str... | Neural Module Networks | 2,015 | http://arxiv.org/pdf/1511.02799v4 | Title Neural Module Networks Summary Visual question answering fundamentally compositional naturea question like dog share substructure question like color dog cat paper seek simultaneously exploit representational capacity deep network compositional linguistic structure question describe procedure constructing learnin... | [0.03032113052904606, 0.04285570979118347, -0.05058463290333748, 0.021224964410066605, -0.01112926285713911, 0.04416812211275101, 0.020102521404623985, -0.005148149561136961, -0.01478614378720522, -0.02363499067723751, -0.027765372768044472, 0.009290046989917755, -0.015773294493556023, 0.03814404830336571, 0.0231920648... |
126 | 126 | ['Federico Raue', 'Andreas Dengel', 'Thomas M. Breuel', 'Marcus Liwicki'] | 1511.04401v5 | In this paper, we extend a symbolic association framework for being able to
handle missing elements in multimodal sequences. The general scope of the work
is the symbolic associations of object-word mappings as it happens in language
development in infants. In other words, two different representations of the
same abst... | Symbol Grounding Association in Multimodal Sequences with Missing
Elements | 2,015 | http://arxiv.org/pdf/1511.04401v5 | Title Symbol Grounding Association Multimodal Sequences Missing Elements Summary paper extend symbolic association framework able handle missing element multimodal sequence general scope work symbolic association objectword mapping happens language development infant word two different representation abstract concept a... | [-0.00641487305983901, 0.02614639326930046, -0.01266532950103283, 0.02603224106132984, -0.014365623705089092, 0.02258208394050598, 0.0633237436413765, -0.0059031276032328606, -0.040016911923885345, -0.03117685206234455, -0.019343946129083633, -0.042571887373924255, 0.048582714051008224, 0.028821134939789772, 0.01439730... |
127 | 127 | ['Dan Hendrycks', 'Mantas Mazeika', 'Duncan Wilson', 'Kevin Gimpel'] | 1802.05300v1 | The growing importance of massive datasets with the advent of deep learning
makes robustness to label noise a critical property for classifiers to have.
Sources of label noise include automatic labeling for large datasets,
non-expert labeling, and label corruption by data poisoning adversaries. In the
latter case, corr... | Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe
Noise | 2,018 | http://arxiv.org/pdf/1802.05300v1 | Title Using Trusted Data Train Deep Networks Labels Corrupted Severe Noise Summary growing importance massive datasets advent deep learning make robustness label noise critical property classifier Sources label noise include automatic labeling large datasets nonexpert labeling label corruption data poisoning adversary ... | [0.0499345101416111, 0.06746858358383179, 0.0007473518489859998, 0.04109011963009834, -0.011891260743141174, -0.01363668404519558, 0.04493304342031479, 0.00044890728895552456, 0.008615335449576378, -0.03051946870982647, -0.03643353655934334, 0.005021167453378439, 0.010708912275731564, 0.0793510153889656, 0.009104130789... |
128 | 128 | ['Kyunghyun Cho', 'Aaron Courville', 'Yoshua Bengio'] | 1507.01053v1 | Whereas deep neural networks were first mostly used for classification tasks,
they are rapidly expanding in the realm of structured output problems, where
the observed target is composed of multiple random variables that have a rich
joint distribution, given the input. We focus in this paper on the case where
the input... | Describing Multimedia Content using Attention-based Encoder--Decoder
Networks | 2,015 | http://arxiv.org/pdf/1507.01053v1 | Title Describing Multimedia Content using Attentionbased EncoderDecoder Networks Summary Whereas deep neural network first mostly used classification task rapidly expanding realm structured output problem observed target composed multiple random variable rich joint distribution given input focus paper case input also r... | [0.020975526422262192, -0.010257728397846222, -0.0024284517858177423, 0.04366829991340637, 0.0024071575608104467, -0.023577744141221046, 0.032206058502197266, -0.036669131368398666, -0.06916859745979309, -0.06539066880941391, -0.032877132296562195, -0.012988226488232613, 0.014521989040076733, 0.09337498992681503, 0.025... |
129 | 129 | ['Desmond Elliott', 'Stella Frank', 'Eva Hasler'] | 1510.04709v2 | In this paper we present an approach to multi-language image description
bringing together insights from neural machine translation and neural image
description. To create a description of an image for a given target language,
our sequence generation models condition on feature vectors from the image, the
description f... | Multilingual Image Description with Neural Sequence Models | 2,015 | http://arxiv.org/pdf/1510.04709v2 | Title Multilingual Image Description Neural Sequence Models Summary paper present approach multilanguage image description bringing together insight neural machine translation neural image description create description image given target language sequence generation model condition feature vector image description sou... | [0.03873835876584053, 0.03974294289946556, 0.011122329160571098, 0.07812575995922089, -0.030448313802480698, 0.031966615468263626, 0.01927098073065281, -0.019631776958703995, -0.045805588364601135, -0.03218648210167885, -0.026834923774003983, -0.060991425067186356, 0.032955825328826904, 0.025300851091742516, 0.03094795... |
130 | 130 | ['Oswaldo Ludwig', 'Xiao Liu', 'Parisa Kordjamshidi', 'Marie-Francine Moens'] | 1603.08474v1 | This paper introduces the visually informed embedding of word (VIEW), a
continuous vector representation for a word extracted from a deep neural model
trained using the Microsoft COCO data set to forecast the spatial arrangements
between visual objects, given a textual description. The model is composed of a
deep multi... | Deep Embedding for Spatial Role Labeling | 2,016 | http://arxiv.org/pdf/1603.08474v1 | Title Deep Embedding Spatial Role Labeling Summary paper introduces visually informed embedding word VIEW continuous vector representation word extracted deep neural model trained using Microsoft COCO data set forecast spatial arrangement visual object given textual description model composed deep multilayer perceptron... | [0.049278587102890015, 0.006925229914486408, -0.00891056563705206, 0.05675702169537544, -0.02547542192041874, 0.002801428083330393, 0.013896778225898743, -0.05079101771116257, -0.00027351450989954174, -0.03531043604016304, -0.017839377745985985, 0.005415066611021757, 0.013618972152471542, 0.030895709991455078, 0.000100... |
131 | 131 | ['Yuntian Deng', 'Anssi Kanervisto', 'Jeffrey Ling', 'Alexander M. Rush'] | 1609.04938v2 | We present a neural encoder-decoder model to convert images into
presentational markup based on a scalable coarse-to-fine attention mechanism.
Our method is evaluated in the context of image-to-LaTeX generation, and we
introduce a new dataset of real-world rendered mathematical expressions paired
with LaTeX markup. We ... | Image-to-Markup Generation with Coarse-to-Fine Attention | 2,016 | http://arxiv.org/pdf/1609.04938v2 | Title ImagetoMarkup Generation CoarsetoFine Attention Summary present neural encoderdecoder model convert image presentational markup based scalable coarsetofine attention mechanism method evaluated context imagetoLaTeX generation introduce new dataset realworld rendered mathematical expression paired LaTeX markup show... | [-0.037048403173685074, 0.06474770605564117, 0.023353565484285355, 0.04908118024468422, 0.0032489988952875137, -0.007392432540655136, 0.045315228402614594, 0.01213331799954176, -0.050541844218969345, -0.025739675387740135, -0.02434820681810379, 0.039918601512908936, 0.017113251611590385, 0.07616622745990753, 0.04478913... |
132 | 132 | ['Sumeet S. Singh'] | 1802.05415v1 | We present a deep recurrent neural network model with soft visual attention
that learns to generate LaTeX markup of real-world math formulas given their
images. Applying neural sequence generation techniques that have been very
successful in the fields of machine translation and image/handwriting/speech
captioning, rec... | Teaching Machines to Code: Neural Markup Generation with Visual
Attention | 2,018 | http://arxiv.org/pdf/1802.05415v1 | Title Teaching Machines Code Neural Markup Generation Visual Attention Summary present deep recurrent neural network model soft visual attention learns generate LaTeX markup realworld math formula given image Applying neural sequence generation technique successful field machine translation imagehandwritingspeech capti... | [0.01412122044712305, 0.043919309973716736, -0.006231324747204781, 0.030719632282853127, 0.0054451823234558105, 0.0015950931701809168, 0.04878579080104828, -0.0071022603660821915, -0.03350820392370224, -0.03550293669104576, -0.004475708119571209, -0.005813692230731249, 0.01067198533564806, 0.10894140601158142, 0.061574... |
133 | 133 | ['Mohammad Javad Shafiee', 'Elnaz Barshan', 'Alexander Wong'] | 1704.02081v1 | A promising paradigm for achieving highly efficient deep neural networks is
the idea of evolutionary deep intelligence, which mimics biological evolution
processes to progressively synthesize more efficient networks. A crucial design
factor in evolutionary deep intelligence is the genetic encoding scheme used to
simula... | Evolution in Groups: A deeper look at synaptic cluster driven evolution
of deep neural networks | 2,017 | http://arxiv.org/pdf/1704.02081v1 | Title Evolution Groups deeper look synaptic cluster driven evolution deep neural network Summary promising paradigm achieving highly efficient deep neural network idea evolutionary deep intelligence mimic biological evolution process progressively synthesize efficient network crucial design factor evolutionary deep int... | [-0.02147897146642208, 0.025692127645015717, -0.054428599774837494, 0.05017568916082382, 0.0025389394722878933, 0.005992465186864138, 0.030231373384594917, 0.007615628186613321, -0.023921435698866844, 0.016216987743973732, -0.02581300400197506, 0.017828235402703285, -0.03593451902270317, 0.02337166853249073, 0.03265490... |
134 | 134 | ['Mete Ozay', 'Ilke Öztekin', 'Uygar Öztekin', 'Fatos T. Yarman Vural'] | 1205.2382v3 | A relatively recent advance in cognitive neuroscience has been multi-voxel
pattern analysis (MVPA), which enables researchers to decode brain states
and/or the type of information represented in the brain during a cognitive
operation. MVPA methods utilize machine learning algorithms to distinguish
among types of inform... | Mesh Learning for Classifying Cognitive Processes | 2,012 | http://arxiv.org/pdf/1205.2382v3 | Title Mesh Learning Classifying Cognitive Processes Summary relatively recent advance cognitive neuroscience multivoxel pattern analysis MVPA enables researcher decode brain state andor type information represented brain cognitive operation MVPA method utilize machine learning algorithm distinguish among type informati... | [-0.031485315412282944, 0.019229529425501823, -0.054886896163225174, 0.0016190819442272186, 0.0027205513324588537, 0.006775710731744766, 0.036257434636354446, 0.023961171507835388, 0.04668120667338371, 0.028998807072639465, -0.01394672505557537, 0.017376072704792023, 0.080603688955307, 0.06615357846021652, 0.0436157360... |
135 | 135 | ['A. H. Karimi', 'M. J. Shafiee', 'A. Ghodsi', 'A. Wong'] | 1707.00081v1 | In this work, we perform an exploratory study on synthesizing deep neural
networks using biological synaptic strength distributions, and the potential
influence of different distributions on modelling performance particularly for
the scenario associated with small data sets. Surprisingly, a CNN with
convolutional layer... | Synthesizing Deep Neural Network Architectures using Biological Synaptic
Strength Distributions | 2,017 | http://arxiv.org/pdf/1707.00081v1 | Title Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions Summary work perform exploratory study synthesizing deep neural network using biological synaptic strength distribution potential influence different distribution modelling performance particularly scenario associated ... | [0.010573148727416992, -0.008800351992249489, -0.02899429388344288, 0.0047276862896978855, -0.000527168158441782, -0.030559904873371124, 0.014191307127475739, -0.027332350611686707, -0.02529105916619301, 0.03455287218093872, -0.05245533585548401, -0.017583083361387253, 0.025366928428411484, 0.03453656658530235, 0.03021... |
136 | 136 | ['Yukun Bao', 'Zhongyi Hu', 'Tao Xiong'] | 1401.1926v1 | Addressing the issue of SVMs parameters optimization, this study proposes an
efficient memetic algorithm based on Particle Swarm Optimization algorithm
(PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is
responsible for exploration of the search space and the detection of the
potential regions with... | A PSO and Pattern Search based Memetic Algorithm for SVMs Parameters
Optimization | 2,014 | http://arxiv.org/pdf/1401.1926v1 | Title PSO Pattern Search based Memetic Algorithm SVMs Parameters Optimization Summary Addressing issue SVMs parameter optimization study proposes efficient memetic algorithm based Particle Swarm Optimization algorithm PSO Pattern Search PS proposed memetic algorithm PSO responsible exploration search space detection po... | [-0.003677141619846225, -0.035738736391067505, -0.04013725370168686, -0.03242357820272446, 0.030800711363554, -0.039950333535671234, -0.01886816881597042, 0.005230278708040714, -0.042481835931539536, 0.024726781994104385, 0.012441267259418964, 0.03372093662619591, 0.02127053588628769, 0.031095923855900764, 0.0043699624... |
137 | 137 | ['Laurent Dinh', 'Jascha Sohl-Dickstein', 'Samy Bengio'] | 1605.08803v3 | Unsupervised learning of probabilistic models is a central yet challenging
problem in machine learning. Specifically, designing models with tractable
learning, sampling, inference and evaluation is crucial in solving this task.
We extend the space of such models using real-valued non-volume preserving
(real NVP) transf... | Density estimation using Real NVP | 2,016 | http://arxiv.org/pdf/1605.08803v3 | Title Density estimation using Real NVP Summary Unsupervised learning probabilistic model central yet challenging problem machine learning Specifically designing model tractable learning sampling inference evaluation crucial solving task extend space model using realvalued nonvolume preserving real NVP transformation s... | [-0.010649830102920532, 0.02311602793633938, -0.020355718210339546, -0.005554623901844025, 0.0006417612312361598, -0.028737174347043037, -0.010901322588324547, -0.01590411737561226, -0.1122327521443367, 0.03075490891933441, 0.037034448236227036, 0.010051854886114597, -0.011723372153937817, 0.07618006318807602, 0.031419... |
138 | 138 | ['Tim Salimans', 'Jonathan Ho', 'Xi Chen', 'Szymon Sidor', 'Ilya Sutskever'] | 1703.03864v2 | We explore the use of Evolution Strategies (ES), a class of black box
optimization algorithms, as an alternative to popular MDP-based RL techniques
such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show
that ES is a viable solution strategy that scales extremely well with the
number of CPUs avail... | Evolution Strategies as a Scalable Alternative to Reinforcement Learning | 2,017 | http://arxiv.org/pdf/1703.03864v2 | Title Evolution Strategies Scalable Alternative Reinforcement Learning Summary explore use Evolution Strategies ES class black box optimization algorithm alternative popular MDPbased RL technique Qlearning Policy Gradients Experiments MuJoCo Atari show ES viable solution strategy scale extremely well number CPUs availa... | [0.010028883814811707, -0.013128409162163734, -0.026057640090584755, -0.030221175402402878, 0.004878659266978502, -0.014877968467772007, -0.030878612771630287, -0.0016068447148427367, -0.03687499091029167, 0.013204746879637241, -0.025796733796596527, 0.041408758610486984, -0.018586693331599236, 0.0671517550945282, 0.02... |
139 | 139 | ['Peter Karkus', 'David Hsu', 'Wee Sun Lee'] | 1703.06692v3 | This paper introduces the QMDP-net, a neural network architecture for
planning under partial observability. The QMDP-net combines the strengths of
model-free learning and model-based planning. It is a recurrent policy network,
but it represents a policy for a parameterized set of tasks by connecting a
model with a plan... | QMDP-Net: Deep Learning for Planning under Partial Observability | 2,017 | http://arxiv.org/pdf/1703.06692v3 | Title QMDPNet Deep Learning Planning Partial Observability Summary paper introduces QMDPnet neural network architecture planning partial observability QMDPnet combine strength modelfree learning modelbased planning recurrent policy network represents policy parameterized set task connecting model planning algorithm sol... | [-0.031025972217321396, 0.02668103389441967, 0.021649088710546494, -0.015323212370276451, -0.0014813239686191082, -0.016121603548526764, 0.00402535405009985, -0.032107558101415634, -0.03283702954649925, 0.008134311065077782, -0.0023769207764416933, 0.04723343253135681, -0.0532427579164505, 0.05191734433174133, -0.00111... |
140 | 140 | ['Gregory Farquhar', 'Tim Rocktäschel', 'Maximilian Igl', 'Shimon Whiteson'] | 1710.11417v2 | Combining deep model-free reinforcement learning with on-line planning is a
promising approach to building on the successes of deep RL. On-line planning
with look-ahead trees has proven successful in environments where transition
models are known a priori. However, in complex environments where transition
models need t... | TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep
Reinforcement Learning | 2,017 | http://arxiv.org/pdf/1710.11417v2 | Title TreeQN ATreeC Differentiable TreeStructured Models Deep Reinforcement Learning Summary Combining deep modelfree reinforcement learning online planning promising approach building success deep RL Online planning lookahead tree proven successful environment transition model known priori However complex environment ... | [-0.01316402293741703, 0.05500173568725586, -0.01709579862654209, -0.011896061711013317, 0.011172867380082607, -0.028798716142773628, -0.027678873389959335, -0.033178865909576416, -0.0897672027349472, 0.03314647823572159, -0.01905691996216774, 0.04594454914331436, -0.013976276852190495, 0.09259748458862305, -0.02823040... |
141 | 141 | ['Nan Rosemary Ke', 'Anirudh Goyal', 'Olexa Bilaniuk', 'Jonathan Binas', 'Laurent Charlin', 'Chris Pal', 'Yoshua Bengio'] | 1711.02326v1 | A major drawback of backpropagation through time (BPTT) is the difficulty of
learning long-term dependencies, coming from having to propagate credit
information backwards through every single step of the forward computation.
This makes BPTT both computationally impractical and biologically implausible.
For this reason,... | Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent
Networks | 2,017 | http://arxiv.org/pdf/1711.02326v1 | Title Sparse Attentive Backtracking LongRange Credit Assignment Recurrent Networks Summary major drawback backpropagation time BPTT difficulty learning longterm dependency coming propagate credit information backwards every single step forward computation make BPTT computationally impractical biologically implausible r... | [0.029247580096125603, 0.01919320784509182, 0.0024480000138282776, 8.124144369503483e-05, 0.0074616107158362865, -0.012341553345322609, -0.01701655425131321, 0.028148027136921883, 0.015285437926650047, -0.01433069258928299, -0.00913965329527855, -0.04621945321559906, 0.04792526364326477, -0.0030442881397902966, 0.04588... |
142 | 142 | ['Anakha V Babu', 'Bipin Rajendran'] | 1711.03640v1 | We study the performance of stochastically trained deep neural networks
(DNNs) whose synaptic weights are implemented using emerging memristive devices
that exhibit limited dynamic range, resolution, and variability in their
programming characteristics. We show that a key device parameter to optimize
the learning effic... | Stochastic Deep Learning in Memristive Networks | 2,017 | http://arxiv.org/pdf/1711.03640v1 | Title Stochastic Deep Learning Memristive Networks Summary study performance stochastically trained deep neural network DNNs whose synaptic weight implemented using emerging memristive device exhibit limited dynamic range resolution variability programming characteristic show key device parameter optimize learning effi... | [-0.05314948037266731, -0.009155993349850178, -0.050157349556684494, 0.04872630164027214, 0.005095598287880421, -0.02518356591463089, 0.0003099807072430849, -0.05767659470438957, 0.002156688831746578, 0.01658071018755436, -0.03266753628849983, -0.05126240849494934, -0.011261051520705223, 0.06419191509485245, 0.02200341... |
143 | 143 | ['Yukun Bao', 'Tao Xiong', 'Zhongyi Hu'] | 1401.0104v1 | Multi-step-ahead time series prediction is one of the most challenging
research topics in the field of time series modeling and prediction, and is
continually under research. Recently, the multiple-input several
multiple-outputs (MISMO) modeling strategy has been proposed as a promising
alternative for multi-step-ahead... | PSO-MISMO Modeling Strategy for Multi-Step-Ahead Time Series Prediction | 2,013 | http://arxiv.org/pdf/1401.0104v1 | Title PSOMISMO Modeling Strategy MultiStepAhead Time Series Prediction Summary Multistepahead time series prediction one challenging research topic field time series modeling prediction continually research Recently multipleinput several multipleoutputs MISMO modeling strategy proposed promising alternative multistepah... | [-0.03915258124470711, -0.0018047576304525137, -0.037946537137031555, -0.029163600876927376, 0.023922298103570938, -0.010559935122728348, 0.015669895336031914, -0.0034794695675373077, -0.0033809870947152376, 0.021691443398594856, 0.01624354161322117, -0.0034497908782213926, 0.016113724559545517, 0.02761148288846016, 0.... |
144 | 144 | ['Behnam Neyshabur', 'Ryota Tomioka', 'Nathan Srebro'] | 1503.00036v2 | We investigate the capacity, convexity and characterization of a general
family of norm-constrained feed-forward networks. | Norm-Based Capacity Control in Neural Networks | 2,015 | http://arxiv.org/pdf/1503.00036v2 | Title NormBased Capacity Control Neural Networks Summary investigate capacity convexity characterization general family normconstrained feedforward network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4... | [-0.02022957056760788, 0.012715630233287811, -0.04774617776274681, 0.01465693674981594, -0.021458376199007034, -0.05353955924510956, 0.008180944249033928, -0.01536419428884983, -0.03482271730899811, 0.057900186628103256, -0.042462870478630066, -0.06971564888954163, -0.030261939391493797, 0.09634757786989212, 0.03070682... |
145 | 145 | ['Konrad Zolna'] | 1612.01589v1 | The method presented extends a given regression neural network to make its
performance improve. The modification affects the learning procedure only,
hence the extension may be easily omitted during evaluation without any change
in prediction. It means that the modified model may be evaluated as quickly as
the original... | Improving the Performance of Neural Networks in Regression Tasks Using
Drawering | 2,016 | http://arxiv.org/pdf/1612.01589v1 | Title Improving Performance Neural Networks Regression Tasks Using Drawering Summary method presented extends given regression neural network make performance improve modification affect learning procedure hence extension may easily omitted evaluation without change prediction mean modified model may evaluated quickly ... | [0.00877348706126213, 0.0447792187333107, -0.028045685961842537, 0.007741921115666628, 0.001329071819782257, -0.01616915501654148, 0.04684789851307869, 0.024029122665524483, -0.03844287246465683, 0.011336134746670723, -0.013169636949896812, 0.04185344651341438, 0.03981006518006325, 0.0020347849931567907, 0.047180231660... |
146 | 146 | ['Yujia Li', 'Kevin Swersky', 'Richard Zemel'] | 1412.5244v1 | A key element in transfer learning is representation learning; if
representations can be developed that expose the relevant factors underlying
the data, then new tasks and domains can be learned readily based on mappings
of these salient factors. We propose that an important aim for these
representations are to be unbi... | Learning unbiased features | 2,014 | http://arxiv.org/pdf/1412.5244v1 | Title Learning unbiased feature Summary key element transfer learning representation learning representation developed expose relevant factor underlying data new task domain learned readily based mapping salient factor propose important aim representation unbiased Different form representation learning derived alternat... | [-0.021625731140375137, -0.013297004625201225, -0.03595166280865669, 0.02415221743285656, -0.0011171476216986775, -0.00457181828096509, 0.07256867736577988, -0.016797970980405807, -0.022556159645318985, -0.04133708029985428, -0.030532395467162132, 0.027638962492346764, -0.00877831969410181, 0.08131878077983856, 0.01382... |
147 | 147 | ['David Balduzzi', 'Muhammad Ghifary'] | 1509.03005v1 | This paper proposes GProp, a deep reinforcement learning algorithm for
continuous policies with compatible function approximation. The algorithm is
based on two innovations. Firstly, we present a temporal-difference based
method for learning the gradient of the value-function. Secondly, we present
the deviator-actor-cr... | Compatible Value Gradients for Reinforcement Learning of Continuous Deep
Policies | 2,015 | http://arxiv.org/pdf/1509.03005v1 | Title Compatible Value Gradients Reinforcement Learning Continuous Deep Policies Summary paper proposes GProp deep reinforcement learning algorithm continuous policy compatible function approximation algorithm based two innovation Firstly present temporaldifference based method learning gradient valuefunction Secondly ... | [-0.0199118759483099, 0.019877638667821884, -0.012061123736202717, -0.004727119579911232, 0.021368667483329773, -0.03343823552131653, 0.021761588752269745, -0.025325322523713112, -0.034446652978658676, 0.048284150660037994, -0.047001712024211884, 0.019318830221891403, 0.004566787742078304, 0.07806970924139023, -0.01044... |
148 | 148 | ['Takayuki Osogami', 'Makoto Otsuka'] | 1509.08634v1 | We propose a particularly structured Boltzmann machine, which we refer to as
a dynamic Boltzmann machine (DyBM), as a stochastic model of a
multi-dimensional time-series. The DyBM can have infinitely many layers of
units but allows exact and efficient inference and learning when its parameters
have a proposed structure... | Learning dynamic Boltzmann machines with spike-timing dependent
plasticity | 2,015 | http://arxiv.org/pdf/1509.08634v1 | Title Learning dynamic Boltzmann machine spiketiming dependent plasticity Summary propose particularly structured Boltzmann machine refer dynamic Boltzmann machine DyBM stochastic model multidimensional timeseries DyBM infinitely many layer unit allows exact efficient inference learning parameter proposed structure pro... | [-0.023196227848529816, -0.07988418638706207, -0.03992109000682831, 0.012567665427923203, 0.015530785545706749, 0.014076362363994122, 0.028746625408530235, -0.01719609461724758, -0.006394918542355299, 0.04103895276784897, 0.008705190382897854, 0.029434477910399437, 0.0034950783010572195, 0.06274043768644333, 0.03900062... |
149 | 149 | ['Yujia Li', 'Daniel Tarlow', 'Marc Brockschmidt', 'Richard Zemel'] | 1511.05493v4 | Graph-structured data appears frequently in domains including chemistry,
natural language semantics, social networks, and knowledge bases. In this work,
we study feature learning techniques for graph-structured inputs. Our starting
point is previous work on Graph Neural Networks (Scarselli et al., 2009), which
we modif... | Gated Graph Sequence Neural Networks | 2,015 | http://arxiv.org/pdf/1511.05493v4 | Title Gated Graph Sequence Neural Networks Summary Graphstructured data appears frequently domain including chemistry natural language semantics social network knowledge base work study feature learning technique graphstructured input starting point previous work Graph Neural Networks Scarselli et al 2009 modify use ga... | [0.004501670598983765, 0.04923496022820473, 0.006892047822475433, 0.03186250478029251, -0.04291075840592384, -0.03801732137799263, 0.0056146360002458096, 0.047856032848358154, 0.07702115923166275, -0.02041093073785305, 0.029697367921471596, -0.00595736363902688, -0.0017703132471069694, 0.09651996940374374, 0.0196350105... |
150 | 150 | ['Gabriel Dulac-Arnold', 'Richard Evans', 'Hado van Hasselt', 'Peter Sunehag', 'Timothy Lillicrap', 'Jonathan Hunt', 'Timothy Mann', 'Theophane Weber', 'Thomas Degris', 'Ben Coppin'] | 1512.07679v2 | Being able to reason in an environment with a large number of discrete
actions is essential to bringing reinforcement learning to a larger class of
problems. Recommender systems, industrial plants and language models are only
some of the many real-world tasks involving large numbers of discrete actions
for which curren... | Deep Reinforcement Learning in Large Discrete Action Spaces | 2,015 | http://arxiv.org/pdf/1512.07679v2 | Title Deep Reinforcement Learning Large Discrete Action Spaces Summary able reason environment large number discrete action essential bringing reinforcement learning larger class problem Recommender system industrial plant language model many realworld task involving large number discrete action current method difficul... | [0.0021167639642953873, 0.0476946122944355, -0.014139327220618725, -0.011740886606276035, 0.005617568269371986, -0.009041255339980125, 0.04904504492878914, -0.0007096983026713133, 0.0023235927801579237, -0.004593364428728819, -0.019344856962561607, 0.009114304557442665, -0.09643930196762085, 0.08340571820735931, 0.0100... |
151 | 151 | ['Aviv Tamar', 'Yi Wu', 'Garrett Thomas', 'Sergey Levine', 'Pieter Abbeel'] | 1602.02867v4 | We introduce the value iteration network (VIN): a fully differentiable neural
network with a `planning module' embedded within. VINs can learn to plan, and
are suitable for predicting outcomes that involve planning-based reasoning,
such as policies for reinforcement learning. Key to our approach is a novel
differentiab... | Value Iteration Networks | 2,016 | http://arxiv.org/pdf/1602.02867v4 | Title Value Iteration Networks Summary introduce value iteration network VIN fully differentiable neural network planning module embedded within VINs learn plan suitable predicting outcome involve planningbased reasoning policy reinforcement learning Key approach novel differentiable approximation valueiteration algori... | [0.004493028856813908, 0.019453197717666626, -0.03255758062005043, -0.01281800027936697, -0.006495372857898474, -0.06049295514822006, 0.009518175385892391, -0.04015645384788513, -0.07157497107982635, 0.05011313036084175, 0.04419944807887077, 0.05142959579825401, -0.02482539974153042, 0.11494502425193787, -0.00948524288... |
152 | 152 | ['Mikael Henaff', 'Arthur Szlam', 'Yann LeCun'] | 1602.06662v2 | Although RNNs have been shown to be powerful tools for processing sequential
data, finding architectures or optimization strategies that allow them to model
very long term dependencies is still an active area of research. In this work,
we carefully analyze two synthetic datasets originally outlined in (Hochreiter
and S... | Recurrent Orthogonal Networks and Long-Memory Tasks | 2,016 | http://arxiv.org/pdf/1602.06662v2 | Title Recurrent Orthogonal Networks LongMemory Tasks Summary Although RNNs shown powerful tool processing sequential data finding architecture optimization strategy allow model long term dependency still active area research work carefully analyze two synthetic datasets originally outlined Hochreiter Schmidhuber 1997 u... | [-0.01747208833694458, 0.08482013642787933, -0.0024121911264955997, 0.022803330793976784, -0.014167627319693565, -0.023958789184689522, -0.001385111128911376, -0.005846528336405754, -0.018839579075574875, -0.0077763148583471775, -0.0014658243162557483, -0.020521823316812515, 0.025712769478559494, -0.02004188299179077, ... |
153 | 153 | ['Hado van Hasselt', 'Arthur Guez', 'Matteo Hessel', 'Volodymyr Mnih', 'David Silver'] | 1602.07714v2 | Most learning algorithms are not invariant to the scale of the function that
is being approximated. We propose to adaptively normalize the targets used in
learning. This is useful in value-based reinforcement learning, where the
magnitude of appropriate value approximations can change over time when we
update the polic... | Learning values across many orders of magnitude | 2,016 | http://arxiv.org/pdf/1602.07714v2 | Title Learning value across many order magnitude Summary learning algorithm invariant scale function approximated propose adaptively normalize target used learning useful valuebased reinforcement learning magnitude appropriate value approximation change time update policy behavior main motivation prior work learning pl... | [-0.04185480251908302, 0.030381247401237488, -0.02586919628083706, -0.039523981511592865, 0.020662624388933182, -0.030731257051229477, 0.021895112469792366, 0.008124059066176414, -0.060192834585905075, 0.04004546254873276, -0.04899836704134941, -0.0027776153292506933, -0.020518342033028603, 0.08556074649095535, -0.0272... |
154 | 154 | ['Laura Deming', 'Sasha Targ', 'Nate Sauder', 'Diogo Almeida', 'Chun Jimmie Ye'] | 1605.07156v1 | Each human genome is a 3 billion base pair set of encoding instructions.
Decoding the genome using deep learning fundamentally differs from most tasks,
as we do not know the full structure of the data and therefore cannot design
architectures to suit it. As such, architectures that fit the structure of
genomics should ... | Genetic Architect: Discovering Genomic Structure with Learned Neural
Architectures | 2,016 | http://arxiv.org/pdf/1605.07156v1 | Title Genetic Architect Discovering Genomic Structure Learned Neural Architectures Summary human genome 3 billion base pair set encoding instruction Decoding genome using deep learning fundamentally differs task know full structure data therefore cannot design architecture suit architecture fit structure genomics learn... | [-7.282174192368984e-05, 0.09117594361305237, -0.03623959422111511, -0.015610825270414352, 0.018734263256192207, 0.028670238330960274, 0.0044066994450986385, 0.042840730398893356, 0.004791349172592163, 0.0667874664068222, 0.024639450013637543, -0.021972354501485825, 0.0029261228628456593, 0.10818623006343842, -0.001823... |
155 | 155 | ['Tejas D. Kulkarni', 'Ardavan Saeedi', 'Simanta Gautam', 'Samuel J. Gershman'] | 1606.02396v1 | Learning robust value functions given raw observations and rewards is now
possible with model-free and model-based deep reinforcement learning
algorithms. There is a third alternative, called Successor Representations
(SR), which decomposes the value function into two components -- a reward
predictor and a successor ma... | Deep Successor Reinforcement Learning | 2,016 | http://arxiv.org/pdf/1606.02396v1 | Title Deep Successor Reinforcement Learning Summary Learning robust value function given raw observation reward possible modelfree modelbased deep reinforcement learning algorithm third alternative called Successor Representations SR decomposes value function two component reward predictor successor map successor map r... | [0.0047506894916296005, 0.06638247519731522, -0.007088123355060816, -0.003773239441215992, -0.016015956178307533, -0.013669737614691257, -0.018909061327576637, -0.028850967064499855, -0.03467404097318649, 0.043055251240730286, -0.01732652820646763, 0.039135873317718506, -0.025339452549815178, 0.052633967250585556, 0.01... |
156 | 156 | ['Yan Duan', 'John Schulman', 'Xi Chen', 'Peter L. Bartlett', 'Ilya Sutskever', 'Pieter Abbeel'] | 1611.02779v2 | Deep reinforcement learning (deep RL) has been successful in learning
sophisticated behaviors automatically; however, the learning process requires a
huge number of trials. In contrast, animals can learn new tasks in just a few
trials, benefiting from their prior knowledge about the world. This paper seeks
to bridge th... | RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning | 2,016 | http://arxiv.org/pdf/1611.02779v2 | Title RL2 Fast Reinforcement Learning via Slow Reinforcement Learning Summary Deep reinforcement learning deep RL successful learning sophisticated behavior automatically however learning process requires huge number trial contrast animal learn new task trial benefiting prior knowledge world paper seek bridge gap Rathe... | [0.029053961858153343, 0.031220439821481705, -0.022526610642671585, -0.0188052449375391, -0.01906392350792885, 0.006721537094563246, 0.002386258915066719, -0.00878199189901352, -0.03018711879849434, -0.015467081218957901, 0.002808040240779519, 0.021843677386641502, -0.016497164964675903, 0.053887419402599335, 0.0067304... |
157 | 157 | ['Jasmine Collins', 'Jascha Sohl-Dickstein', 'David Sussillo'] | 1611.09913v3 | Two potential bottlenecks on the expressiveness of recurrent neural networks
(RNNs) are their ability to store information about the task in their
parameters, and to store information about the input history in their units. We
show experimentally that all common RNN architectures achieve nearly the same
per-task and pe... | Capacity and Trainability in Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1611.09913v3 | Title Capacity Trainability Recurrent Neural Networks Summary Two potential bottleneck expressiveness recurrent neural network RNNs ability store information task parameter store information input history unit show experimentally common RNN architecture achieve nearly pertask perunit capacity bound careful training var... | [0.023925350978970528, 0.01983645185828209, 0.009100564755499363, 0.0378272607922554, -0.008427752181887627, -0.021360011771321297, 0.03861137479543686, -0.03598586469888687, -0.02964172326028347, -0.004948351997882128, -0.04585633799433708, -0.05377557873725891, 0.015707507729530334, 0.03036889061331749, 0.01282105408... |
158 | 158 | ['Mohammad Taha Bahadori', 'Krzysztof Chalupka', 'Edward Choi', 'Robert Chen', 'Walter F. Stewart', 'Jimeng Sun'] | 1702.02604v2 | In application domains such as healthcare, we want accurate predictive models
that are also causally interpretable. In pursuit of such models, we propose a
causal regularizer to steer predictive models towards causally-interpretable
solutions and theoretically study its properties. In a large-scale analysis of
Electron... | Causal Regularization | 2,017 | http://arxiv.org/pdf/1702.02604v2 | Title Causal Regularization Summary application domain healthcare want accurate predictive model also causally interpretable pursuit model propose causal regularizer steer predictive model towards causallyinterpretable solution theoretically study property largescale analysis Electronic Health Records EHR causallyregul... | [-0.02947417087852955, 0.08376093208789825, -0.018450533971190453, -0.04846975579857826, 0.028199339285492897, 0.00529453856870532, 0.01475520059466362, 0.037952154874801636, 0.017821740359067917, 0.05523843318223953, 0.08522357791662216, 0.033800411969423294, -0.02397766336798668, 0.05173458531498909, 0.02230899967253... |
159 | 159 | ['Dario Garcia-Gasulla', 'Ferran Parés', 'Armand Vilalta', 'Jonatan Moreno', 'Eduard Ayguadé', 'Jesús Labarta', 'Ulises Cortés', 'Toyotaro Suzumura'] | 1703.01127v4 | Deep neural networks are representation learning techniques. During training,
a deep net is capable of generating a descriptive language of unprecedented
size and detail in machine learning. Extracting the descriptive language coded
within a trained CNN model (in the case of image data), and reusing it for
other purpos... | On the Behavior of Convolutional Nets for Feature Extraction | 2,017 | http://arxiv.org/pdf/1703.01127v4 | Title Behavior Convolutional Nets Feature Extraction Summary Deep neural network representation learning technique training deep net capable generating descriptive language unprecedented size detail machine learning Extracting descriptive language coded within trained CNN model case image data reusing purpose field int... | [0.032952629029750824, 0.01611396297812462, -0.01034631859511137, 0.10258033126592636, -0.02511817403137684, 0.008014094084501266, 0.06135148927569389, 0.025373611599206924, -0.04846315085887909, -0.0306550282984972, -0.015558541752398014, 0.02333213947713375, -0.027563640847802162, 0.058921780437231064, 0.025731228291... |
160 | 160 | ['Aditya Grover', 'Manik Dhar', 'Stefano Ermon'] | 1705.08868v2 | Adversarial learning of probabilistic models has recently emerged as a
promising alternative to maximum likelihood. Implicit models such as generative
adversarial networks (GAN) often generate better samples compared to explicit
models trained by maximum likelihood. Yet, GANs sidestep the characterization
of an explici... | Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in
Generative Models | 2,017 | http://arxiv.org/pdf/1705.08868v2 | Title FlowGAN Combining Maximum Likelihood Adversarial Learning Generative Models Summary Adversarial learning probabilistic model recently emerged promising alternative maximum likelihood Implicit model generative adversarial network GAN often generate better sample compared explicit model trained maximum likelihood Y... | [-0.003950317390263081, 0.07688191533088684, 0.0009166643721982837, -0.004617008846253157, -0.0009049780201166868, -0.018247541040182114, 0.03817833214998245, -0.009871242567896843, -0.035942502319812775, 0.002297213999554515, -0.03915375843644142, -0.0024538380093872547, -0.005245716776698828, 0.07906213402748108, 0.0... |
161 | 161 | ['Chris J. Maddison', 'Dieterich Lawson', 'George Tucker', 'Nicolas Heess', 'Mohammad Norouzi', 'Andriy Mnih', 'Arnaud Doucet', 'Yee Whye Teh'] | 1705.09279v3 | When used as a surrogate objective for maximum likelihood estimation in
latent variable models, the evidence lower bound (ELBO) produces
state-of-the-art results. Inspired by this, we consider the extension of the
ELBO to a family of lower bounds defined by a particle filter's estimator of
the marginal likelihood, the ... | Filtering Variational Objectives | 2,017 | http://arxiv.org/pdf/1705.09279v3 | Title Filtering Variational Objectives Summary used surrogate objective maximum likelihood estimation latent variable model evidence lower bound ELBO produce stateoftheart result Inspired consider extension ELBO family lower bound defined particle filter estimator marginal likelihood filtering variational objective FIV... | [0.006874850019812584, 0.0401766411960125, 0.0023397665936499834, -0.037960510700941086, 0.028556382283568382, -0.05808863043785095, 0.02238357998430729, 0.009222697466611862, -0.05419144779443741, 0.01739583909511566, 0.027890967205166817, 0.025587838143110275, -0.010478954762220383, 0.09561523050069809, 0.00105245469... |
162 | 162 | ['Jiaxin Shi', 'Shengyang Sun', 'Jun Zhu'] | 1705.10119v3 | Recent progress in variational inference has paid much attention to the
flexibility of variational posteriors. One promising direction is to use
implicit distributions, i.e., distributions without tractable densities as the
variational posterior. However, existing methods on implicit posteriors still
face challenges of... | Kernel Implicit Variational Inference | 2,017 | http://arxiv.org/pdf/1705.10119v3 | Title Kernel Implicit Variational Inference Summary Recent progress variational inference paid much attention flexibility variational posterior One promising direction use implicit distribution ie distribution without tractable density variational posterior However existing method implicit posterior still face challeng... | [-0.010575110092759132, 0.08029486238956451, -0.028552521020174026, 0.009393423795700073, 0.014080696739256382, -0.04454711452126503, -0.006002450827509165, -0.004905517678707838, -0.012617061845958233, 0.03260224685072899, -0.00027383549604564905, 0.05595364049077034, 0.03977024555206299, 0.04732508212327957, 0.060321... |
163 | 163 | ['Julien Perez', 'Tomi Silander'] | 1705.10993v1 | Partially observable environments present an important open challenge in the
domain of sequential control learning with delayed rewards. Despite numerous
attempts during the two last decades, the majority of reinforcement learning
algorithms and associated approximate models, applied to this context, still
assume Marko... | Non-Markovian Control with Gated End-to-End Memory Policy Networks | 2,017 | http://arxiv.org/pdf/1705.10993v1 | Title NonMarkovian Control Gated EndtoEnd Memory Policy Networks Summary Partially observable environment present important open challenge domain sequential control learning delayed reward Despite numerous attempt two last decade majority reinforcement learning algorithm associated approximate model applied context sti... | [-0.024732718244194984, 0.01664862409234047, -0.02197991870343685, -0.03774814307689667, 0.022393692284822464, -0.009015173651278019, 0.01819654554128647, -0.02713029459118843, -0.014965947717428207, 0.0020225439220666885, -0.015060298144817352, 0.005750337149947882, -0.061916615813970566, 0.08552317321300507, 0.028598... |
164 | 164 | ['Emmanuel Dufourq', 'Bruce A. Bassett'] | 1707.00703v1 | Regression or classification? This is perhaps the most basic question faced
when tackling a new supervised learning problem. We present an Evolutionary
Deep Learning (EDL) algorithm that automatically solves this by identifying the
question type with high accuracy, along with a proposed deep architecture.
Typically, a ... | Automated Problem Identification: Regression vs Classification via
Evolutionary Deep Networks | 2,017 | http://arxiv.org/pdf/1707.00703v1 | Title Automated Problem Identification Regression v Classification via Evolutionary Deep Networks Summary Regression classification perhaps basic question faced tackling new supervised learning problem present Evolutionary Deep Learning EDL algorithm automatically solves identifying question type high accuracy along pr... | [0.017554648220539093, 0.059413518756628036, -0.04749822989106178, 0.021838361397385597, -0.031179143115878105, 0.04083777591586113, 0.023964963853359222, 0.014833181165158749, 0.0026751523837447166, -0.031123120337724686, 0.0407755970954895, 0.03238639608025551, -0.03210805729031563, 0.05949537083506584, 0.01950212195... |
165 | 165 | ['Nikhil Mishra', 'Mostafa Rohaninejad', 'Xi Chen', 'Pieter Abbeel'] | 1707.03141v3 | Deep neural networks excel in regimes with large amounts of data, but tend to
struggle when data is scarce or when they need to adapt quickly to changes in
the task. In response, recent work in meta-learning proposes training a
meta-learner on a distribution of similar tasks, in the hopes of generalization
to novel but... | A Simple Neural Attentive Meta-Learner | 2,017 | http://arxiv.org/pdf/1707.03141v3 | Title Simple Neural Attentive MetaLearner Summary Deep neural network excel regime large amount data tend struggle data scarce need adapt quickly change task response recent work metalearning proposes training metalearner distribution similar task hope generalization novel related task learning highlevel strategy captu... | [0.004009567201137543, 0.03183344751596451, -0.025987913832068443, -0.008682005107402802, 0.004264588467776775, -0.02678695321083069, 0.03491746261715889, -0.0019520559580996633, -0.0837555080652237, 0.006071198731660843, -0.02262096107006073, 0.020031025633215904, -0.023839369416236877, 0.03718545287847519, 0.04783983... |
166 | 166 | ['Simone Scardapane', 'Steven Van Vaerenbergh', 'Simone Totaro', 'Aurelio Uncini'] | 1707.04035v2 | Neural networks are generally built by interleaving (adaptable) linear layers
with (fixed) nonlinear activation functions. To increase their flexibility,
several authors have proposed methods for adapting the activation functions
themselves, endowing them with varying degrees of flexibility. None of these
approaches, h... | Kafnets: kernel-based non-parametric activation functions for neural
networks | 2,017 | http://arxiv.org/pdf/1707.04035v2 | Title Kafnets kernelbased nonparametric activation function neural network Summary Neural network generally built interleaving adaptable linear layer fixed nonlinear activation function increase flexibility several author proposed method adapting activation function endowing varying degree flexibility None approach how... | [-0.022498300299048424, 0.017312129959464073, -0.030839217826724052, 0.00999076571315527, 0.004893321078270674, -0.04123031347990036, 0.03281127288937569, -0.023878756910562515, -0.062344033271074295, 0.02721944823861122, -0.052044257521629333, 0.06830356270074844, -0.0011952181812375784, 0.08157125115394592, 0.0450752... |
167 | 167 | ['Razvan Pascanu', 'Yujia Li', 'Oriol Vinyals', 'Nicolas Heess', 'Lars Buesing', 'Sebastien Racanière', 'David Reichert', 'Théophane Weber', 'Daan Wierstra', 'Peter Battaglia'] | 1707.06170v1 | Conventional wisdom holds that model-based planning is a powerful approach to
sequential decision-making. It is often very challenging in practice, however,
because while a model can be used to evaluate a plan, it does not prescribe how
to construct a plan. Here we introduce the "Imagination-based Planner", the
first m... | Learning model-based planning from scratch | 2,017 | http://arxiv.org/pdf/1707.06170v1 | Title Learning modelbased planning scratch Summary Conventional wisdom hold modelbased planning powerful approach sequential decisionmaking often challenging practice however model used evaluate plan prescribe construct plan introduce Imaginationbased Planner first modelbased sequential decisionmaking agent learn const... | [0.006597284693270922, 0.017582206055521965, -0.01829913817346096, -0.030757684260606766, -0.02217869460582733, -0.004649126436561346, -0.02426075004041195, -0.014787039719522, -0.0075731598772108555, 0.007130926474928856, 0.03597297519445419, 0.03913571685552597, -0.04022463038563728, 0.1198381632566452, -0.0135048776... |
168 | 168 | ['Isabeau Prémont-Schwarz', 'Alexander Ilin', 'Tele Hotloo Hao', 'Antti Rasmus', 'Rinu Boney', 'Harri Valpola'] | 1707.09219v4 | We propose a recurrent extension of the Ladder networks whose structure is
motivated by the inference required in hierarchical latent variable models. We
demonstrate that the recurrent Ladder is able to handle a wide variety of
complex learning tasks that benefit from iterative inference and temporal
modeling. The arch... | Recurrent Ladder Networks | 2,017 | http://arxiv.org/pdf/1707.09219v4 | Title Recurrent Ladder Networks Summary propose recurrent extension Ladder network whose structure motivated inference required hierarchical latent variable model demonstrate recurrent Ladder able handle wide variety complex learning task benefit iterative inference temporal modeling architecture show closetooptimal re... | [0.008140220306813717, 0.0573250949382782, 0.0059646740555763245, 0.04990041255950928, 0.00830447394400835, 0.0008088753093034029, 0.01290297694504261, -0.02803797833621502, -0.05059698969125748, 0.03342720493674278, -0.022068766877055168, -0.007347153499722481, -0.00231430702842772, 0.031997282058000565, 0.04565506055... |
169 | 169 | ['Kenji Kawaguchi', 'Leslie Pack Kaelbling', 'Yoshua Bengio'] | 1710.05468v3 | With a direct analysis of neural networks, this paper presents a
mathematically tight generalization theory to partially address an open problem
regarding the generalization of deep learning. Unlike previous bound-based
theory, our main theory is quantitatively as tight as possible for every
dataset individually, while... | Generalization in Deep Learning | 2,017 | http://arxiv.org/pdf/1710.05468v3 | Title Generalization Deep Learning Summary direct analysis neural network paper present mathematically tight generalization theory partially address open problem regarding generalization deep learning Unlike previous boundbased theory main theory quantitatively tight possible every dataset individually producing qualit... | [-0.012876843102276325, 0.04441167414188385, -0.01813766546547413, 0.03599070385098457, 0.007772340439260006, -0.008717292919754982, 0.000206919910851866, -0.012016835622489452, -0.043246444314718246, 0.04879344627261162, 0.012480729259550571, -0.02803785540163517, -0.015330921858549118, 0.05843678116798401, 0.01337924... |
170 | 170 | ['Yannic Kilcher', 'Gary Becigneul', 'Thomas Hofmann'] | 1710.11386v1 | It is commonly agreed that the use of relevant invariances as a good
statistical bias is important in machine-learning. However, most approaches
that explicitly incorporate invariances into a model architecture only make use
of very simple transformations, such as translations and rotations. Hence,
there is a need for ... | Parametrizing filters of a CNN with a GAN | 2,017 | http://arxiv.org/pdf/1710.11386v1 | Title Parametrizing filter CNN GAN Summary commonly agreed use relevant invariance good statistical bias important machinelearning However approach explicitly incorporate invariance model architecture make use simple transformation translation rotation Hence need method model extract richer transformation capture much ... | [-0.018514923751354218, 0.07642007619142532, -0.01028684712946415, 0.010576027445495129, 0.019531575962901115, -0.013879798352718353, 0.008748224005103111, 0.00696737552061677, -0.10806851089000702, -0.002081956248730421, -0.0034765591844916344, 0.07079602032899857, -0.026015527546405792, 0.02715121954679489, 0.0681100... |
171 | 171 | ['Zhen He', 'Shaobing Gao', 'Liang Xiao', 'Daxue Liu', 'Hangen He', 'David Barber'] | 1711.01577v3 | Long Short-Term Memory (LSTM) is a popular approach to boosting the ability
of Recurrent Neural Networks to store longer term temporal information. The
capacity of an LSTM network can be increased by widening and adding layers.
However, usually the former introduces additional parameters, while the latter
increases the... | Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence
Learning | 2,017 | http://arxiv.org/pdf/1711.01577v3 | Title Wider Deeper Cheaper Faster Tensorized LSTMs Sequence Learning Summary Long ShortTerm Memory LSTM popular approach boosting ability Recurrent Neural Networks store longer term temporal information capacity LSTM network increased widening adding layer However usually former introduces additional parameter latter i... | [0.00562323397025466, 0.033965133130550385, -0.014653699472546577, 0.014243245124816895, -0.020695164799690247, 0.005932270083576441, 0.042821645736694336, 0.03192581981420517, 0.0015997419832274318, -0.037798233330249786, -0.031120603904128075, -0.05774397403001785, 0.03605860844254494, 0.02911956235766411, 0.00345828... |
172 | 172 | ['Shruti R. Kulkarni', 'John M. Alexiades', 'Bipin Rajendran'] | 1711.03637v1 | We describe a novel spiking neural network (SNN) for automated, real-time
handwritten digit classification and its implementation on a GP-GPU platform.
Information processing within the network, from feature extraction to
classification is implemented by mimicking the basic aspects of neuronal spike
initiation and prop... | Learning and Real-time Classification of Hand-written Digits With
Spiking Neural Networks | 2,017 | http://arxiv.org/pdf/1711.03637v1 | Title Learning Realtime Classification Handwritten Digits Spiking Neural Networks Summary describe novel spiking neural network SNN automated realtime handwritten digit classification implementation GPGPU platform Information processing within network feature extraction classification implemented mimicking basic aspect... | [-0.029836155474185944, -0.004212182946503162, -0.005331828258931637, 0.062443844974040985, 0.03699972480535507, 0.010561011731624603, 0.06665130704641342, 0.006106346379965544, 0.006240308750420809, -0.013324867002665997, -0.020448651164770126, 0.02570227161049843, 0.025979647412896156, 0.09828615933656693, 0.01205835... |
173 | 173 | ['Joan Serrà', 'Dídac Surís', 'Marius Miron', 'Alexandros Karatzoglou'] | 1801.01423v2 | Catastrophic forgetting occurs when a neural network loses the information
learned in a previous task after training on subsequent tasks. This problem
remains a hurdle for artificial intelligence systems with sequential learning
capabilities. In this paper, we propose a task-based hard attention mechanism
that preserve... | Overcoming catastrophic forgetting with hard attention to the task | 2,018 | http://arxiv.org/pdf/1801.01423v2 | Title Overcoming catastrophic forgetting hard attention task Summary Catastrophic forgetting occurs neural network loses information learned previous task training subsequent task problem remains hurdle artificial intelligence system sequential learning capability paper propose taskbased hard attention mechanism preser... | [-0.037000950425863266, 0.03462167829275131, -0.007812450174242258, -0.01077504362910986, 0.009100215509533882, 0.009886335581541061, -0.023790210485458374, -0.0192704014480114, 0.002923540771007538, 0.01397237554192543, -0.0008448531734757125, 0.0033932134974747896, -0.004014899488538504, 0.05124600976705551, 0.033668... |
174 | 174 | ['Zachary C. Lipton', 'Yu-Xiang Wang', 'Alex Smola'] | 1802.03916v2 | Faced with distribution shift between training and test set, we wish to
detect and quantify the shift, and to correct our classifiers without test set
labels. Motivated by medical diagnosis, where diseases (targets), cause
symptoms (observations), we focus on label shift, where the label marginal
$p(y)$ changes but the... | Detecting and Correcting for Label Shift with Black Box Predictors | 2,018 | http://arxiv.org/pdf/1802.03916v2 | Title Detecting Correcting Label Shift Black Box Predictors Summary Faced distribution shift training test set wish detect quantify shift correct classifier without test set label Motivated medical diagnosis disease target cause symptom observation focus label shift label marginal py change conditional pxy propose Blac... | [0.0030144392512738705, 0.03700340911746025, -0.009759852662682533, -0.00436204532161355, -0.010795993730425835, 0.01528732106089592, 0.0360267236828804, 0.0810960903763771, -0.04707879573106766, -0.00498958257958293, 0.09225547313690186, 0.006553373299539089, 0.00669174874201417, 0.05810375139117241, -0.01896798051893... |
175 | 175 | ['Kenji Kawaguchi', 'Yoshua Bengio'] | 1802.07426v1 | This paper introduces a novel measure-theoretic learning theory to analyze
generalization behaviors of practical interest. The proposed learning theory
has the following abilities: 1) to utilize the qualities of each learned
representation on the path from raw inputs to outputs in representation
learning, 2) to guarant... | Generalization in Machine Learning via Analytical Learning Theory | 2,018 | http://arxiv.org/pdf/1802.07426v1 | Title Generalization Machine Learning via Analytical Learning Theory Summary paper introduces novel measuretheoretic learning theory analyze generalization behavior practical interest proposed learning theory following ability 1 utilize quality learned representation path raw input output representation learning 2 guar... | [-0.04089696332812309, 0.010580497793853283, -0.04603782296180725, -0.009592321701347828, 0.004706556908786297, 0.004627256654202938, 0.006789667531847954, 0.010783826932311058, -0.05618637055158615, 0.03374860808253288, 0.06609243899583817, -0.017072129994630814, -0.003834246890619397, 0.05823005735874176, 0.024226350... |
176 | 176 | ['Roman Novak', 'Yasaman Bahri', 'Daniel A. Abolafia', 'Jeffrey Pennington', 'Jascha Sohl-Dickstein'] | 1802.08760v1 | In practice it is often found that large over-parameterized neural networks
generalize better than their smaller counterparts, an observation that appears
to conflict with classical notions of function complexity, which typically
favor smaller models. In this work, we investigate this tension between
complexity and gen... | Sensitivity and Generalization in Neural Networks: an Empirical Study | 2,018 | http://arxiv.org/pdf/1802.08760v1 | Title Sensitivity Generalization Neural Networks Empirical Study Summary practice often found large overparameterized neural network generalize better smaller counterpart observation appears conflict classical notion function complexity typically favor smaller model work investigate tension complexity generalization ex... | [-0.008312312886118889, 0.009196307510137558, -0.05233009159564972, 0.021689528599381447, 0.03583923727273941, -0.023269161581993103, 0.03057517297565937, -0.006933005526661873, -0.021818634122610092, 0.027232104912400246, -0.00268950336612761, 0.030916081741452217, 0.00390625512227416, 0.02507195994257927, 0.027891039... |
177 | 177 | ['Ari S. Morcos', 'David G. T. Barrett', 'Neil C. Rabinowitz', 'Matthew Botvinick'] | 1803.06959v1 | Despite their ability to memorize large datasets, deep neural networks often
achieve good generalization performance. However, the differences between the
learned solutions of networks which generalize and those which do not remain
unclear. Additionally, the tuning properties of single directions (defined as
the activa... | On the importance of single directions for generalization | 2,018 | http://arxiv.org/pdf/1803.06959v1 | Title importance single direction generalization Summary Despite ability memorize large datasets deep neural network often achieve good generalization performance However difference learned solution network generalize remain unclear Additionally tuning property single direction defined activation single unit linear com... | [-0.025508176535367966, 0.058791566640138626, -0.02577769011259079, 0.013703260570764542, 0.011755692772567272, 0.011057221330702305, 0.06269649416208267, -0.022969113662838936, -0.06650397926568985, 0.03535652160644531, -0.004868027754127979, -0.016830842941999435, -0.001781700411811471, 0.06200243905186653, 0.0127793... |
178 | 178 | ['Srinivas C. Turaga', 'Kevin L. Briggman', 'Moritz Helmstaedter', 'Winfried Denk', 'H. Sebastian Seung'] | 0911.5372v1 | Images can be segmented by first using a classifier to predict an affinity
graph that reflects the degree to which image pixels must be grouped together
and then partitioning the graph to yield a segmentation. Machine learning has
been applied to the affinity classifier to produce affinity graphs that are
good in the s... | Maximin affinity learning of image segmentation | 2,009 | http://arxiv.org/pdf/0911.5372v1 | Title Maximin affinity learning image segmentation Summary Images segmented first using classifier predict affinity graph reflects degree image pixel must grouped together partitioning graph yield segmentation Machine learning applied affinity classifier produce affinity graph good sense minimizing edge misclassificati... | [-0.026119409129023552, -0.0805911123752594, -0.007136921398341656, 0.028323953971266747, -0.02615479752421379, -0.021099906414747238, 0.008177189156413078, 0.014270447194576263, 0.04525921121239662, 0.029587985947728157, 0.014500692486763, 0.0403924323618412, 0.018854228779673576, -0.03336678072810173, 0.0146484868600... |
179 | 179 | ['Sergey S. Tarasenko'] | 1102.2739v1 | This study is focused on the development of the cortex-like visual object
recognition system. We propose a general framework, which consists of three
hierarchical levels (modules). These modules functionally correspond to the V1,
V4 and IT areas. Both bottom-up and top-down connections between the
hierarchical levels V... | A General Framework for Development of the Cortex-like Visual Object
Recognition System: Waves of Spikes, Predictive Coding and Universal
Dictionary of Features | 2,011 | http://arxiv.org/pdf/1102.2739v1 | Title General Framework Development Cortexlike Visual Object Recognition System Waves Spikes Predictive Coding Universal Dictionary Features Summary study focused development cortexlike visual object recognition system propose general framework consists three hierarchical level module module functionally correspond V1 ... | [-0.0009064223268069327, 0.0021670248825103045, -0.025190727785229683, 0.04891321063041687, -0.002613988472148776, 0.014427641406655312, 0.01968766376376152, 0.008697547018527985, 0.03644901141524315, 0.005616879090666771, -0.037766337394714355, 0.014864770695567131, 0.00046208896674215794, 0.08074658364057541, 0.01453... |
180 | 180 | ['Dan C. Cireşan', 'Ueli Meier', 'Luca M. Gambardella', 'Jürgen Schmidhuber'] | 1103.4487v1 | The competitive MNIST handwritten digit recognition benchmark has a long
history of broken records since 1998. The most recent substantial improvement
by others dates back 7 years (error rate 0.4%) . Recently we were able to
significantly improve this result, using graphics cards to greatly speed up
training of simple ... | Handwritten Digit Recognition with a Committee of Deep Neural Nets on
GPUs | 2,011 | http://arxiv.org/pdf/1103.4487v1 | Title Handwritten Digit Recognition Committee Deep Neural Nets GPUs Summary competitive MNIST handwritten digit recognition benchmark long history broken record since 1998 recent substantial improvement others date back 7 year error rate 04 Recently able significantly improve result using graphic card greatly speed tra... | [-0.009414189495146275, 0.0630563497543335, 0.0035463629756122828, 0.09012654423713684, -0.010637854225933552, 0.0219058096408844, 0.08015017956495285, 0.023462682962417603, -0.002985337283462286, 0.03166167810559273, 0.02365194261074066, -0.023339396342635155, 0.050962645560503006, 0.022648407146334648, -0.01319724414... |
181 | 181 | ['Ridwan Al Iqbal'] | 1110.0214v1 | Artificial Neural Network is among the most popular algorithm for supervised
learning. However, Neural Networks have a well-known drawback of being a "Black
Box" learner that is not comprehensible to the Users. This lack of transparency
makes it unsuitable for many high risk tasks such as medical diagnosis that
require... | Eclectic Extraction of Propositional Rules from Neural Networks | 2,011 | http://arxiv.org/pdf/1110.0214v1 | Title Eclectic Extraction Propositional Rules Neural Networks Summary Artificial Neural Network among popular algorithm supervised learning However Neural Networks wellknown drawback Black Box learner comprehensible Users lack transparency make unsuitable many high risk task medical diagnosis requires rational justific... | [0.0367228165268898, 0.029489461332559586, -0.013720064423978329, 0.011697973124682903, -0.06896384060382843, -0.0035867507103830576, -0.007628391031175852, 0.046477969735860825, 0.011711702682077885, 0.007252187933772802, 0.04214801266789436, 0.019037997350096703, 0.02832217514514923, 0.028179598972201347, -0.02131610... |
182 | 182 | ['Arnab Ghosh', 'Viveka Kulharia', 'Vinay Namboodiri'] | 1612.01294v1 | Communicating and sharing intelligence among agents is an important facet of
achieving Artificial General Intelligence. As a first step towards this
challenge, we introduce a novel framework for image generation: Message Passing
Multi-Agent Generative Adversarial Networks (MPM GANs). While GANs have
recently been shown... | Message Passing Multi-Agent GANs | 2,016 | http://arxiv.org/pdf/1612.01294v1 | Title Message Passing MultiAgent GANs Summary Communicating sharing intelligence among agent important facet achieving Artificial General Intelligence first step towards challenge introduce novel framework image generation Message Passing MultiAgent Generative Adversarial Networks MPM GANs GANs recently shown effective... | [0.017849059775471687, 0.05504539608955383, 0.0005229501985013485, 0.022542985156178474, -0.021121103316545486, 0.0011106240563094616, 0.05898742750287056, -0.0020006345584988594, -0.0388215146958828, 0.001636327593587339, -0.030131246894598007, -0.0034502067137509584, -0.03480396419763565, 0.010919131338596344, 0.0714... |
183 | 183 | ['Tong Che', 'Yanran Li', 'Athul Paul Jacob', 'Yoshua Bengio', 'Wenjie Li'] | 1612.02136v5 | Although Generative Adversarial Networks achieve state-of-the-art results on
a variety of generative tasks, they are regarded as highly unstable and prone
to miss modes. We argue that these bad behaviors of GANs are due to the very
particular functional shape of the trained discriminators in high dimensional
spaces, wh... | Mode Regularized Generative Adversarial Networks | 2,016 | http://arxiv.org/pdf/1612.02136v5 | Title Mode Regularized Generative Adversarial Networks Summary Although Generative Adversarial Networks achieve stateoftheart result variety generative task regarded highly unstable prone miss mode argue bad behavior GANs due particular functional shape trained discriminator high dimensional space easily make training ... | [0.0021598569583147764, 0.08404155820608139, -0.019086865708231926, -0.008449694141745567, 0.03577665239572525, -0.025913136079907417, 0.005626949481666088, 0.00010370239760959521, -0.04340387135744095, 0.051369521766901016, -0.003550903871655464, 0.015018542297184467, -0.02204766683280468, 0.033727481961250305, 0.0594... |
184 | 184 | ['Bharat Singh', 'Soham De', 'Yangmuzi Zhang', 'Thomas Goldstein', 'Gavin Taylor'] | 1510.04609v1 | The increasing complexity of deep learning architectures is resulting in
training time requiring weeks or even months. This slow training is due in part
to vanishing gradients, in which the gradients used by back-propagation are
extremely large for weights connecting deep layers (layers near the output
layer), and extr... | Layer-Specific Adaptive Learning Rates for Deep Networks | 2,015 | http://arxiv.org/pdf/1510.04609v1 | Title LayerSpecific Adaptive Learning Rates Deep Networks Summary increasing complexity deep learning architecture resulting training time requiring week even month slow training due part vanishing gradient gradient used backpropagation extremely large weight connecting deep layer layer near output layer extremely smal... | [0.004674241412431002, -0.010195175185799599, -0.02250341884791851, 0.06782056391239166, 0.020764455199241638, -0.0065002962946891785, 0.06451480090618134, -0.014551694504916668, -0.024900073185563087, 0.015319187194108963, -0.01956958696246147, 0.039943575859069824, 0.016074420884251595, 0.0381023995578289, 0.00629762... |
185 | 185 | ['Baochen Sun', 'Jiashi Feng', 'Kate Saenko'] | 1511.05547v2 | Unlike human learning, machine learning often fails to handle changes between
training (source) and test (target) input distributions. Such domain shifts,
common in practical scenarios, severely damage the performance of conventional
machine learning methods. Supervised domain adaptation methods have been
proposed for ... | Return of Frustratingly Easy Domain Adaptation | 2,015 | http://arxiv.org/pdf/1511.05547v2 | Title Return Frustratingly Easy Domain Adaptation Summary Unlike human learning machine learning often fails handle change training source test target input distribution domain shift common practical scenario severely damage performance conventional machine learning method Supervised domain adaptation method proposed c... | [0.006140164099633694, 0.03558460250496864, -0.02201683446764946, -0.03230151906609535, -0.02162107452750206, 0.00257741822861135, 0.08435023576021194, -0.0018613454885780811, -0.05232500284910202, -0.008091026917099953, -0.0293829794973135, 0.00776475016027689, 0.02512151189148426, 0.05079600214958191, -0.029154613614... |
186 | 186 | ['Lukas Cavigelli', 'Luca Benini'] | 1512.04295v2 | An ever increasing number of computer vision and image/video processing
challenges are being approached using deep convolutional neural networks,
obtaining state-of-the-art results in object recognition and detection,
semantic segmentation, action recognition, optical flow and superresolution.
Hardware acceleration of ... | Origami: A 803 GOp/s/W Convolutional Network Accelerator | 2,015 | http://arxiv.org/pdf/1512.04295v2 | Title Origami 803 GOpsW Convolutional Network Accelerator Summary ever increasing number computer vision imagevideo processing challenge approached using deep convolutional neural network obtaining stateoftheart result object recognition detection semantic segmentation action recognition optical flow superresolution Ha... | [-0.03533274680376053, 0.014776026830077171, -0.0014125180896371603, 0.11635368317365646, 0.015131840482354164, -0.014659779146313667, 0.02819630317389965, 0.006116359960287809, -0.02864663116633892, -0.03482743725180626, 0.019600851461291313, 0.012567292898893356, 0.017588449642062187, 0.06970798969268799, 0.044958956... |
187 | 187 | ['Aravind S. Lakshminarayanan', 'Ramnandan Krishnamurthy', 'Peeyush Kumar', 'Balaraman Ravindran'] | 1605.05359v2 | This paper introduces an automated skill acquisition framework in
reinforcement learning which involves identifying a hierarchical description of
the given task in terms of abstract states and extended actions between
abstract states. Identifying such structures present in the task provides ways
to simplify and speed u... | Option Discovery in Hierarchical Reinforcement Learning using
Spatio-Temporal Clustering | 2,016 | http://arxiv.org/pdf/1605.05359v2 | Title Option Discovery Hierarchical Reinforcement Learning using SpatioTemporal Clustering Summary paper introduces automated skill acquisition framework reinforcement learning involves identifying hierarchical description given task term abstract state extended action abstract state Identifying structure present task ... | [-0.030196843668818474, 0.0006299293017946184, -0.02648656629025936, -0.008701860904693604, 0.003459151368588209, -0.027547433972358704, 0.014461868442595005, -0.00015311477181967348, -0.03315132483839989, -0.013102319091558456, 0.00047338573494926095, 0.0448630228638649, 0.0005496739177033305, 0.07918047159910202, 0.0... |
188 | 188 | ['Andreas Veit', 'Michael Wilber', 'Serge Belongie'] | 1605.06431v2 | In this work we propose a novel interpretation of residual networks showing
that they can be seen as a collection of many paths of differing length.
Moreover, residual networks seem to enable very deep networks by leveraging
only the short paths during training. To support this observation, we rewrite
residual networks... | Residual Networks Behave Like Ensembles of Relatively Shallow Networks | 2,016 | http://arxiv.org/pdf/1605.06431v2 | Title Residual Networks Behave Like Ensembles Relatively Shallow Networks Summary work propose novel interpretation residual network showing seen collection many path differing length Moreover residual network seem enable deep network leveraging short path training support observation rewrite residual network explicit ... | [-0.008388273417949677, 0.02791118435561657, -0.022707780823111534, 0.055501021444797516, 0.01037044171243906, -0.02185480110347271, 9.231572767021134e-05, -0.010486502200365067, -0.06265868246555328, 0.028354881331324577, 0.01657799817621708, 0.024325605481863022, -0.008000190369784832, -0.006819234229624271, 0.019591... |
189 | 189 | ['Anh Nguyen', 'Alexey Dosovitskiy', 'Jason Yosinski', 'Thomas Brox', 'Jeff Clune'] | 1605.09304v5 | Deep neural networks (DNNs) have demonstrated state-of-the-art results on
many pattern recognition tasks, especially vision classification problems.
Understanding the inner workings of such computational brains is both
fascinating basic science that is interesting in its own right - similar to why
we study the human br... | Synthesizing the preferred inputs for neurons in neural networks via
deep generator networks | 2,016 | http://arxiv.org/pdf/1605.09304v5 | Title Synthesizing preferred input neuron neural network via deep generator network Summary Deep neural network DNNs demonstrated stateoftheart result many pattern recognition task especially vision classification problem Understanding inner working computational brain fascinating basic science interesting right simila... | [-0.01585194654762745, 0.07251649349927902, -0.01084416825324297, 0.02141485922038555, 0.002123874379321933, -0.0032464833930134773, 0.025581488385796547, 0.011971809901297092, -0.01633543334901333, 0.035361990332603455, -0.015380226075649261, 0.004455838818103075, -0.009208879433572292, 0.06377391517162323, 0.06678320... |
190 | 190 | ['Rathinakumar Appuswamy', 'Tapan Nayak', 'John Arthur', 'Steven Esser', 'Paul Merolla', 'Jeffrey Mckinstry', 'Timothy Melano', 'Myron Flickner', 'Dharmendra Modha'] | 1606.02407v1 | We derive a relationship between network representation in energy-efficient
neuromorphic architectures and block Toplitz convolutional matrices. Inspired
by this connection, we develop deep convolutional networks using a family of
structured convolutional matrices and achieve state-of-the-art trade-off
between energy e... | Structured Convolution Matrices for Energy-efficient Deep learning | 2,016 | http://arxiv.org/pdf/1606.02407v1 | Title Structured Convolution Matrices Energyefficient Deep learning Summary derive relationship network representation energyefficient neuromorphic architecture block Toplitz convolutional matrix Inspired connection develop deep convolutional network using family structured convolutional matrix achieve stateoftheart tr... | [-0.016599668189883232, 0.04337156191468239, -0.00867722649127245, 0.11679377406835556, 0.0019713821820914745, -0.0058877295814454556, 0.016658274456858635, 0.01525559090077877, -0.006829366087913513, -0.032412685453891754, -0.042291976511478424, 0.010505524463951588, -0.004430582281202078, 0.04620462656021118, 0.02834... |
191 | 191 | ['Baochen Sun', 'Kate Saenko'] | 1607.01719v1 | Deep neural networks are able to learn powerful representations from large
quantities of labeled input data, however they cannot always generalize well
across changes in input distributions. Domain adaptation algorithms have been
proposed to compensate for the degradation in performance due to domain shift.
In this pap... | Deep CORAL: Correlation Alignment for Deep Domain Adaptation | 2,016 | http://arxiv.org/pdf/1607.01719v1 | Title Deep CORAL Correlation Alignment Deep Domain Adaptation Summary Deep neural network able learn powerful representation large quantity labeled input data however cannot always generalize well across change input distribution Domain adaptation algorithm proposed compensate degradation performance due domain shift p... | [-0.010725886560976505, 0.05521048605442047, -0.019344104453921318, -0.018435906618833542, -0.012039180845022202, 0.00935688428580761, 0.04981807991862297, -0.024802017956972122, -0.026348521932959557, 0.03277937322854996, -0.07072371989488602, 0.019499894231557846, 0.031083934009075165, 0.04162105545401573, -0.0245450... |
192 | 192 | ['Jun Liu', 'Amir Shahroudy', 'Dong Xu', 'Gang Wang'] | 1607.07043v1 | 3D action recognition - analysis of human actions based on 3D skeleton data -
becomes popular recently due to its succinctness, robustness, and
view-invariant representation. Recent attempts on this problem suggested to
develop RNN-based learning methods to model the contextual dependency in the
temporal domain. In thi... | Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition | 2,016 | http://arxiv.org/pdf/1607.07043v1 | Title SpatioTemporal LSTM Trust Gates 3D Human Action Recognition Summary 3D action recognition analysis human action based 3D skeleton data becomes popular recently due succinctness robustness viewinvariant representation Recent attempt problem suggested develop RNNbased learning method model contextual dependency tem... | [-0.012921557761728764, 0.017357636243104935, 0.0009066946222446859, 0.07766448706388474, -0.0021652295254170895, 0.03554300218820572, -0.007129453122615814, -0.007529738824814558, 0.0005641651805490255, -0.051401954144239426, -0.04626976698637009, -0.05487972870469093, 0.04389138147234917, 0.0846305638551712, 0.040922... |
193 | 193 | ['Suraj Srinivas', 'R. Venkatesh Babu'] | 1611.06791v1 | Deep Neural Networks often require good regularizers to generalize well.
Dropout is one such regularizer that is widely used among Deep Learning
practitioners. Recent work has shown that Dropout can also be viewed as
performing Approximate Bayesian Inference over the network parameters. In this
work, we generalize this... | Generalized Dropout | 2,016 | http://arxiv.org/pdf/1611.06791v1 | Title Generalized Dropout Summary Deep Neural Networks often require good regularizers generalize well Dropout one regularizer widely used among Deep Learning practitioner Recent work shown Dropout also viewed performing Approximate Bayesian Inference network parameter work generalize notion introduce rich family regul... | [-0.016172297298908234, 0.060791682451963425, -0.006108471658080816, 0.029795153066515923, 0.00688586849719286, -0.0159979946911335, 0.0460902638733387, 0.006665560882538557, -0.02113274671137333, 0.038688767701387405, 0.014540540054440498, 0.020467763766646385, 0.005920268129557371, 0.06350425630807877, 0.017433755099... |
194 | 194 | ['I. Theodorakopoulos', 'V. Pothos', 'D. Kastaniotis', 'N. Fragoulis'] | 1701.05221v5 | A new, radical CNN design approach is presented in this paper, considering
the reduction of the total computational load during inference. This is
achieved by a new holistic intervention on both the CNN architecture and the
training procedure, which targets to the parsimonious inference by learning to
exploit or remove... | Parsimonious Inference on Convolutional Neural Networks: Learning and
applying on-line kernel activation rules | 2,017 | http://arxiv.org/pdf/1701.05221v5 | Title Parsimonious Inference Convolutional Neural Networks Learning applying online kernel activation rule Summary new radical CNN design approach presented paper considering reduction total computational load inference achieved new holistic intervention CNN architecture training procedure target parsimonious inference... | [0.01892285794019699, 0.03176836296916008, -0.02030724287033081, 0.06722205877304077, -0.006590038072317839, -0.007452232763171196, 0.0459536537528038, 0.019313611090183258, -0.02842235006392002, -0.026905441656708717, 0.021887587383389473, 0.051471006125211716, -0.00291715981438756, 0.038893792778253555, 0.01017731800... |
195 | 195 | ['Chelsea Finn', 'Pieter Abbeel', 'Sergey Levine'] | 1703.03400v3 | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on... | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | 2,017 | http://arxiv.org/pdf/1703.03400v3 | Title ModelAgnostic MetaLearning Fast Adaptation Deep Networks Summary propose algorithm metalearning modelagnostic sense compatible model trained gradient descent applicable variety different learning problem including classification regression reinforcement learning goal metalearning train model variety learning task... | [-0.008136829361319542, 0.023955857381224632, -0.03234463930130005, 0.029733700677752495, 0.04428404942154884, 0.005840153433382511, 0.04772241786122322, 0.0074520595371723175, -0.05295838415622711, 0.00922408513724804, -0.034250665456056595, 0.10482901334762573, -0.027029139921069145, 0.01720396988093853, 0.0220488514... |
196 | 196 | ['Asit Mishra', 'Jeffrey J Cook', 'Eriko Nurvitadhi', 'Debbie Marr'] | 1704.03079v1 | For computer vision applications, prior works have shown the efficacy of
reducing the numeric precision of model parameters (network weights) in deep
neural networks but also that reducing the precision of activations hurts model
accuracy much more than reducing the precision of model parameters. We study
schemes to tr... | WRPN: Training and Inference using Wide Reduced-Precision Networks | 2,017 | http://arxiv.org/pdf/1704.03079v1 | Title WRPN Training Inference using Wide ReducedPrecision Networks Summary computer vision application prior work shown efficacy reducing numeric precision model parameter network weight deep neural network also reducing precision activation hurt model accuracy much reducing precision model parameter study scheme train... | [-0.0358797088265419, 0.044300444424152374, -0.024891357868909836, 0.0524381622672081, 0.0145856449380517, -0.03891358897089958, 0.03083834797143936, 0.008303012698888779, -0.06069585680961609, 0.050226014107465744, -0.016413861885666847, 0.0038845674134790897, 0.03238734230399132, -0.008598925545811653, 0.020039036870... |
197 | 197 | ['David Rolnick', 'Andreas Veit', 'Serge Belongie', 'Nir Shavit'] | 1705.10694v3 | Deep neural networks trained on large supervised datasets have led to
impressive results in image classification and other tasks. However,
well-annotated datasets can be time-consuming and expensive to collect, lending
increased interest to larger but noisy datasets that are more easily obtained.
In this paper, we show... | Deep Learning is Robust to Massive Label Noise | 2,017 | http://arxiv.org/pdf/1705.10694v3 | Title Deep Learning Robust Massive Label Noise Summary Deep neural network trained large supervised datasets led impressive result image classification task However wellannotated datasets timeconsuming expensive collect lending increased interest larger noisy datasets easily obtained paper show deep neural network capa... | [0.030910776928067207, 0.05125641077756882, -0.01859557442367077, 0.03943230211734772, 0.03508317098021507, 0.0039650918915867805, 0.05846835672855377, -0.0035400555934756994, -0.01660863310098648, 0.018252722918987274, -0.02822532132267952, 0.0233351718634367, -0.023831773549318314, 0.04866470396518707, 0.029610130935... |
198 | 198 | ['Stefan Lattner', 'Maarten Grachten'] | 1707.01357v1 | Content-invariance in mapping codes learned by GAEs is a useful feature for
various relation learning tasks. In this paper we show that the
content-invariance of mapping codes for images of 2D and 3D rotated objects can
be substantially improved by extending the standard GAE loss (symmetric
reconstruction error) with a... | Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object
Rotation | 2,017 | http://arxiv.org/pdf/1707.01357v1 | Title Improving ContentInvariance Gated Autoencoders 2D 3D Object Rotation Summary Contentinvariance mapping code learned GAEs useful feature various relation learning task paper show contentinvariance mapping code image 2D 3D rotated object substantially improved extending standard GAE loss symmetric reconstruction er... | [0.008513077162206173, 0.07667741179466248, 0.014725294895470142, 0.057814229279756546, -0.0029120943509042263, 0.034956950694322586, 0.01792016439139843, 0.006903417408466339, -0.054473649710416794, -0.026799369603395462, -0.033115796744823456, 0.0428788922727108, 0.03869888186454773, 0.10498959571123123, 0.0543282441... |
199 | 199 | ['Jindong Wang', 'Yiqiang Chen', 'Shuji Hao', 'Xiaohui Peng', 'Lisha Hu'] | 1707.03502v2 | Sensor-based activity recognition seeks the profound high-level knowledge
about human activities from multitudes of low-level sensor readings.
Conventional pattern recognition approaches have made tremendous progress in
the past years. However, those methods often heavily rely on heuristic
hand-crafted feature extracti... | Deep Learning for Sensor-based Activity Recognition: A Survey | 2,017 | http://arxiv.org/pdf/1707.03502v2 | Title Deep Learning Sensorbased Activity Recognition Survey Summary Sensorbased activity recognition seek profound highlevel knowledge human activity multitude lowlevel sensor reading Conventional pattern recognition approach made tremendous progress past year However method often heavily rely heuristic handcrafted fea... | [-0.04772906005382538, 0.004994967021048069, -0.011839666403830051, 0.05454079434275627, 0.0062690130434930325, 0.02029799297451973, 0.038358382880687714, -0.0038154057692736387, 0.004776395857334137, -0.018325598910450935, -0.011894668452441692, 0.03594629466533661, 0.018405994400382042, 0.027837131172418594, 0.000997... |
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