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 |
|---|---|---|---|---|---|---|---|---|---|
600 | 600 | ['Lili Mou', 'Rui Yan', 'Ge Li', 'Lu Zhang', 'Zhi Jin'] | 1512.06612v2 | Recent language models, especially those based on recurrent neural networks
(RNNs), make it possible to generate natural language from a learned
probability. Language generation has wide applications including machine
translation, summarization, question answering, conversation systems, etc.
Existing methods typically ... | Backward and Forward Language Modeling for Constrained Sentence
Generation | 2,015 | http://arxiv.org/pdf/1512.06612v2 | Title Backward Forward Language Modeling Constrained Sentence Generation Summary Recent language model especially based recurrent neural network RNNs make possible generate natural language learned probability Language generation wide application including machine translation summarization question answering conversati... | [0.0713164359331131, 0.04077165201306343, -0.00351351173594594, 0.009156038984656334, -0.050216205418109894, -0.03484518453478813, -0.01834772154688835, 0.015102071687579155, -0.011920073069632053, -0.04726139456033707, 0.04541974887251854, -0.043521977961063385, 0.005748002789914608, 0.04906434938311577, 0.02093552052... |
601 | 601 | ['Kyuyeon Hwang', 'Minjae Lee', 'Wonyong Sung'] | 1512.08903v1 | In this paper, we propose a context-aware keyword spotting model employing a
character-level recurrent neural network (RNN) for spoken term detection in
continuous speech. The RNN is end-to-end trained with connectionist temporal
classification (CTC) to generate the probabilities of character and
word-boundary labels. ... | Online Keyword Spotting with a Character-Level Recurrent Neural Network | 2,015 | http://arxiv.org/pdf/1512.08903v1 | Title Online Keyword Spotting CharacterLevel Recurrent Neural Network Summary paper propose contextaware keyword spotting model employing characterlevel recurrent neural network RNN spoken term detection continuous speech RNN endtoend trained connectionist temporal classification CTC generate probability character word... | [-0.006053483579307795, 0.017226427793502808, 0.04451555758714676, 0.04900750890374184, 0.017928095534443855, -0.02497158572077751, -0.008989205583930016, 0.008729066699743271, -0.047539252787828445, -0.03288761526346207, -0.03752556070685387, -0.04293350502848625, 0.030366219580173492, 0.09057103842496872, 0.001829272... |
602 | 602 | ['Zhenhao Ge', 'Yufang Sun'] | 1602.07393v1 | Authorship attribution refers to the task of automatically determining the
author based on a given sample of text. It is a problem with a long history and
has a wide range of application. Building author profiles using language models
is one of the most successful methods to automate this task. New language
modeling me... | Domain Specific Author Attribution Based on Feedforward Neural Network
Language Models | 2,016 | http://arxiv.org/pdf/1602.07393v1 | Title Domain Specific Author Attribution Based Feedforward Neural Network Language Models Summary Authorship attribution refers task automatically determining author based given sample text problem long history wide range application Building author profile using language model one successful method automate task New l... | [0.049107592552900314, 0.05301670730113983, -0.03609152138233185, 0.024805482476949692, -0.05954847112298012, -0.019497159868478775, 0.10058499872684479, -0.013518590480089188, 0.01585269533097744, -0.027639785781502724, 0.015150275081396103, -0.0017323875799775124, 0.024015109986066818, 0.03368305042386055, 0.02501071... |
603 | 603 | ['Liang Lu', 'Lingpeng Kong', 'Chris Dyer', 'Noah A. Smith', 'Steve Renals'] | 1603.00223v2 | We study the segmental recurrent neural network for end-to-end acoustic
modelling. This model connects the segmental conditional random field (CRF)
with a recurrent neural network (RNN) used for feature extraction. Compared to
most previous CRF-based acoustic models, it does not rely on an external system
to provide fe... | Segmental Recurrent Neural Networks for End-to-end Speech Recognition | 2,016 | http://arxiv.org/pdf/1603.00223v2 | Title Segmental Recurrent Neural Networks Endtoend Speech Recognition Summary study segmental recurrent neural network endtoend acoustic modelling model connects segmental conditional random field CRF recurrent neural network RNN used feature extraction Compared previous CRFbased acoustic model rely external system pro... | [0.01909462735056877, 0.029673578217625618, 0.037180881947278976, 0.03395266830921173, 0.002734440378844738, -0.03491126373410225, -0.0002440836833557114, 0.007297059986740351, -0.08068285882472992, -0.017752185463905334, -0.02081473171710968, -0.013872994109988213, 0.03581831231713295, 0.017155731096863747, -0.0233709... |
604 | 604 | ['Lili Mou', 'Zhao Meng', 'Rui Yan', 'Ge Li', 'Yan Xu', 'Lu Zhang', 'Zhi Jin'] | 1603.06111v2 | Transfer learning is aimed to make use of valuable knowledge in a source
domain to help model performance in a target domain. It is particularly
important to neural networks, which are very likely to be overfitting. In some
fields like image processing, many studies have shown the effectiveness of
neural network-based ... | How Transferable are Neural Networks in NLP Applications? | 2,016 | http://arxiv.org/pdf/1603.06111v2 | Title Transferable Neural Networks NLP Applications Summary Transfer learning aimed make use valuable knowledge source domain help model performance target domain particularly important neural network likely overfitting field like image processing many study shown effectiveness neural networkbased transfer learning neu... | [0.0072393398731946945, 0.044481221586465836, -0.004846601746976376, 0.06205492094159126, -0.013320247642695904, -0.012743615545332432, 0.0037230942398309708, 0.002024505054578185, -0.02883164770901203, -0.030240140855312347, -0.04762844368815422, 0.05009543523192406, -0.008013979531824589, 0.06258271634578705, 0.02063... |
605 | 605 | ['Wei-Ning Hsu', 'Yu Zhang', 'James Glass'] | 1603.07044v1 | We apply a general recurrent neural network (RNN) encoder framework to
community question answering (cQA) tasks. Our approach does not rely on any
linguistic processing, and can be applied to different languages or domains.
Further improvements are observed when we extend the RNN encoders with a neural
attention mechan... | Recurrent Neural Network Encoder with Attention for Community Question
Answering | 2,016 | http://arxiv.org/pdf/1603.07044v1 | Title Recurrent Neural Network Encoder Attention Community Question Answering Summary apply general recurrent neural network RNN encoder framework community question answering cQA task approach rely linguistic processing applied different language domain improvement observed extend RNN encoders neural attention mechani... | [0.028209848329424858, 0.039999667555093765, -0.011160486377775669, 0.06346011161804199, -0.007395118474960327, -0.006305999588221312, 0.0022823307663202286, -0.00979146920144558, -0.008441049605607986, -0.039311543107032776, 0.01076226495206356, 0.0029576115775853395, 0.0028488426469266415, 0.030060268938541412, -0.00... |
606 | 606 | ['Saurabh Kataria'] | 1603.07646v1 | We consider the problem of learning distributed representations for tags from
their associated content for the task of tag recommendation. Considering
tagging information is usually very sparse, effective learning from content and
tag association is very crucial and challenging task. Recently, various neural
representa... | Recursive Neural Language Architecture for Tag Prediction | 2,016 | http://arxiv.org/pdf/1603.07646v1 | Title Recursive Neural Language Architecture Tag Prediction Summary consider problem learning distributed representation tag associated content task tag recommendation Considering tagging information usually sparse effective learning content tag association crucial challenging task Recently various neural representatio... | [0.046709440648555756, -0.005000155884772539, -0.046266429126262665, 0.02858760952949524, 0.001003048848360777, 0.0013018466997891665, -0.013201013207435608, -0.004298039246350527, 0.05208227410912514, -0.023654785007238388, -0.025690404698252678, -0.0348496213555336, 0.024195024743676186, 0.07179399579763412, 0.014678... |
607 | 607 | ['Rohit Prabhavalkar', 'Ouais Alsharif', 'Antoine Bruguier', 'Ian McGraw'] | 1603.08042v2 | We study the problem of compressing recurrent neural networks (RNNs). In
particular, we focus on the compression of RNN acoustic models, which are
motivated by the goal of building compact and accurate speech recognition
systems which can be run efficiently on mobile devices. In this work, we
present a technique for ge... | On the Compression of Recurrent Neural Networks with an Application to
LVCSR acoustic modeling for Embedded Speech Recognition | 2,016 | http://arxiv.org/pdf/1603.08042v2 | Title Compression Recurrent Neural Networks Application LVCSR acoustic modeling Embedded Speech Recognition Summary study problem compressing recurrent neural network RNNs particular focus compression RNN acoustic model motivated goal building compact accurate speech recognition system run efficiently mobile device wor... | [-0.01828327216207981, -0.004037776961922646, 0.015006584115326405, 0.054580144584178925, -0.012824430130422115, -0.023773901164531708, 0.027628332376480103, 0.022657198831439018, -0.06416162848472595, 0.023214837536215782, -0.03965678811073303, -0.013697794638574123, 0.04727723076939583, -0.015872197225689888, 0.00747... |
608 | 608 | ['Caglar Gulcehre', 'Sungjin Ahn', 'Ramesh Nallapati', 'Bowen Zhou', 'Yoshua Bengio'] | 1603.08148v3 | The problem of rare and unknown words is an important issue that can
potentially influence the performance of many NLP systems, including both the
traditional count-based and the deep learning models. We propose a novel way to
deal with the rare and unseen words for the neural network models using
attention. Our model ... | Pointing the Unknown Words | 2,016 | http://arxiv.org/pdf/1603.08148v3 | Title Pointing Unknown Words Summary problem rare unknown word important issue potentially influence performance many NLP system including traditional countbased deep learning model propose novel way deal rare unseen word neural network model using attention model us two softmax layer order predict next word conditiona... | [0.0698966532945633, 0.012464814819395542, 0.01565462164580822, 0.032125454396009445, 0.0012413514778017998, 0.0022937203757464886, -0.005260268691927195, 0.0024114649277180433, -0.021239766851067543, -0.04228523001074791, 0.010414793156087399, -0.006894045043736696, 0.017795942723751068, 0.015037612058222294, 0.041376... |
609 | 609 | ['Zhenyao Zhu', 'Jesse H. Engel', 'Awni Hannun'] | 1603.09509v2 | Deep learning has dramatically improved the performance of speech recognition
systems through learning hierarchies of features optimized for the task at
hand. However, true end-to-end learning, where features are learned directly
from waveforms, has only recently reached the performance of hand-tailored
representations... | Learning Multiscale Features Directly From Waveforms | 2,016 | http://arxiv.org/pdf/1603.09509v2 | Title Learning Multiscale Features Directly Waveforms Summary Deep learning dramatically improved performance speech recognition system learning hierarchy feature optimized task hand However true endtoend learning feature learned directly waveform recently reached performance handtailored representation based Fourier t... | [-0.033016979694366455, 0.059531424194574356, 0.01716112531721592, 0.058574970811605453, 0.017842482775449753, 5.215239434619434e-05, 0.06738042831420898, 0.010663111694157124, -0.023073263466358185, 0.011679023504257202, -0.08431396633386612, -0.05607355386018753, 0.037901535630226135, 0.06979480385780334, 0.009736104... |
610 | 610 | ['Petr Baudis', 'Silvestr Stanko', 'Jan Sedivy'] | 1605.04655v2 | We consider the problem of Recognizing Textual Entailment within an
Information Retrieval context, where we must simultaneously determine the
relevancy as well as degree of entailment for individual pieces of evidence to
determine a yes/no answer to a binary natural language question.
We compare several variants of n... | Joint Learning of Sentence Embeddings for Relevance and Entailment | 2,016 | http://arxiv.org/pdf/1605.04655v2 | Title Joint Learning Sentence Embeddings Relevance Entailment Summary consider problem Recognizing Textual Entailment within Information Retrieval context must simultaneously determine relevancy well degree entailment individual piece evidence determine yesno answer binary natural language question compare several vari... | [0.06912939995527267, 0.0009819197002798319, -0.000473639986012131, 0.045569971203804016, -0.05108325555920601, 0.03289928287267685, -0.02245612069964409, -0.008449180983006954, 0.021231716498732567, -0.041814446449279785, 0.0102603854611516, 0.051032230257987976, -0.028879690915346146, 0.03999879211187363, -0.00059553... |
611 | 611 | ['Xiaodong Gu', 'Hongyu Zhang', 'Dongmei Zhang', 'Sunghun Kim'] | 1605.08535v3 | Developers often wonder how to implement a certain functionality (e.g., how
to parse XML files) using APIs. Obtaining an API usage sequence based on an
API-related natural language query is very helpful in this regard. Given a
query, existing approaches utilize information retrieval models to search for
matching API se... | Deep API Learning | 2,016 | http://arxiv.org/pdf/1605.08535v3 | Title Deep API Learning Summary Developers often wonder implement certain functionality eg parse XML file using APIs Obtaining API usage sequence based APIrelated natural language query helpful regard Given query existing approach utilize information retrieval model search matching API sequence approach treat query API... | [0.022035649046301842, 0.08039530366659164, -0.022920427843928337, 0.05839980021119118, -0.023591330274939537, -0.025041567161679268, -0.018716687336564064, 0.004967098589986563, 0.017906080931425095, -0.03410175442695618, 0.015619850717484951, 0.014092438854277134, 0.019767960533499718, 0.09026051312685013, 0.00809093... |
612 | 612 | ['Ozan Caglayan', 'Walid Aransa', 'Yaxing Wang', 'Marc Masana', 'Mercedes García-Martínez', 'Fethi Bougares', 'Loïc Barrault', 'Joost van de Weijer'] | 1605.09186v4 | This paper presents the systems developed by LIUM and CVC for the WMT16
Multimodal Machine Translation challenge. We explored various comparative
methods, namely phrase-based systems and attentional recurrent neural networks
models trained using monomodal or multimodal data. We also performed a human
evaluation in orde... | Does Multimodality Help Human and Machine for Translation and Image
Captioning? | 2,016 | http://arxiv.org/pdf/1605.09186v4 | Title Multimodality Help Human Machine Translation Image Captioning Summary paper present system developed LIUM CVC WMT16 Multimodal Machine Translation challenge explored various comparative method namely phrasebased system attentional recurrent neural network model trained using monomodal multimodal data also perform... | [0.0539010725915432, 0.0076685454696416855, 0.01904091238975525, 0.06651923060417175, -0.01815539225935936, 0.015360726043581963, 0.03222649171948433, -0.017729010432958603, -0.015600577928125858, -0.06154349073767662, -0.04799551144242287, -0.04930960759520531, 0.044641319662332535, 0.01870400458574295, 0.051146630197... |
613 | 613 | ['Alexis Conneau', 'Holger Schwenk', 'Loïc Barrault', 'Yann Lecun'] | 1606.01781v2 | The dominant approach for many NLP tasks are recurrent neural networks, in
particular LSTMs, and convolutional neural networks. However, these
architectures are rather shallow in comparison to the deep convolutional
networks which have pushed the state-of-the-art in computer vision. We present
a new architecture (VDCNN... | Very Deep Convolutional Networks for Text Classification | 2,016 | http://arxiv.org/pdf/1606.01781v2 | Title Deep Convolutional Networks Text Classification Summary dominant approach many NLP task recurrent neural network particular LSTMs convolutional neural network However architecture rather shallow comparison deep convolutional network pushed stateoftheart computer vision present new architecture VDCNN text processi... | [0.060872286558151245, 0.024899516254663467, 0.0042010704055428505, 0.07925429940223694, -0.017536798492074013, 0.012691543437540531, 0.03025246411561966, 0.02713078074157238, 0.017733626067638397, -0.07143654674291611, -0.014239005744457245, -0.006598727311939001, -0.004835042171180248, 0.044520456343889236, -0.008369... |
614 | 614 | ['Marco Dinarelli', 'Isabelle Tellier'] | 1606.02555v1 | In this paper we study different types of Recurrent Neural Networks (RNN) for
sequence labeling tasks. We propose two new variants of RNNs integrating
improvements for sequence labeling, and we compare them to the more traditional
Elman and Jordan RNNs. We compare all models, either traditional or new, on
four distinct... | Improving Recurrent Neural Networks For Sequence Labelling | 2,016 | http://arxiv.org/pdf/1606.02555v1 | Title Improving Recurrent Neural Networks Sequence Labelling Summary paper study different type Recurrent Neural Networks RNN sequence labeling task propose two new variant RNNs integrating improvement sequence labeling compare traditional Elman Jordan RNNs compare model either traditional new four distinct task sequen... | [0.04753683879971504, -0.0005172011442482471, 0.02614475227892399, 0.044264134019613266, -0.04098315164446831, -0.0019576717168092728, 0.0019845752976834774, -0.02557271532714367, -0.016920369118452072, -0.029783299192786217, -0.01887935772538185, -0.037809696048498154, 0.026239173486828804, 0.026845987886190414, 0.013... |
615 | 615 | ['Marek Rei', 'Ronan Cummins'] | 1606.03144v1 | We investigate the task of assessing sentence-level prompt relevance in
learner essays. Various systems using word overlap, neural embeddings and
neural compositional models are evaluated on two datasets of learner writing.
We propose a new method for sentence-level similarity calculation, which learns
to adjust the we... | Sentence Similarity Measures for Fine-Grained Estimation of Topical
Relevance in Learner Essays | 2,016 | http://arxiv.org/pdf/1606.03144v1 | Title Sentence Similarity Measures FineGrained Estimation Topical Relevance Learner Essays Summary investigate task assessing sentencelevel prompt relevance learner essay Various system using word overlap neural embeddings neural compositional model evaluated two datasets learner writing propose new method sentenceleve... | [0.06661763787269592, 0.027093369513750076, 0.0011273910058662295, 0.0300119798630476, -0.040839653462171555, 0.011714022606611252, 0.019872717559337616, -0.019970372319221497, -0.0007985280826687813, -0.04079223424196243, -0.019642191007733345, -0.009494829922914505, 0.015323278494179249, 0.013418403454124928, -0.0068... |
616 | 616 | ['Hwaran Lee', 'Geonmin Kim', 'Ho-Gyeong Kim', 'Sang-Hoon Oh', 'Soo-Young Lee'] | 1606.03207v2 | Convolutional neural networks (CNNs) with convolutional and pooling
operations along the frequency axis have been proposed to attain invariance to
frequency shifts of features. However, this is inappropriate with regard to the
fact that acoustic features vary in frequency. In this paper, we contend that
convolution alo... | Deep CNNs along the Time Axis with Intermap Pooling for Robustness to
Spectral Variations | 2,016 | http://arxiv.org/pdf/1606.03207v2 | Title Deep CNNs along Time Axis Intermap Pooling Robustness Spectral Variations Summary Convolutional neural network CNNs convolutional pooling operation along frequency axis proposed attain invariance frequency shift feature However inappropriate regard fact acoustic feature vary frequency paper contend convolution al... | [-0.006704570259898901, -0.0024397079832851887, 0.002484459662809968, 0.047621820122003555, -0.01375389751046896, -0.014747148379683495, 0.04437816143035889, 0.00294478889554739, -0.07536257058382034, 0.023285526782274246, -0.07044918090105057, -0.013373246416449547, 0.03883817791938782, 0.020465411245822906, 0.0048978... |
617 | 617 | ['Dimitrios Alikaniotis', 'Helen Yannakoudakis', 'Marek Rei'] | 1606.04289v2 | Automated Text Scoring (ATS) provides a cost-effective and consistent
alternative to human marking. However, in order to achieve good performance,
the predictive features of the system need to be manually engineered by human
experts. We introduce a model that forms word representations by learning the
extent to which s... | Automatic Text Scoring Using Neural Networks | 2,016 | http://arxiv.org/pdf/1606.04289v2 | Title Automatic Text Scoring Using Neural Networks Summary Automated Text Scoring ATS provides costeffective consistent alternative human marking However order achieve good performance predictive feature system need manually engineered human expert introduce model form word representation learning extent specific word ... | [0.03240957856178284, -0.006253769621253014, -0.019459666684269905, 0.02273603342473507, -0.02264132909476757, -0.011064781807363033, 0.018832484260201454, 0.024143334478139877, 0.0169227197766304, -0.059367869049310684, -0.016813043504953384, 0.010927054099738598, 0.061029329895973206, 0.0573597326874733, 0.0074659073... |
618 | 618 | ['Albert Zeyer', 'Patrick Doetsch', 'Paul Voigtlaender', 'Ralf Schlüter', 'Hermann Ney'] | 1606.06871v2 | We present a comprehensive study of deep bidirectional long short-term memory
(LSTM) recurrent neural network (RNN) based acoustic models for automatic
speech recognition (ASR). We study the effect of size and depth and train
models of up to 8 layers. We investigate the training aspect and study
different variants of o... | A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic
Modeling in Speech Recognition | 2,016 | http://arxiv.org/pdf/1606.06871v2 | Title Comprehensive Study Deep Bidirectional LSTM RNNs Acoustic Modeling Speech Recognition Summary present comprehensive study deep bidirectional long shortterm memory LSTM recurrent neural network RNN based acoustic model automatic speech recognition ASR study effect size depth train model 8 layer investigate trainin... | [0.018957188352942467, -0.0008593597449362278, 0.02219080552458763, 0.09828613698482513, -0.019801892340183258, -0.0029993506614118814, 0.02846546471118927, -0.02588002383708954, -0.02469421923160553, -0.025839192792773247, -0.06362873315811157, -0.053315840661525726, 0.0055857026018202305, 0.011284314095973969, 0.0029... |
619 | 619 | ['Yoon Kim', 'Alexander M. Rush'] | 1606.07947v4 | Neural machine translation (NMT) offers a novel alternative formulation of
translation that is potentially simpler than statistical approaches. However to
reach competitive performance, NMT models need to be exceedingly large. In this
paper we consider applying knowledge distillation approaches (Bucila et al.,
2006; Hi... | Sequence-Level Knowledge Distillation | 2,016 | http://arxiv.org/pdf/1606.07947v4 | Title SequenceLevel Knowledge Distillation Summary Neural machine translation NMT offer novel alternative formulation translation potentially simpler statistical approach However reach competitive performance NMT model need exceedingly large paper consider applying knowledge distillation approach Bucila et al 2006 Hint... | [0.042545102536678314, 0.07262073457241058, 0.002678351476788521, 0.030915237963199615, -0.016466815024614334, -0.013895583339035511, 0.00313385808840394, 0.009221713058650494, -0.07044472545385361, -0.038467105478048325, 0.016061589121818542, -0.0013897559838369489, 0.025277916342020035, 0.02924548089504242, 0.0240577... |
620 | 620 | ['Madhusudan Lakshmana', 'Sundararajan Sellamanickam', 'Shirish Shevade', 'Keerthi Selvaraj'] | 1608.00466v2 | The state-of-the-art CNN models give good performance on sentence
classification tasks. The purpose of this work is to empirically study
desirable properties such as semantic coherence, attention mechanism and
reusability of CNNs in these tasks. Semantically coherent kernels are
preferable as they are a lot more interp... | Learning Semantically Coherent and Reusable Kernels in Convolution
Neural Nets for Sentence Classification | 2,016 | http://arxiv.org/pdf/1608.00466v2 | Title Learning Semantically Coherent Reusable Kernels Convolution Neural Nets Sentence Classification Summary stateoftheart CNN model give good performance sentence classification task purpose work empirically study desirable property semantic coherence attention mechanism reusability CNNs task Semantically coherent ke... | [0.028136268258094788, 0.0006274341722019017, 0.009030177257955074, 0.08677586168050766, 9.5728573796805e-05, -0.03109797276556492, -0.007404872216284275, -0.012728176079690456, -0.003021592739969492, -0.06996281445026398, 0.011573974043130875, 0.06878609210252762, -0.01573127880692482, 0.03351372852921486, 0.001849544... |
621 | 621 | ['Patrick Doetsch', 'Albert Zeyer', 'Paul Voigtlaender', 'Ilya Kulikov', 'Ralf Schlüter', 'Hermann Ney'] | 1608.00895v2 | In this work we release our extensible and easily configurable neural network
training software. It provides a rich set of functional layers with a
particular focus on efficient training of recurrent neural network topologies
on multiple GPUs. The source of the software package is public and freely
available for academ... | RETURNN: The RWTH Extensible Training framework for Universal Recurrent
Neural Networks | 2,016 | http://arxiv.org/pdf/1608.00895v2 | Title RETURNN RWTH Extensible Training framework Universal Recurrent Neural Networks Summary work release extensible easily configurable neural network training software provides rich set functional layer particular focus efficient training recurrent neural network topology multiple GPUs source software package public ... | [-0.0106066158041358, 0.029567375779151917, 0.012187649495899677, 0.03967103362083435, -0.01368166133761406, -0.000895736156962812, 0.040856894105672836, 0.004182773642241955, -0.016622059047222137, -0.004811123013496399, -0.03874160349369049, -0.042976900935173035, 0.032456908375024796, 0.055932242423295975, 0.0137951... |
622 | 622 | ['Kyuyeon Hwang', 'Wonyong Sung'] | 1609.03777v2 | Recurrent neural network (RNN) based character-level language models (CLMs)
are extremely useful for modeling out-of-vocabulary words by nature. However,
their performance is generally much worse than the word-level language models
(WLMs), since CLMs need to consider longer history of tokens to properly
predict the nex... | Character-Level Language Modeling with Hierarchical Recurrent Neural
Networks | 2,016 | http://arxiv.org/pdf/1609.03777v2 | Title CharacterLevel Language Modeling Hierarchical Recurrent Neural Networks Summary Recurrent neural network RNN based characterlevel language model CLMs extremely useful modeling outofvocabulary word nature However performance generally much worse wordlevel language model WLMs since CLMs need consider longer history... | [0.03231010586023331, 0.066754549741745, 0.01192980632185936, 0.039426058530807495, -0.03793578967452049, 0.006929038558155298, 0.048987630754709244, 0.01880742609500885, -0.0144269410520792, -0.035498570650815964, -0.007920471951365471, -0.05754922702908516, 0.032995760440826416, 0.03209879249334335, 0.004534644540399... |
623 | 623 | ['Zhiyuan Tang', 'Lantian Li', 'Dong Wang'] | 1609.08337v1 | Research on multilingual speech recognition remains attractive yet
challenging. Recent studies focus on learning shared structures under the
multi-task paradigm, in particular a feature sharing structure. This approach
has been found effective to improve performance on each individual language.
However, this approach i... | Multi-task Recurrent Model for True Multilingual Speech Recognition | 2,016 | http://arxiv.org/pdf/1609.08337v1 | Title Multitask Recurrent Model True Multilingual Speech Recognition Summary Research multilingual speech recognition remains attractive yet challenging Recent study focus learning shared structure multitask paradigm particular feature sharing structure approach found effective improve performance individual language H... | [0.011886225081980228, -0.02644840069115162, 0.0017623017774894834, 0.07945913821458817, -0.020982034504413605, 0.03868953511118889, 0.042908940464258194, -0.020795507356524467, 0.008639167994260788, -0.023615295067429543, -0.09359521418809891, -0.06473927199840546, 0.04473632946610451, 0.0031332438811659813, -0.006202... |
624 | 624 | ['A. Hassan', 'M. R. Amin', 'N. Mohammed', 'A. K. A. Azad'] | 1610.00369v2 | Sentiment Analysis (SA) is an action research area in the digital age. With
rapid and constant growth of online social media sites and services, and the
increasing amount of textual data such as - statuses, comments, reviews etc.
available in them, application of automatic SA is on the rise. However, most of
the resear... | Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep
Recurrent models | 2,016 | http://arxiv.org/pdf/1610.00369v2 | Title Sentiment Analysis Bangla Romanized Bangla Text BRBT using Deep Recurrent model Summary Sentiment Analysis SA action research area digital age rapid constant growth online social medium site service increasing amount textual data status comment review etc available application automatic SA rise However research w... | [0.05192713811993599, 0.027055339887738228, -0.0023078983649611473, 0.05712750926613808, -0.03185954689979553, 0.005102986469864845, -0.018143607303500175, 0.008185272105038166, -0.0019556041806936264, -0.05489837005734444, -0.005273478105664253, -0.059093259274959564, 0.01299042534083128, 0.07535676658153534, -0.02079... |
625 | 625 | ['Marek Rei', 'Gamal K. O. Crichton', 'Sampo Pyysalo'] | 1611.04361v1 | Sequence labeling architectures use word embeddings for capturing similarity,
but suffer when handling previously unseen or rare words. We investigate
character-level extensions to such models and propose a novel architecture for
combining alternative word representations. By using an attention mechanism,
the model is ... | Attending to Characters in Neural Sequence Labeling Models | 2,016 | http://arxiv.org/pdf/1611.04361v1 | Title Attending Characters Neural Sequence Labeling Models Summary Sequence labeling architecture use word embeddings capturing similarity suffer handling previously unseen rare word investigate characterlevel extension model propose novel architecture combining alternative word representation using attention mechanism... | [0.061423782259225845, 0.026141155511140823, 0.010138444602489471, 0.027237990871071815, 0.009262291714549065, 0.022990919649600983, 0.0010185353457927704, -0.012745029293000698, 0.026322072371840477, -0.007542262319475412, -0.021471505984663963, -0.05359182134270668, 0.028383905068039894, 0.04412228241562843, 0.049286... |
626 | 626 | ['Volkan Cirik', 'Eduard Hovy', 'Louis-Philippe Morency'] | 1611.06204v1 | Curriculum Learning emphasizes the order of training instances in a
computational learning setup. The core hypothesis is that simpler instances
should be learned early as building blocks to learn more complex ones. Despite
its usefulness, it is still unknown how exactly the internal representation of
models are affecte... | Visualizing and Understanding Curriculum Learning for Long Short-Term
Memory Networks | 2,016 | http://arxiv.org/pdf/1611.06204v1 | Title Visualizing Understanding Curriculum Learning Long ShortTerm Memory Networks Summary Curriculum Learning emphasizes order training instance computational learning setup core hypothesis simpler instance learned early building block learn complex one Despite usefulness still unknown exactly internal representation ... | [0.003692563623189926, -0.04951534792780876, -0.04816649109125137, 0.01879819668829441, 0.009423155337572098, 0.03182114660739899, 0.018345246091485023, -0.05829724669456482, -0.017054418101906776, -0.014998605474829674, -0.0012486756313592196, 0.014945280738174915, 0.03155054897069931, 0.0015359147218987346, 0.0460324... |
627 | 627 | ['Tom Sercu', 'Vaibhava Goel'] | 1611.09288v2 | In computer vision pixelwise dense prediction is the task of predicting a
label for each pixel in the image. Convolutional neural networks achieve good
performance on this task, while being computationally efficient. In this paper
we carry these ideas over to the problem of assigning a sequence of labels to a
set of sp... | Dense Prediction on Sequences with Time-Dilated Convolutions for Speech
Recognition | 2,016 | http://arxiv.org/pdf/1611.09288v2 | Title Dense Prediction Sequences TimeDilated Convolutions Speech Recognition Summary computer vision pixelwise dense prediction task predicting label pixel image Convolutional neural network achieve good performance task computationally efficient paper carry idea problem assigning sequence label set speech frame task c... | [-0.01365703996270895, 0.03957163169980049, 0.006407111417502165, 0.04693054407835007, -0.008529930375516415, -0.023427637293934822, 0.06719914823770523, 0.002023087115958333, -0.053115736693143845, -0.005807191599160433, -0.014297999441623688, -0.03579714149236679, 0.032626617699861526, 0.06886547803878784, 0.01784059... |
628 | 628 | ['Kartik Audhkhasi', 'Andrew Rosenberg', 'Abhinav Sethy', 'Bhuvana Ramabhadran', 'Brian Kingsbury'] | 1701.04313v1 | End-to-end (E2E) systems have achieved competitive results compared to
conventional hybrid hidden Markov model (HMM)-deep neural network based
automatic speech recognition (ASR) systems. Such E2E systems are attractive due
to the lack of dependence on alignments between input acoustic and output
grapheme or HMM state s... | End-to-End ASR-free Keyword Search from Speech | 2,017 | http://arxiv.org/pdf/1701.04313v1 | Title EndtoEnd ASRfree Keyword Search Speech Summary Endtoend E2E system achieved competitive result compared conventional hybrid hidden Markov model HMMdeep neural network based automatic speech recognition ASR system E2E system attractive due lack dependence alignment input acoustic output grapheme HMM state sequence... | [0.019321588799357414, 0.02940099500119686, 0.027120331302285194, 0.0550609789788723, -0.006799870170652866, -0.020971085876226425, 0.003670979058369994, 0.0041054971516132355, -0.023339271545410156, -0.05908973887562752, -0.03312704339623451, 0.004711388610303402, -0.012850308790802956, 0.06760329753160477, 0.00777878... |
629 | 629 | ['Sam Wiseman', 'Sumit Chopra', "Marc'Aurelio Ranzato", 'Arthur Szlam', 'Ruoyu Sun', 'Soumith Chintala', 'Nicolas Vasilache'] | 1702.04770v1 | While Truncated Back-Propagation through Time (BPTT) is the most popular
approach to training Recurrent Neural Networks (RNNs), it suffers from being
inherently sequential (making parallelization difficult) and from truncating
gradient flow between distant time-steps. We investigate whether Target
Propagation (TPROP) s... | Training Language Models Using Target-Propagation | 2,017 | http://arxiv.org/pdf/1702.04770v1 | Title Training Language Models Using TargetPropagation Summary Truncated BackPropagation Time BPTT popular approach training Recurrent Neural Networks RNNs suffers inherently sequential making parallelization difficult truncating gradient flow distant timesteps investigate whether Target Propagation TPROP style approac... | [0.022913193330168724, 0.018226375803351402, -0.0020813068840652704, 0.01972082257270813, -0.017786908894777298, -0.022440539672970772, 0.020408382639288902, -0.02054072543978691, -0.09643825143575668, -0.04322342947125435, -0.044961269944906235, -0.05960092693567276, 0.041636064648628235, 0.0011985463788732886, 0.0354... |
630 | 630 | ['Sercan O. Arik', 'Mike Chrzanowski', 'Adam Coates', 'Gregory Diamos', 'Andrew Gibiansky', 'Yongguo Kang', 'Xian Li', 'John Miller', 'Andrew Ng', 'Jonathan Raiman', 'Shubho Sengupta', 'Mohammad Shoeybi'] | 1702.07825v2 | We present Deep Voice, a production-quality text-to-speech system constructed
entirely from deep neural networks. Deep Voice lays the groundwork for truly
end-to-end neural speech synthesis. The system comprises five major building
blocks: a segmentation model for locating phoneme boundaries, a
grapheme-to-phoneme conv... | Deep Voice: Real-time Neural Text-to-Speech | 2,017 | http://arxiv.org/pdf/1702.07825v2 | Title Deep Voice Realtime Neural TexttoSpeech Summary present Deep Voice productionquality texttospeech system constructed entirely deep neural network Deep Voice lay groundwork truly endtoend neural speech synthesis system comprises five major building block segmentation model locating phoneme boundary graphemetophone... | [-0.009917370043694973, 0.047601792961359024, 0.015423452481627464, 0.02271011844277382, 0.007447405252605677, -0.04852304980158806, 0.039579831063747406, 0.010334247723221779, -0.051909007132053375, -0.01405063085258007, -0.04730913043022156, -0.025458768010139465, 0.044778551906347275, 0.0770290419459343, 0.020705791... |
631 | 631 | ['Zichao Yang', 'Zhiting Hu', 'Ruslan Salakhutdinov', 'Taylor Berg-Kirkpatrick'] | 1702.08139v2 | Recent work on generative modeling of text has found that variational
auto-encoders (VAE) incorporating LSTM decoders perform worse than simpler LSTM
language models (Bowman et al., 2015). This negative result is so far poorly
understood, but has been attributed to the propensity of LSTM decoders to
ignore conditioning... | Improved Variational Autoencoders for Text Modeling using Dilated
Convolutions | 2,017 | http://arxiv.org/pdf/1702.08139v2 | Title Improved Variational Autoencoders Text Modeling using Dilated Convolutions Summary Recent work generative modeling text found variational autoencoders VAE incorporating LSTM decoder perform worse simpler LSTM language model Bowman et al 2015 negative result far poorly understood attributed propensity LSTM decoder... | [0.031139275059103966, 0.08634331822395325, -0.017654934898018837, 0.04814178869128227, -0.019941547885537148, 0.008397197350859642, 0.024982603266835213, -0.0009738968801684678, -0.06876417994499207, -0.0027986273635178804, -0.01723877340555191, -0.04735134541988373, -0.016995685175061226, 0.08497116714715958, 0.02997... |
632 | 632 | ['Hairong Liu', 'Zhenyao Zhu', 'Xiangang Li', 'Sanjeev Satheesh'] | 1703.00096v2 | Most existing sequence labelling models rely on a fixed decomposition of a
target sequence into a sequence of basic units. These methods suffer from two
major drawbacks: 1) the set of basic units is fixed, such as the set of words,
characters or phonemes in speech recognition, and 2) the decomposition of
target sequenc... | Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence
Labelling | 2,017 | http://arxiv.org/pdf/1703.00096v2 | Title GramCTC Automatic Unit Selection Target Decomposition Sequence Labelling Summary existing sequence labelling model rely fixed decomposition target sequence sequence basic unit method suffer two major drawback 1 set basic unit fixed set word character phoneme speech recognition 2 decomposition target sequence fixe... | [0.029830938205122948, 0.04454585537314415, 0.034033939242362976, 0.027482159435749054, -0.016133543103933334, 0.0429430790245533, 0.019833918660879135, 0.0166368018835783, -0.020888321101665497, -0.02717079408466816, -0.000637555553112179, -0.029659390449523926, 0.0503210611641407, 0.0027869197074323893, -0.0121830888... |
633 | 633 | ['Govardana Sachithanandam Ramachandran', 'Ajay Sohmshetty'] | 1703.03939v1 | We examine Memory Networks for the task of question answering (QA), under
common real world scenario where training examples are scarce and under weakly
supervised scenario, that is only extrinsic labels are available for training.
We propose extensions for the Dynamic Memory Network (DMN), specifically within
the atte... | Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model) | 2,017 | http://arxiv.org/pdf/1703.03939v1 | Title Ask Even Dynamic Memory Tensor Networks Extended Model Summary examine Memory Networks task question answering QA common real world scenario training example scarce weakly supervised scenario extrinsic label available training propose extension Dynamic Memory Network DMN specifically within attention mechanism ca... | [0.01754993386566639, -0.01755429059267044, -0.037438519299030304, 0.00472089322283864, -0.007093632128089666, 0.03203025832772255, 0.011884788051247597, -0.020065026357769966, -0.010369201190769672, -0.016145899891853333, -0.0012571010738611221, -0.04139208793640137, -0.013332019560039043, 0.0303955115377903, 0.047639... |
634 | 634 | ['Lior Fritz', 'David Burshtein'] | 1703.10356v2 | A simplified speech recognition system that uses the maximum mutual
information (MMI) criterion is considered. End-to-end training using gradient
descent is suggested, similarly to the training of connectionist temporal
classification (CTC). We use an MMI criterion with a simple language model in
the training stage, an... | Simplified End-to-End MMI Training and Voting for ASR | 2,017 | http://arxiv.org/pdf/1703.10356v2 | Title Simplified EndtoEnd MMI Training Voting ASR Summary simplified speech recognition system us maximum mutual information MMI criterion considered Endtoend training using gradient descent suggested similarly training connectionist temporal classification CTC use MMI criterion simple language model training stage sta... | [-0.026127465069293976, 0.040844596922397614, 0.02768903411924839, 0.027729712426662445, 0.0038226251490414143, 0.022259080782532692, 0.006353217642754316, -0.0174814872443676, -0.0220170971006155, -0.013572930358350277, -0.042083434760570526, -0.01985441893339157, 0.07458272576332092, 0.010344461537897587, -0.01860390... |
635 | 635 | ['Alec Radford', 'Rafal Jozefowicz', 'Ilya Sutskever'] | 1704.01444v2 | We explore the properties of byte-level recurrent language models. When given
sufficient amounts of capacity, training data, and compute time, the
representations learned by these models include disentangled features
corresponding to high-level concepts. Specifically, we find a single unit which
performs sentiment anal... | Learning to Generate Reviews and Discovering Sentiment | 2,017 | http://arxiv.org/pdf/1704.01444v2 | Title Learning Generate Reviews Discovering Sentiment Summary explore property bytelevel recurrent language model given sufficient amount capacity training data compute time representation learned model include disentangled feature corresponding highlevel concept Specifically find single unit performs sentiment analysi... | [0.04611093923449516, 0.05558440461754799, -0.005580800119787455, 0.02697698585689068, -0.04319903999567032, 0.011704379692673683, -0.022924331948161125, 0.0033520604483783245, -0.012462098151445389, -0.06253533810377121, 0.004261876456439495, -0.01009831391274929, -0.013715391047298908, 0.09788276255130768, -0.0238393... |
636 | 636 | ['Marek Rei'] | 1704.07156v1 | We propose a sequence labeling framework with a secondary training objective,
learning to predict surrounding words for every word in the dataset. This
language modeling objective incentivises the system to learn general-purpose
patterns of semantic and syntactic composition, which are also useful for
improving accurac... | Semi-supervised Multitask Learning for Sequence Labeling | 2,017 | http://arxiv.org/pdf/1704.07156v1 | Title Semisupervised Multitask Learning Sequence Labeling Summary propose sequence labeling framework secondary training objective learning predict surrounding word every word dataset language modeling objective incentivises system learn generalpurpose pattern semantic syntactic composition also useful improving accura... | [0.05621160566806793, -0.00344303366728127, 0.021400338038802147, 0.00940660759806633, -0.03424806892871857, 0.039948977530002594, -0.0034205468837171793, -0.040986593812704086, 0.03295162692666054, -0.06236645206809044, -0.036111000925302505, -0.030893437564373016, -0.014165043830871582, 0.058400969952344894, 0.019437... |
637 | 637 | ['Danhao Zhu', 'Si Shen', 'Xin-Yu Dai', 'Jiajun Chen'] | 1705.01346v1 | Recurrent Neural Network (RNN) has been widely applied for sequence modeling.
In RNN, the hidden states at current step are full connected to those at
previous step, thus the influence from less related features at previous step
may potentially decrease model's learning ability. We propose a simple
technique called par... | Going Wider: Recurrent Neural Network With Parallel Cells | 2,017 | http://arxiv.org/pdf/1705.01346v1 | Title Going Wider Recurrent Neural Network Parallel Cells Summary Recurrent Neural Network RNN widely applied sequence modeling RNN hidden state current step full connected previous step thus influence le related feature previous step may potentially decrease model learning ability propose simple technique called paral... | [0.009379123337566853, 0.025332294404506683, -0.024564344435930252, 0.029763156548142433, -0.04892450571060181, 0.014327235519886017, 0.052719030529260635, -0.021398676559329033, -0.05949936434626579, -0.007525046821683645, 0.010927753522992134, -0.06231151521205902, 0.04505552724003792, 0.01007845439016819, 0.02437165... |
638 | 638 | ['Zhiyuan Tang', 'Dong Wang', 'Yixiang Chen', 'Lantian Li', 'Andrew Abel'] | 1705.03151v3 | Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We ... | Phonetic Temporal Neural Model for Language Identification | 2,017 | http://arxiv.org/pdf/1705.03151v3 | Title Phonetic Temporal Neural Model Language Identification Summary Deep neural model particularly LSTMRNN model shown great potential language identification LID However use phonetic information largely overlooked existing neural LID method although information used successfully conventional phonetic LID system prese... | [0.010538937523961067, 0.051746804267168045, 0.009194403886795044, 0.02748473547399044, -0.019157063215970993, 0.004291570279747248, 0.04313242807984352, -0.024848293513059616, -0.01615096442401409, 0.013568012043833733, -0.02703378163278103, -0.058014191687107086, 0.07893027365207672, 9.135649634117726e-06, 0.01781799... |
639 | 639 | ['Hamed Zamani', 'W. Bruce Croft'] | 1705.03556v2 | Learning a high-dimensional dense representation for vocabulary terms, also
known as a word embedding, has recently attracted much attention in natural
language processing and information retrieval tasks. The embedding vectors are
typically learned based on term proximity in a large corpus. This means that
the objectiv... | Relevance-based Word Embedding | 2,017 | http://arxiv.org/pdf/1705.03556v2 | Title Relevancebased Word Embedding Summary Learning highdimensional dense representation vocabulary term also known word embedding recently attracted much attention natural language processing information retrieval task embedding vector typically learned based term proximity large corpus mean objective wellknown word ... | [0.055417437106370926, 0.007513559889048338, 0.005542187485843897, 0.029102232307195663, -0.034605324268341064, -0.012237999588251114, -0.03458545729517937, 0.024213548749685287, -0.010251839645206928, -0.06271302700042725, -0.033824384212493896, 0.0020126882009208202, -0.013109618797898293, 0.021384557709097862, 0.006... |
640 | 640 | ['Tao Lei', 'Wengong Jin', 'Regina Barzilay', 'Tommi Jaakkola'] | 1705.09037v3 | The design of neural architectures for structured objects is typically guided
by experimental insights rather than a formal process. In this work, we appeal
to kernels over combinatorial structures, such as sequences and graphs, to
derive appropriate neural operations. We introduce a class of deep recurrent
neural oper... | Deriving Neural Architectures from Sequence and Graph Kernels | 2,017 | http://arxiv.org/pdf/1705.09037v3 | Title Deriving Neural Architectures Sequence Graph Kernels Summary design neural architecture structured object typically guided experimental insight rather formal process work appeal kernel combinatorial structure sequence graph derive appropriate neural operation introduce class deep recurrent neural operation formal... | [0.014733297750353813, 0.04007579758763313, -0.02269957773387432, 0.011624796316027641, -0.007425493560731411, -0.0345165878534317, 0.004953464493155479, 0.03830841928720474, 0.024719465523958206, 0.001643794821575284, 0.05051158741116524, -0.012354633770883083, -0.002367529785260558, 0.09574262797832489, 0.03041026182... |
641 | 641 | ['Michał Zapotoczny', 'Paweł Rychlikowski', 'Jan Chorowski'] | 1705.10209v1 | We show that a recently proposed neural dependency parser can be improved by
joint training on multiple languages from the same family. The parser is
implemented as a deep neural network whose only input is orthographic
representations of words. In order to successfully parse, the network has to
discover how linguistic... | On Multilingual Training of Neural Dependency Parsers | 2,017 | http://arxiv.org/pdf/1705.10209v1 | Title Multilingual Training Neural Dependency Parsers Summary show recently proposed neural dependency parser improved joint training multiple language family parser implemented deep neural network whose input orthographic representation word order successfully parse network discover linguistically relevant concept inf... | [0.01944381557404995, 0.052187707275152206, 0.013219852931797504, 0.08311734348535538, -0.046065762639045715, 0.02014671266078949, 0.0015334389172494411, -0.010520074516534805, -0.010884782299399376, -0.013641287572681904, 0.007354326080530882, -0.037044864147901535, 0.016053467988967896, -0.0013310664799064398, 0.0089... |
642 | 642 | ['Marian Tietz', 'Tayfun Alpay', 'Johannes Twiefel', 'Stefan Wermter'] | 1706.02124v2 | Ladder networks are a notable new concept in the field of semi-supervised
learning by showing state-of-the-art results in image recognition tasks while
being compatible with many existing neural architectures. We present the
recurrent ladder network, a novel modification of the ladder network, for
semi-supervised learn... | Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks | 2,017 | http://arxiv.org/pdf/1706.02124v2 | Title SemiSupervised Phoneme Recognition Recurrent Ladder Networks Summary Ladder network notable new concept field semisupervised learning showing stateoftheart result image recognition task compatible many existing neural architecture present recurrent ladder network novel modification ladder network semisupervised l... | [0.008167542517185211, 0.0533309131860733, 0.02403194271028042, 0.020374435931444168, 0.018757058307528496, -0.005465107969939709, 0.037153396755456924, -0.020483996719121933, 0.0333324559032917, 0.033537209033966064, -0.05731246620416641, -0.034057050943374634, 0.019655730575323105, 0.0464630089700222, 0.0181184187531... |
643 | 643 | ['Junbo', 'Zhao', 'Yoon Kim', 'Kelly Zhang', 'Alexander M. Rush', 'Yann LeCun'] | 1706.04223v2 | While autoencoders are a key technique in representation learning for
continuous structures, such as images or wave forms, developing general-purpose
autoencoders for discrete structures, such as text sequence or discretized
images, has proven to be more challenging. In particular, discrete inputs make
it more difficul... | Adversarially Regularized Autoencoders | 2,017 | http://arxiv.org/pdf/1706.04223v2 | Title Adversarially Regularized Autoencoders Summary autoencoders key technique representation learning continuous structure image wave form developing generalpurpose autoencoders discrete structure text sequence discretized image proven challenging particular discrete input make difficult learn smooth encoder preserve... | [0.003998361993581057, 0.10605769604444504, -0.023995626717805862, 0.047150611877441406, 0.011567512527108192, -0.005254093091934919, 0.02776133269071579, 0.010005134157836437, -0.03373934328556061, 0.008464936167001724, -0.037195928394794464, 0.001652729930356145, -0.002006764989346266, 0.05625103786587715, 0.06576611... |
644 | 644 | ['Marek Rei', 'Helen Yannakoudakis'] | 1707.05227v1 | We investigate the utility of different auxiliary objectives and training
strategies within a neural sequence labeling approach to error detection in
learner writing. Auxiliary costs provide the model with additional linguistic
information, allowing it to learn general-purpose compositional features that
can then be ex... | Auxiliary Objectives for Neural Error Detection Models | 2,017 | http://arxiv.org/pdf/1707.05227v1 | Title Auxiliary Objectives Neural Error Detection Models Summary investigate utility different auxiliary objective training strategy within neural sequence labeling approach error detection learner writing Auxiliary cost provide model additional linguistic information allowing learn generalpurpose compositional feature... | [0.053201671689748764, 0.03163161873817444, 0.020694365724921227, 0.008173013105988503, 0.015069123357534409, 0.03954949602484703, 0.004487816710025072, 0.004229953978210688, -0.010595637373626232, -0.049160826951265335, 0.0034147666301578283, -0.036453258246183395, 0.029932990670204163, 0.0006174460286274552, 0.005157... |
645 | 645 | ['Youmna Farag', 'Marek Rei', 'Ted Briscoe'] | 1707.06841v1 | We propose a novel word embedding pre-training approach that exploits writing
errors in learners' scripts. We compare our method to previous models that tune
the embeddings based on script scores and the discrimination between correct
and corrupt word contexts in addition to the generic commonly-used embeddings
pre-tra... | An Error-Oriented Approach to Word Embedding Pre-Training | 2,017 | http://arxiv.org/pdf/1707.06841v1 | Title ErrorOriented Approach Word Embedding PreTraining Summary propose novel word embedding pretraining approach exploit writing error learner script compare method previous model tune embeddings based script score discrimination correct corrupt word context addition generic commonlyused embeddings pretrained large co... | [0.009240753017365932, 0.028996504843235016, 0.02859247289597988, 0.041179753839969635, 0.01521011721342802, 0.008262046612799168, -0.022857774049043655, 0.0456257164478302, 0.02617158554494381, -0.0855959951877594, -0.03694905713200569, -0.012926281429827213, 0.02177080698311329, 0.04429576173424721, 0.032448224723339... |
646 | 646 | ['Kartik Goyal', 'Graham Neubig', 'Chris Dyer', 'Taylor Berg-Kirkpatrick'] | 1708.00111v2 | Beam search is a desirable choice of test-time decoding algorithm for neural
sequence models because it potentially avoids search errors made by simpler
greedy methods. However, typical cross entropy training procedures for these
models do not directly consider the behaviour of the final decoding method. As
a result, f... | A Continuous Relaxation of Beam Search for End-to-end Training of Neural
Sequence Models | 2,017 | http://arxiv.org/pdf/1708.00111v2 | Title Continuous Relaxation Beam Search Endtoend Training Neural Sequence Models Summary Beam search desirable choice testtime decoding algorithm neural sequence model potentially avoids search error made simpler greedy method However typical cross entropy training procedure model directly consider behaviour final deco... | [0.03741150721907616, 0.030471503734588623, 0.03220384195446968, 0.025864267721772194, 0.0011311826528981328, 0.03016337752342224, -0.016953421756625175, 0.013072589412331581, 0.004709469620138407, 0.01821279339492321, -0.028442755341529846, -0.03348269686102867, 0.00608800770714879, -0.007798672188073397, -0.009676429... |
647 | 647 | ['Stephen Merity', 'Nitish Shirish Keskar', 'Richard Socher'] | 1708.02182v1 | Recurrent neural networks (RNNs), such as long short-term memory networks
(LSTMs), serve as a fundamental building block for many sequence learning
tasks, including machine translation, language modeling, and question
answering. In this paper, we consider the specific problem of word-level
language modeling and investi... | Regularizing and Optimizing LSTM Language Models | 2,017 | http://arxiv.org/pdf/1708.02182v1 | Title Regularizing Optimizing LSTM Language Models Summary Recurrent neural network RNNs long shortterm memory network LSTMs serve fundamental building block many sequence learning task including machine translation language modeling question answering paper consider specific problem wordlevel language modeling investi... | [0.03623424842953682, 0.055790528655052185, 0.004418463911861181, 0.06507393717765808, -0.03167427331209183, -0.003000459400936961, 0.013029449619352818, 0.016414618119597435, -0.02476254291832447, -0.019145308062434196, -0.03168972209095955, -0.07381616532802582, 0.05584363266825676, 0.011357031762599945, 0.0064641078... |
648 | 648 | ['DeLiang Wang', 'Jitong Chen'] | 1708.07524v1 | Speech separation is the task of separating target speech from background
interference. Traditionally, speech separation is studied as a signal
processing problem. A more recent approach formulates speech separation as a
supervised learning problem, where the discriminative patterns of speech,
speakers, and background ... | Supervised Speech Separation Based on Deep Learning: An Overview | 2,017 | http://arxiv.org/pdf/1708.07524v1 | Title Supervised Speech Separation Based Deep Learning Overview Summary Speech separation task separating target speech background interference Traditionally speech separation studied signal processing problem recent approach formulates speech separation supervised learning problem discriminative pattern speech speaker... | [0.038107819855213165, 0.01861475221812725, 0.027826201170682907, 0.046899646520614624, 0.004771906416863203, -0.03258495032787323, 0.07156315445899963, -0.01173003576695919, -0.0398559495806694, -0.00904389750212431, -0.06965471059083939, -0.01244617160409689, 0.0264066644012928, 0.004790692590177059, -0.0230887718498... |
649 | 649 | ['Marek Rei', 'Luana Bulat', 'Douwe Kiela', 'Ekaterina Shutova'] | 1709.00575v1 | The ubiquity of metaphor in our everyday communication makes it an important
problem for natural language understanding. Yet, the majority of metaphor
processing systems to date rely on hand-engineered features and there is still
no consensus in the field as to which features are optimal for this task. In
this paper, w... | Grasping the Finer Point: A Supervised Similarity Network for Metaphor
Detection | 2,017 | http://arxiv.org/pdf/1709.00575v1 | Title Grasping Finer Point Supervised Similarity Network Metaphor Detection Summary ubiquity metaphor everyday communication make important problem natural language understanding Yet majority metaphor processing system date rely handengineered feature still consensus field feature optimal task paper present first deep ... | [0.05495205149054527, 0.00798508059233427, -0.023405704647302628, 0.03188026323914528, -0.017655406147241592, 0.023375147953629494, -0.003949107602238655, -0.014763303101062775, -0.01064575556665659, -0.025932954624295235, 0.009613321162760258, -0.0046531278640031815, 0.022552382200956345, 0.010603792034089565, 0.01836... |
650 | 650 | ['Danushka Bollegala', 'Kohei Hayashi', 'Ken-ichi Kawarabayashi'] | 1709.06671v1 | Distributed word embeddings have shown superior performances in numerous
Natural Language Processing (NLP) tasks. However, their performances vary
significantly across different tasks, implying that the word embeddings learnt
by those methods capture complementary aspects of lexical semantics. Therefore,
we believe tha... | Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words | 2,017 | http://arxiv.org/pdf/1709.06671v1 | Title Think Globally Embed Locally Locally Linear Metaembedding Words Summary Distributed word embeddings shown superior performance numerous Natural Language Processing NLP task However performance vary significantly across different task implying word embeddings learnt method capture complementary aspect lexical sema... | [0.012094358913600445, 0.01215886790305376, 0.007214866112917662, 0.071959488093853, -0.008110486902296543, -0.003673032158985734, 0.007556939963251352, -0.031710900366306305, -0.05535687133669853, -0.05274314433336258, -0.04593686759471893, 0.07059600949287415, 0.0120661910623312, -0.016054438427090645, 0.029526535421... |
651 | 651 | ['Bin Bi', 'Hao Ma'] | 1709.09749v1 | Previous studies have demonstrated the empirical success of word embeddings
in various applications. In this paper, we investigate the problem of learning
distributed representations for text documents which many machine learning
algorithms take as input for a number of NLP tasks.
We propose a neural network model, K... | KeyVec: Key-semantics Preserving Document Representations | 2,017 | http://arxiv.org/pdf/1709.09749v1 | Title KeyVec Keysemantics Preserving Document Representations Summary Previous study demonstrated empirical success word embeddings various application paper investigate problem learning distributed representation text document many machine learning algorithm take input number NLP task propose neural network model KeyV... | [0.04206375405192375, -0.0009373161592520773, 0.021352212876081467, 0.03221099078655243, -0.022413088008761406, 0.015619372017681599, -0.0132781146094203, 0.039899419993162155, -0.010965495370328426, -0.0836675688624382, 0.008697493001818657, 0.010289817117154598, -0.008964930661022663, 0.0367455892264843, 0.0169996228... |
652 | 652 | ['Shuai Tang', 'Hailin Jin', 'Chen Fang', 'Zhaowen Wang', 'Virginia R. de Sa'] | 1710.10380v2 | Context information plays an important role in human language understanding,
and it is also useful for machines to learn vector representations of language.
In this paper, we explore an asymmetric encoder-decoder structure for
unsupervised context-based sentence representation learning. As a result, we
build an encoder... | Exploring Asymmetric Encoder-Decoder Structure for Context-based
Sentence Representation Learning | 2,017 | http://arxiv.org/pdf/1710.10380v2 | Title Exploring Asymmetric EncoderDecoder Structure Contextbased Sentence Representation Learning Summary Context information play important role human language understanding also useful machine learn vector representation language paper explore asymmetric encoderdecoder structure unsupervised contextbased sentence rep... | [0.031040281057357788, 0.012019283138215542, -0.020153719931840897, 0.10539435595273972, -0.011638340540230274, 0.0379156768321991, 0.016123764216899872, -0.021891169250011444, -0.071658194065094, -0.08328808844089508, -0.021733807399868965, -0.017984628677368164, -0.016857003793120384, 0.02559058926999569, -0.00907099... |
653 | 653 | ['Qiming Chen', 'Ren Wu'] | 1712.09662v1 | The Convolution Neural Network (CNN) has demonstrated the unique advantage in
audio, image and text learning; recently it has also challenged Recurrent
Neural Networks (RNNs) with long short-term memory cells (LSTM) in
sequence-to-sequence learning, since the computations involved in CNN are
easily parallelizable where... | CNN Is All You Need | 2,017 | http://arxiv.org/pdf/1712.09662v1 | Title CNN Need Summary Convolution Neural Network CNN demonstrated unique advantage audio image text learning recently also challenged Recurrent Neural Networks RNNs long shortterm memory cell LSTM sequencetosequence learning since computation involved CNN easily parallelizable whereas involved RNN mostly sequential le... | [0.023207545280456543, 0.055911850184202194, -0.016193347051739693, 0.03825205937027931, 0.010565239936113358, -0.011192799545824528, 0.031835008412599564, 0.025277020409703255, -0.055737681686878204, -0.008467582054436207, -0.018891112878918648, -0.024584462866187096, 0.026755500584840775, -0.0033087453339248896, 0.01... |
654 | 654 | ['Tim Rocktäschel'] | 1712.09687v1 | The current state-of-the-art in many natural language processing and
automated knowledge base completion tasks is held by representation learning
methods which learn distributed vector representations of symbols via
gradient-based optimization. They require little or no hand-crafted features,
thus avoiding the need for... | Combining Representation Learning with Logic for Language Processing | 2,017 | http://arxiv.org/pdf/1712.09687v1 | Title Combining Representation Learning Logic Language Processing Summary current stateoftheart many natural language processing automated knowledge base completion task held representation learning method learn distributed vector representation symbol via gradientbased optimization require little handcrafted feature t... | [0.04245384782552719, -0.010935433208942413, -0.00979213509708643, 0.019917266443371773, -0.03041921928524971, 0.02238231897354126, -0.009313097223639488, 0.012592816725373268, 0.06660055369138718, -0.09160167723894119, 0.018299521878361702, 0.04960286617279053, 0.0038200649432837963, 0.05390718951821327, 0.01200169976... |
655 | 655 | ['Dietmar Volz'] | 1308.1603v4 | It will be shown that according to theorems of K. Menger, every neuron grid
if identified with a curve is able to preserve the adopted qualitative
structure of a data space. Furthermore, if this identification is made, the
neuron grid structure can always be mapped to a subset of a universal neuron
grid which is constr... | A Note on Topology Preservation in Classification, and the Construction
of a Universal Neuron Grid | 2,013 | http://arxiv.org/pdf/1308.1603v4 | Title Note Topology Preservation Classification Construction Universal Neuron Grid Summary shown according theorem K Menger every neuron grid identified curve able preserve adopted qualitative structure data space Furthermore identification made neuron grid structure always mapped subset universal neuron grid construct... | [-0.018376406282186508, 0.009212622418999672, -0.02799879014492035, 0.002389247063547373, -0.012164520099759102, 0.007206049747765064, 0.07893484085798264, -0.014987609349191189, 0.008515902794897556, 0.027642400935292244, -0.05880561098456383, 0.001487764879129827, 0.009216845966875553, 0.04748561978340149, 0.06948754... |
656 | 656 | ['Louis Yuanlong Shao'] | 1210.8442v3 | One conjecture in both deep learning and classical connectionist viewpoint is
that the biological brain implements certain kinds of deep networks as its
back-end. However, to our knowledge, a detailed correspondence has not yet been
set up, which is important if we want to bridge between neuroscience and
machine learni... | Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On
Boltzmann Machines | 2,012 | http://arxiv.org/pdf/1210.8442v3 | Title LinearNonlinearPoisson Neuron Networks Perform Bayesian Inference Boltzmann Machines Summary One conjecture deep learning classical connectionist viewpoint biological brain implement certain kind deep network backend However knowledge detailed correspondence yet set important want bridge neuroscience machine lear... | [-0.04433208703994751, 0.0404491052031517, -0.029001904651522636, 0.02259504236280918, -0.025148529559373856, 0.007500724866986275, 0.043871693313121796, -0.01455585565418005, -0.03772580251097679, -0.005837182514369488, -0.030823180451989174, 0.01564974896609783, 0.004207680933177471, 0.05848691985011101, 0.0803089365... |
657 | 657 | ['Enmei Tu', 'Nikola Kasabov', 'Jie Yang'] | 1603.05594v1 | This paper proposes a new method for an optimized mapping of temporal
variables, describing a temporal stream data, into the recently proposed
NeuCube spiking neural network architecture. This optimized mapping extends the
use of the NeuCube, which was initially designed for spatiotemporal brain data,
to work on arbitr... | Mapping Temporal Variables into the NeuCube for Improved Pattern
Recognition, Predictive Modelling and Understanding of Stream Data | 2,016 | http://arxiv.org/pdf/1603.05594v1 | Title Mapping Temporal Variables NeuCube Improved Pattern Recognition Predictive Modelling Understanding Stream Data Summary paper proposes new method optimized mapping temporal variable describing temporal stream data recently proposed NeuCube spiking neural network architecture optimized mapping extends use NeuCube i... | [-0.03617202118039131, -0.012301030568778515, -0.032941706478595734, -0.005760688800364733, 0.004749706480652094, 0.016142960637807846, 0.019739855080842972, -0.02388072945177555, -0.021417731419205666, 0.01587310992181301, 0.03027486428618431, -0.020441768690943718, 0.025372151285409927, 0.1118316650390625, 0.02946531... |
658 | 658 | ['Jörn Hees', 'Rouven Bauer', 'Joachim Folz', 'Damian Borth', 'Andreas Dengel'] | 1607.07249v3 | Efficient usage of the knowledge provided by the Linked Data community is
often hindered by the need for domain experts to formulate the right SPARQL
queries to answer questions. For new questions they have to decide which
datasets are suitable and in which terminology and modelling style to phrase
the SPARQL query.
... | An Evolutionary Algorithm to Learn SPARQL Queries for
Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia | 2,016 | http://arxiv.org/pdf/1607.07249v3 | Title Evolutionary Algorithm Learn SPARQL Queries SourceTargetPairs Finding Patterns Human Associations DBpedia Summary Efficient usage knowledge provided Linked Data community often hindered need domain expert formulate right SPARQL query answer question new question decide datasets suitable terminology modelling styl... | [0.05303521826863289, 0.0714755430817604, -0.023572899401187897, -0.0002667703665792942, 0.0017019326332956553, -0.007072402164340019, 0.00022472212731372565, 0.00924236886203289, 0.014618105255067348, -0.013973248191177845, 0.010560072027146816, 0.06168920919299126, -0.004193740896880627, 0.08381201326847076, -0.00691... |
659 | 659 | ['Anthony L. Caterini', 'Dong Eui Chang'] | 1608.04374v2 | In this paper, a geometric framework for neural networks is proposed. This
framework uses the inner product space structure underlying the parameter set
to perform gradient descent not in a component-based form, but in a
coordinate-free manner. Convolutional neural networks are described in this
framework in a compact ... | A Geometric Framework for Convolutional Neural Networks | 2,016 | http://arxiv.org/pdf/1608.04374v2 | Title Geometric Framework Convolutional Neural Networks Summary paper geometric framework neural network proposed framework us inner product space structure underlying parameter set perform gradient descent componentbased form coordinatefree manner Convolutional neural network described framework compact form gradient ... | [-0.0062927547842264175, 0.02175666019320488, -0.01843675784766674, 0.05009322240948677, 0.00307169440202415, 0.0009931556414812803, 0.06940880417823792, -0.007466486655175686, -0.00551449041813612, 0.02989242784678936, 0.01591523550450802, 0.00019580336811486632, 0.0005112610524520278, 0.043925248086452484, 0.03991241... |
660 | 660 | ['Anthony Caterini', 'Dong Eui Chang'] | 1610.01549v2 | Deep Neural Networks (DNNs) have become very popular for prediction in many
areas. Their strength is in representation with a high number of parameters
that are commonly learned via gradient descent or similar optimization methods.
However, the representation is non-standardized, and the gradient calculation
methods ar... | A Novel Representation of Neural Networks | 2,016 | http://arxiv.org/pdf/1610.01549v2 | Title Novel Representation Neural Networks Summary Deep Neural Networks DNNs become popular prediction many area strength representation high number parameter commonly learned via gradient descent similar optimization method However representation nonstandardized gradient calculation method often performed using compon... | [-0.04897966608405113, 0.018185332417488098, -0.04835548624396324, 0.03892781585454941, 0.01695767045021057, -0.028132939711213112, 0.06393872946500778, -0.034992609173059464, -0.01274050772190094, -0.003143599722534418, -0.04479924961924553, -0.010154358111321926, 0.009778140112757683, 0.01962733082473278, 0.026690669... |
661 | 661 | ['Ardavan Salehi Nobandegani', 'Thomas R. Shultz'] | 1701.05004v1 | Humans are not only adept in recognizing what class an input instance belongs
to (i.e., classification task), but perhaps more remarkably, they can imagine
(i.e., generate) plausible instances of a desired class with ease, when
prompted. Inspired by this, we propose a framework which allows transforming
Cascade-Correla... | Converting Cascade-Correlation Neural Nets into Probabilistic Generative
Models | 2,017 | http://arxiv.org/pdf/1701.05004v1 | Title Converting CascadeCorrelation Neural Nets Probabilistic Generative Models Summary Humans adept recognizing class input instance belongs ie classification task perhaps remarkably imagine ie generate plausible instance desired class ease prompted Inspired propose framework allows transforming CascadeCorrelation Neu... | [0.00014280565665103495, 0.09656462073326111, -0.032682426273822784, 0.012405757792294025, -0.03379938006401062, 0.002445181366056204, 0.053696729242801666, -0.02378443256020546, -0.009187174960970879, 0.014752513729035854, -0.0019390084780752659, 0.034141041338443756, 0.009422731585800648, 0.023686226457357407, -0.011... |
662 | 662 | ['Ludvig Ericson', 'Rendani Mbuvha'] | 1701.05130v1 | Artificial Neural Networks (ANNs) have received increasing attention in
recent years with applications that span a wide range of disciplines including
vital domains such as medicine, network security and autonomous transportation.
However, neural network architectures are becoming increasingly complex and
with an incre... | On the Performance of Network Parallel Training in Artificial Neural
Networks | 2,017 | http://arxiv.org/pdf/1701.05130v1 | Title Performance Network Parallel Training Artificial Neural Networks Summary Artificial Neural Networks ANNs received increasing attention recent year application span wide range discipline including vital domain medicine network security autonomous transportation However neural network architecture becoming increasi... | [0.00465263519436121, -0.006094884127378464, -0.03394026309251785, 0.025037458166480064, 0.006173739209771156, 0.01080020610243082, 0.08450014889240265, -0.028840018436312675, -0.03439641743898392, -0.01235903799533844, -0.002023539040237665, 0.02701936475932598, 0.008542477153241634, -0.019386006519198418, 0.023082798... |
663 | 663 | ['Misha Denil', 'Sergio Gómez Colmenarejo', 'Serkan Cabi', 'David Saxton', 'Nando de Freitas'] | 1706.06383v1 | We build deep RL agents that execute declarative programs expressed in formal
language. The agents learn to ground the terms in this language in their
environment, and can generalize their behavior at test time to execute new
programs that refer to objects that were not referenced during training. The
agents develop di... | Programmable Agents | 2,017 | http://arxiv.org/pdf/1706.06383v1 | Title Programmable Agents Summary build deep RL agent execute declarative program expressed formal language agent learn ground term language environment generalize behavior test time execute new program refer object referenced training agent develop disentangled interpretable representation allow generalize wide variet... | [0.015608362853527069, 0.04042176902294159, -0.008841241709887981, 0.021490028128027916, -0.04310496523976326, 0.0172731876373291, 0.004734480753540993, -0.02396218851208687, -0.006850042380392551, -0.029864156618714333, 0.027934923768043518, 0.06956136226654053, -0.03755027800798416, 0.12334099411964417, -0.0050628301... |
664 | 664 | ['Wojciech Samek', 'Thomas Wiegand', 'Klaus-Robert Müller'] | 1708.08296v1 | With the availability of large databases and recent improvements in deep
learning methodology, the performance of AI systems is reaching or even
exceeding the human level on an increasing number of complex tasks. Impressive
examples of this development can be found in domains such as image
classification, sentiment ana... | Explainable Artificial Intelligence: Understanding, Visualizing and
Interpreting Deep Learning Models | 2,017 | http://arxiv.org/pdf/1708.08296v1 | Title Explainable Artificial Intelligence Understanding Visualizing Interpreting Deep Learning Models Summary availability large database recent improvement deep learning methodology performance AI system reaching even exceeding human level increasing number complex task Impressive example development found domain imag... | [0.004354760050773621, 0.025785479694604874, -0.040497828274965286, 0.008426116779446602, 0.0018225862877443433, 0.011091730557382107, 0.017602263018488884, 0.015021657571196556, -0.03170483559370041, 0.016350071877241135, 0.003045437391847372, 0.02138860709965229, -0.0071899862959980965, 0.0925000011920929, 0.01675647... |
665 | 665 | ['Maithra Raghu', 'Alex Irpan', 'Jacob Andreas', 'Robert Kleinberg', 'Quoc V. Le', 'Jon Kleinberg'] | 1711.02301v3 | Deep reinforcement learning has achieved many recent successes, but our
understanding of its strengths and limitations is hampered by the lack of rich
environments in which we can fully characterize optimal behavior, and
correspondingly diagnose individual actions against such a characterization.
Here we consider a fam... | Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? | 2,017 | http://arxiv.org/pdf/1711.02301v3 | Title Deep Reinforcement Learning Solve ErdosSelfridgeSpencer Games Summary Deep reinforcement learning achieved many recent success understanding strength limitation hampered lack rich environment fully characterize optimal behavior correspondingly diagnose individual action characterization consider family combinator... | [0.00046241149539127946, 0.012712793424725533, -0.039370663464069366, 0.024848148226737976, -0.035569336265325546, -0.009846172295510769, 0.011720039881765842, 0.0038367644883692265, -0.031720489263534546, 0.046587537974119186, 0.00973449181765318, 0.033039599657058716, -0.055030446499586105, 0.05365367978811264, 0.004... |
666 | 666 | ['Christopher J. Cueva', 'Xue-Xin Wei'] | 1803.07770v1 | Decades of research on the neural code underlying spatial navigation have
revealed a diverse set of neural response properties. The Entorhinal Cortex
(EC) of the mammalian brain contains a rich set of spatial correlates,
including grid cells which encode space using tessellating patterns. However,
the mechanisms and fu... | Emergence of grid-like representations by training recurrent neural
networks to perform spatial localization | 2,018 | http://arxiv.org/pdf/1803.07770v1 | Title Emergence gridlike representation training recurrent neural network perform spatial localization Summary Decades research neural code underlying spatial navigation revealed diverse set neural response property Entorhinal Cortex EC mammalian brain contains rich set spatial correlate including grid cell encode spac... | [0.0009491996024735272, 0.018944118171930313, -0.03762098774313927, -0.005847285035997629, 0.01585310883820057, 0.039605412632226944, 0.015460417605936527, -0.018404824659228325, -0.020391466096043587, 0.03385598212480545, -0.029813308268785477, -0.029472600668668747, 0.022981125861406326, 0.05065745860338211, 0.083313... |
667 | 667 | ['Dasika Ratna Deepthi', 'Sujeet Kuchibhotla', 'K. Eswaran'] | 0712.0932v1 | In this paper, we present a Mirroring Neural Network architecture to perform
non-linear dimensionality reduction and Object Recognition using a reduced
lowdimensional characteristic vector. In addition to dimensionality reduction,
the network also reconstructs (mirrors) the original high-dimensional input
vector from t... | Dimensionality Reduction and Reconstruction using Mirroring Neural
Networks and Object Recognition based on Reduced Dimension Characteristic
Vector | 2,007 | http://arxiv.org/pdf/0712.0932v1 | Title Dimensionality Reduction Reconstruction using Mirroring Neural Networks Object Recognition based Reduced Dimension Characteristic Vector Summary paper present Mirroring Neural Network architecture perform nonlinear dimensionality reduction Object Recognition using reduced lowdimensional characteristic vector addi... | [-0.024565834552049637, 0.008700142614543438, -0.027781834825873375, 0.06486279517412186, -0.012282852083444595, 0.009073599241673946, 0.050775375217199326, 0.015378891490399837, -0.05731024965643883, 0.012595757842063904, -0.0018333224579691887, 0.008624824695289135, 0.029290223494172096, 0.02354268915951252, 0.034534... |
668 | 668 | ['Yongnan Ji', 'Pierre-Yves Herve', 'Uwe Aickelin', 'Alain Pitiot'] | 1004.3708v1 | Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI)
data based on a standard General Linear Model (GLM)and spectral clustering was
recently proposed as a means to alleviate the issues associated with spatial
normalization in fMRI. However, for all its appeal, a GLM-based parcellation
approach int... | Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach | 2,010 | http://arxiv.org/pdf/1004.3708v1 | Title Parcellation fMRI Datasets ICA PLSA Data Driven Approach Summary Intersubject parcellation functional Magnetic Resonance Imaging fMRI data based standard General Linear Model GLMand spectral clustering recently proposed mean alleviate issue associated spatial normalization fMRI However appeal GLMbased parcellatio... | [-0.00980097334831953, 0.022604599595069885, -0.043833956122398376, -0.005876052659004927, 0.012391098774969578, 0.04289724677801132, 0.06515447795391083, 0.04038946330547333, 0.009628823027014732, 0.06108696386218071, -0.06843267381191254, -0.038438066840171814, 0.05879276618361473, 0.02290216088294983, 0.052266221493... |
669 | 669 | ['N. Popescu-Bodorin', 'V. E. Balas', 'I. M. Motoc'] | 1110.6483v2 | The main topic discussed in this paper is how to use intelligence for
biometric decision defuzzification. A neural training model is proposed and
tested here as a possible solution for dealing with natural fuzzification that
appears between the intra- and inter-class distribution of scores computed
during iris recognit... | Iris Codes Classification Using Discriminant and Witness Directions | 2,011 | http://arxiv.org/pdf/1110.6483v2 | Title Iris Codes Classification Using Discriminant Witness Directions Summary main topic discussed paper use intelligence biometric decision defuzzification neural training model proposed tested possible solution dealing natural fuzzification appears intra interclass distribution score computed iris recognition test sh... | [-0.010166543535888195, 0.02445843070745468, -0.03575289249420166, 0.02355828322470188, 0.008509612642228603, 0.006906767375767231, 0.0847771093249321, 0.030097832903265953, 0.028248466551303864, 0.035606518387794495, 0.008046114817261696, -0.011559037491679192, 0.0764433741569519, 0.045154646039009094, -0.002299973974... |
670 | 670 | ['Harris Georgiou'] | 0910.3348v1 | Medical Informatics and the application of modern signal processing in the
assistance of the diagnostic process in medical imaging is one of the more
recent and active research areas today. This thesis addresses a variety of
issues related to the general problem of medical image analysis, specifically
in mammography, a... | Algorithms for Image Analysis and Combination of Pattern Classifiers
with Application to Medical Diagnosis | 2,009 | http://arxiv.org/pdf/0910.3348v1 | Title Algorithms Image Analysis Combination Pattern Classifiers Application Medical Diagnosis Summary Medical Informatics application modern signal processing assistance diagnostic process medical imaging one recent active research area today thesis address variety issue related general problem medical image analysis s... | [0.021188486367464066, 0.012472002767026424, -0.03954780101776123, 0.0048455409705638885, -0.020418690517544746, 0.026509681716561317, 0.04262324795126915, 0.07307633012533188, -0.034143172204494476, 0.020297590643167496, 0.10182428359985352, -0.00038924592081457376, 0.05464025214314461, 0.07057800889015198, -0.0151524... |
671 | 671 | ['Anh Nguyen', 'Jason Yosinski', 'Jeff Clune'] | 1412.1897v4 | Deep neural networks (DNNs) have recently been achieving state-of-the-art
performance on a variety of pattern-recognition tasks, most notably visual
classification problems. Given that DNNs are now able to classify objects in
images with near-human-level performance, questions naturally arise as to what
differences rem... | Deep Neural Networks are Easily Fooled: High Confidence Predictions for
Unrecognizable Images | 2,014 | http://arxiv.org/pdf/1412.1897v4 | Title Deep Neural Networks Easily Fooled High Confidence Predictions Unrecognizable Images Summary Deep neural network DNNs recently achieving stateoftheart performance variety patternrecognition task notably visual classification problem Given DNNs able classify object image nearhumanlevel performance question natural... | [0.010116065852344036, 0.09094808995723724, -0.03562874346971512, 0.05571739003062248, -0.002176515059545636, 0.0061898017302155495, 0.055231500416994095, -0.015633421018719673, -0.032679539173841476, -0.002561143832281232, -0.01845097541809082, 0.036392152309417725, 0.026111774146556854, 0.05467673018574715, 0.0502916... |
672 | 672 | ['Xinyu Wu', 'Vishal Saxena', 'Kehan Zhu'] | 1506.01072v2 | A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus elimi... | Homogeneous Spiking Neuromorphic System for Real-World Pattern
Recognition | 2,015 | http://arxiv.org/pdf/1506.01072v2 | Title Homogeneous Spiking Neuromorphic System RealWorld Pattern Recognition Summary neuromorphic chip combine CMOS analog spiking neuron memristive synapsis offer promising solution braininspired computing provide massive neural network parallelism density Previous hybrid analog CMOSmemristor approach required extensiv... | [-0.053706616163253784, -0.03694482147693634, -0.052267879247665405, 0.05803336948156357, 0.012484094128012657, 0.00016219884855672717, 0.010011780075728893, -0.00529831787571311, 0.0475807823240757, -0.001519152894616127, -0.042839165776968, 0.001372783095575869, -0.004398516844958067, 0.05477315932512283, 0.031505819... |
673 | 673 | ['Ven Jyn Kok', 'Mei Kuan Lim', 'Chee Seng Chan'] | 1511.06586v1 | Although the traits emerged in a mass gathering are often non-deliberative,
the act of mass impulse may lead to irre- vocable crowd disasters. The two-fold
increase of carnage in crowd since the past two decades has spurred significant
advances in the field of computer vision, towards effective and proactive crowd
surv... | Crowd Behavior Analysis: A Review where Physics meets Biology | 2,015 | http://arxiv.org/pdf/1511.06586v1 | Title Crowd Behavior Analysis Review Physics meet Biology Summary Although trait emerged mass gathering often nondeliberative act mass impulse may lead irre vocable crowd disaster twofold increase carnage crowd since past two decade spurred significant advance field computer vision towards effective proactive crowd sur... | [-0.002358394907787442, 0.015343783423304558, -0.02958426997065544, -0.014622044749557972, -0.010780510492622852, -0.021123582497239113, -0.0009222599328495562, 0.007528827525675297, -0.02057489939033985, 0.004340232815593481, 0.039204876869916916, 0.007756952196359634, -0.018435323610901833, 0.006776188500225544, 0.06... |
674 | 674 | ['Vlado Menkovski', 'Zharko Aleksovski', 'Axel Saalbach', 'Hannes Nickisch'] | 1512.05986v1 | Convolutional neural networks demonstrated outstanding empirical results in
computer vision and speech recognition tasks where labeled training data is
abundant. In medical imaging, there is a huge variety of possible imaging
modalities and contrasts, where annotated data is usually very scarce. We
present two approach... | Can Pretrained Neural Networks Detect Anatomy? | 2,015 | http://arxiv.org/pdf/1512.05986v1 | Title Pretrained Neural Networks Detect Anatomy Summary Convolutional neural network demonstrated outstanding empirical result computer vision speech recognition task labeled training data abundant medical imaging huge variety possible imaging modality contrast annotated data usually scarce present two approach deal ch... | [0.014294211752712727, 0.06194915249943733, -0.008607087656855583, 0.014003215357661247, 0.02963629737496376, 0.034846555441617966, 0.02385333739221096, 0.03210815414786339, 0.0029922490939497948, 0.022602366283535957, -0.048182617872953415, -0.0062265945598483086, 0.025089100003242493, 0.026192206889390945, 0.01063295... |
675 | 675 | ['L. M. Rasdi Rere', 'Mohamad Ivan Fanany', 'Aniati Murni Arymurthy'] | 1610.01925v1 | A typical modern optimization technique is usually either heuristic or
metaheuristic. This technique has managed to solve some optimization problems
in the research area of science, engineering, and industry. However,
implementation strategy of metaheuristic for accuracy improvement on
convolution neural networks (CNN)... | Metaheuristic Algorithms for Convolution Neural Network | 2,016 | http://arxiv.org/pdf/1610.01925v1 | Title Metaheuristic Algorithms Convolution Neural Network Summary typical modern optimization technique usually either heuristic metaheuristic technique managed solve optimization problem research area science engineering industry However implementation strategy metaheuristic accuracy improvement convolution neural net... | [0.02029944397509098, 0.006519587244838476, -0.013802915811538696, 0.011240843683481216, -0.024036340415477753, -0.01870868168771267, 0.038991380482912064, -0.0033244576770812273, -0.07844418287277222, -0.0439758226275444, 0.003446210641413927, 0.030755717307329178, 0.011416098102927208, 0.026213426142930984, 0.0012278... |
676 | 676 | ['Jin-Hwa Kim', 'Kyoung-Woon On', 'Woosang Lim', 'Jeonghee Kim', 'Jung-Woo Ha', 'Byoung-Tak Zhang'] | 1610.04325v4 | Bilinear models provide rich representations compared with linear models.
They have been applied in various visual tasks, such as object recognition,
segmentation, and visual question-answering, to get state-of-the-art
performances taking advantage of the expanded representations. However,
bilinear representations tend... | Hadamard Product for Low-rank Bilinear Pooling | 2,016 | http://arxiv.org/pdf/1610.04325v4 | Title Hadamard Product Lowrank Bilinear Pooling Summary Bilinear model provide rich representation compared linear model applied various visual task object recognition segmentation visual questionanswering get stateoftheart performance taking advantage expanded representation However bilinear representation tend highdi... | [0.0004682496073655784, 0.014315168373286724, -0.02317340485751629, 0.02531825192272663, -0.029307929798960686, 0.017951473593711853, 0.04475909471511841, 0.023417161777615547, 0.02642686665058136, -0.04970598593354225, 0.0156943928450346, -0.0571695975959301, -0.01680913008749485, 0.017787916585803032, 0.0564383789896... |
677 | 677 | ['Aojun Zhou', 'Anbang Yao', 'Yiwen Guo', 'Lin Xu', 'Yurong Chen'] | 1702.03044v2 | This paper presents incremental network quantization (INQ), a novel method,
targeting to efficiently convert any pre-trained full-precision convolutional
neural network (CNN) model into a low-precision version whose weights are
constrained to be either powers of two or zero. Unlike existing methods which
are struggled ... | Incremental Network Quantization: Towards Lossless CNNs with
Low-Precision Weights | 2,017 | http://arxiv.org/pdf/1702.03044v2 | Title Incremental Network Quantization Towards Lossless CNNs LowPrecision Weights Summary paper present incremental network quantization INQ novel method targeting efficiently convert pretrained fullprecision convolutional neural network CNN model lowprecision version whose weight constrained either power two zero Unli... | [-0.02954309619963169, 0.0442105196416378, 0.0049345605075359344, 0.053062520921230316, 0.03744306415319443, -0.01931591145694256, 0.034151557832956314, 0.03957200050354004, -0.053639624267816544, 0.000521279638633132, 0.009054725989699364, 0.016831547021865845, -0.01895867846906185, 0.02614227496087551, 0.031029893085... |
678 | 678 | ['Dhanesh Ramachandram', 'Terrance DeVries'] | 1703.03372v3 | We present a method for skin lesion segmentation for the ISIC 2017 Skin
Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional
Network architecture which is trained end to end, from scratch, on a limited
dataset. Our semantic segmentation architecture utilizes several recent
innovations in partic... | LesionSeg: Semantic segmentation of skin lesions using Deep
Convolutional Neural Network | 2,017 | http://arxiv.org/pdf/1703.03372v3 | Title LesionSeg Semantic segmentation skin lesion using Deep Convolutional Neural Network Summary present method skin lesion segmentation ISIC 2017 Skin Lesion Segmentation Challenge approach based Fully Convolutional Network architecture trained end end scratch limited dataset semantic segmentation architecture utiliz... | [0.03243638575077057, -0.0013901983620598912, 0.012587620876729488, 0.012158333323895931, 0.03590473160147667, 0.008580729365348816, 0.02405596151947975, 0.016792617738246918, -0.015188582241535187, 0.024354035034775734, 0.021119659766554832, 0.024920549243688583, -0.012242786586284637, 0.035538893193006516, -0.0056605... |
679 | 679 | ['Priyadarshini Panda', 'Gopalakrishnan Srinivasan', 'Kaushik Roy'] | 1703.03854v2 | Brain-inspired learning models attempt to mimic the cortical architecture and
computations performed in the neurons and synapses constituting the human brain
to achieve its efficiency in cognitive tasks. In this work, we present
convolutional spike timing dependent plasticity based feature learning with
biologically pl... | Convolutional Spike Timing Dependent Plasticity based Feature Learning
in Spiking Neural Networks | 2,017 | http://arxiv.org/pdf/1703.03854v2 | Title Convolutional Spike Timing Dependent Plasticity based Feature Learning Spiking Neural Networks Summary Braininspired learning model attempt mimic cortical architecture computation performed neuron synapsis constituting human brain achieve efficiency cognitive task work present convolutional spike timing dependent... | [-0.0192340686917305, 0.00753026083111763, -0.027655042707920074, 0.07184738665819168, 0.02239409275352955, -0.022748205810785294, 0.03357100859284401, -0.01638604700565338, 0.025535887107253075, 0.027913639321923256, -0.05634466931223869, 0.04285196214914322, 0.0009294417104683816, 0.09864293038845062, 0.0287422630935... |
680 | 680 | ['Shumeet Baluja', 'Ian Fischer'] | 1703.09387v1 | Multiple different approaches of generating adversarial examples have been
proposed to attack deep neural networks. These approaches involve either
directly computing gradients with respect to the image pixels, or directly
solving an optimization on the image pixels. In this work, we present a
fundamentally new method ... | Adversarial Transformation Networks: Learning to Generate Adversarial
Examples | 2,017 | http://arxiv.org/pdf/1703.09387v1 | Title Adversarial Transformation Networks Learning Generate Adversarial Examples Summary Multiple different approach generating adversarial example proposed attack deep neural network approach involve either directly computing gradient respect image pixel directly solving optimization image pixel work present fundament... | [0.02422841638326645, 0.03188668191432953, -0.02534564398229122, 0.036693260073661804, -0.010438768193125725, -0.04462973400950432, 0.057960670441389084, -0.024842381477355957, -0.05617695674300194, -0.027730831876397133, -0.02204558625817299, 0.06027965992689133, 0.011222568340599537, 0.022123096510767937, 0.059825230... |
681 | 681 | ['Devinder Kumar', 'Graham W Taylor', 'Alexander Wong'] | 1709.01574v1 | Deep learning has been shown to outperform traditional machine learning
algorithms across a wide range of problem domains. However, current deep
learning algorithms have been criticized as uninterpretable "black-boxes" which
cannot explain their decision making processes. This is a major shortcoming
that prevents the w... | Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced
Attentive Response Approach for Explaining and Visualizing Deep
Learning-Driven Stock Market Prediction | 2,017 | http://arxiv.org/pdf/1709.01574v1 | Title Opening Black Box Financial AI CLEARTrade CLassEnhanced Attentive Response Approach Explaining Visualizing Deep LearningDriven Stock Market Prediction Summary Deep learning shown outperform traditional machine learning algorithm across wide range problem domain However current deep learning algorithm criticized u... | [-0.040640443563461304, 0.016146590933203697, -0.05602003633975983, -0.03990969434380531, 0.028110221028327942, -0.006837813183665276, 0.04842669889330864, 0.03750529885292053, -0.010637651197612286, 0.01545463316142559, 0.03076239489018917, 0.022501042112708092, -0.0024728206917643547, 0.1408827304840088, 0.0214335024... |
682 | 682 | ['Mohammad Javad Shafiee', 'Brendan Chywl', 'Francis Li', 'Alexander Wong'] | 1709.05943v1 | Object detection is considered one of the most challenging problems in this
field of computer vision, as it involves the combination of object
classification and object localization within a scene. Recently, deep neural
networks (DNNs) have been demonstrated to achieve superior object detection
performance compared to ... | Fast YOLO: A Fast You Only Look Once System for Real-time Embedded
Object Detection in Video | 2,017 | http://arxiv.org/pdf/1709.05943v1 | Title Fast YOLO Fast Look System Realtime Embedded Object Detection Video Summary Object detection considered one challenging problem field computer vision involves combination object classification object localization within scene Recently deep neural network DNNs demonstrated achieve superior object detection perform... | [0.014544973149895668, 0.03064371645450592, -0.000905121210962534, 0.0811096802353859, 0.007861118763685226, 0.009515101090073586, 0.03478757292032242, 0.019740769639611244, -0.017672598361968994, -0.0129587696865201, 0.027494484558701515, 0.031310345977544785, 0.007866637781262398, 0.054129812866449356, -0.02245800010... |
683 | 683 | ['Xiaoliang Dai', 'Hongxu Yin', 'Niraj K. Jha'] | 1711.02017v2 | Neural networks (NNs) have begun to have a pervasive impact on various
applications of machine learning. However, the problem of finding an optimal NN
architecture for large applications has remained open for several decades.
Conventional approaches search for the optimal NN architecture through
extensive trial-and-err... | NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm | 2,017 | http://arxiv.org/pdf/1711.02017v2 | Title NeST Neural Network Synthesis Tool Based GrowandPrune Paradigm Summary Neural network NNs begun pervasive impact various application machine learning However problem finding optimal NN architecture large application remained open several decade Conventional approach search optimal NN architecture extensive triala... | [-0.04225454106926918, 0.045149169862270355, -0.055379170924425125, 0.027144653722643852, -0.03237073868513107, -0.04498456418514252, 0.04806896671652794, -0.011014611460268497, 0.014655706472694874, 0.025836342945694923, 0.0127420574426651, 0.005657525733113289, 0.005699627101421356, 0.08892931044101715, 0.02083749137... |
684 | 684 | ['Filipe Rolim Cordeiro', 'Wellington Pinheiro dos Santos', 'Abel Guilhermino da Silva Filho'] | 1712.07312v1 | Breast cancer is already one of the most common form of cancer worldwide.
Mammography image analysis is still the most effective diagnostic method to
promote the early detection of breast cancer. Accurately segmenting tumors in
digital mammography images is important to improve diagnosis capabilities of
health speciali... | Analysis of supervised and semi-supervised GrowCut applied to
segmentation of masses in mammography images | 2,017 | http://arxiv.org/pdf/1712.07312v1 | Title Analysis supervised semisupervised GrowCut applied segmentation mass mammography image Summary Breast cancer already one common form cancer worldwide Mammography image analysis still effective diagnostic method promote early detection breast cancer Accurately segmenting tumor digital mammography image important i... | [0.016599994152784348, -0.0029411392752081156, -0.023990413174033165, 0.001886810059659183, -0.05486414209008217, -0.0001775944692781195, 0.03415572643280029, 0.037300389260053635, 0.02353079617023468, 0.031302161514759064, 0.07191748917102814, 0.01975228823721409, -0.01802855171263218, 0.07661595195531845, -0.02419896... |
685 | 685 | ['Shumeet Baluja'] | 1801.05156v1 | We present extensive experiments training and testing hidden units in deep
networks that emit only a predefined, static, number of discretized values.
These units provide benefits in real-world deployment in systems in which
memory and/or computation may be limited. Additionally, they are particularly
well suited for u... | Empirical Explorations in Training Networks with Discrete Activations | 2,018 | http://arxiv.org/pdf/1801.05156v1 | Title Empirical Explorations Training Networks Discrete Activations Summary present extensive experiment training testing hidden unit deep network emit predefined static number discretized value unit provide benefit realworld deployment system memory andor computation may limited Additionally particularly well suited u... | [-0.012845207937061787, 0.0042851511389017105, -0.014024388045072556, 0.0053544011898338795, 0.03046133928000927, -0.0171323474496603, 0.07774453610181808, -0.008793994784355164, -0.017757412046194077, 0.0006350188632495701, 0.010669970884919167, -0.01071455143392086, -0.007712224032729864, 0.038528744131326675, 0.0219... |
686 | 686 | ['Esteban Real', 'Alok Aggarwal', 'Yanping Huang', 'Quoc V Le'] | 1802.01548v3 | The effort devoted to hand-crafting image classifiers has motivated the use
of architecture search to discover them automatically. Reinforcement learning
and evolution have both shown promise for this purpose. This study employs a
regularized version of a popular asynchronous evolutionary algorithm. We
rigorously compa... | Regularized Evolution for Image Classifier Architecture Search | 2,018 | http://arxiv.org/pdf/1802.01548v3 | Title Regularized Evolution Image Classifier Architecture Search Summary effort devoted handcrafting image classifier motivated use architecture search discover automatically Reinforcement learning evolution shown promise purpose study employ regularized version popular asynchronous evolutionary algorithm rigorously co... | [0.022280516102910042, 0.049255795776844025, -0.04031810164451599, 0.014892525039613247, -0.008276914246380329, -0.026720911264419556, 0.013798962347209454, 0.031990114599466324, -0.031773824244737625, 0.028270630165934563, -0.0018290071748197079, 0.04984038323163986, -0.015239505097270012, 0.04911781847476959, -0.0088... |
687 | 687 | ['Alexander Wong', 'Mohammad Javad Shafiee', 'Francis Li', 'Brendan Chwyl'] | 1802.06488v1 | Object detection is a major challenge in computer vision, involving both
object classification and object localization within a scene. While deep neural
networks have been shown in recent years to yield very powerful techniques for
tackling the challenge of object detection, one of the biggest challenges with
enabling ... | Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network
for Real-time Embedded Object Detection | 2,018 | http://arxiv.org/pdf/1802.06488v1 | Title Tiny SSD Tiny Singleshot Detection Deep Convolutional Neural Network Realtime Embedded Object Detection Summary Object detection major challenge computer vision involving object classification object localization within scene deep neural network shown recent year yield powerful technique tackling challenge object... | [-0.00894768163561821, -0.015540339052677155, -0.010371421463787556, 0.09251029044389725, -0.0014948333846405149, -0.029780378565192223, 0.06379145383834839, -0.010930354706943035, -0.016195353120565414, -0.026631584390997887, 0.03221234679222107, 0.03448629751801491, -0.0014456340577453375, 0.029058344662189484, 0.014... |
688 | 688 | ['Tobias Hinz', 'Stefan Wermter'] | 1803.02627v1 | Combining Generative Adversarial Networks (GANs) with encoders that learn to
encode data points has shown promising results in learning data representations
in an unsupervised way. We propose a framework that combines an encoder and a
generator to learn disentangled representations which encode meaningful
information a... | Inferencing Based on Unsupervised Learning of Disentangled
Representations | 2,018 | http://arxiv.org/pdf/1803.02627v1 | Title Inferencing Based Unsupervised Learning Disentangled Representations Summary Combining Generative Adversarial Networks GANs encoders learn encode data point shown promising result learning data representation unsupervised way propose framework combine encoder generator learn disentangled representation encode mea... | [-0.03528452292084694, 0.12660232186317444, -0.018537303432822227, 0.07999861240386963, 0.01150921918451786, 0.03174477815628052, -0.004988578148186207, 0.017566945403814316, -0.007421194110065699, 0.03771132230758667, -0.01803317666053772, 0.05032367631793022, -0.010365442372858524, 0.03543653339147568, 0.061726044863... |
689 | 689 | ['Erik Berglund', 'Joaquin Sitte'] | 0705.0199v2 | The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network
algorithm based on the Self-Organizing Map (SOM). It eliminates the need for a
learning rate and annealing schemes for learning rate and neighbourhood size.
We discuss the relative performance of the PLSOM and the SOM and demonstrate
some tasks in w... | The Parameter-Less Self-Organizing Map algorithm | 2,007 | http://arxiv.org/pdf/0705.0199v2 | Title ParameterLess SelfOrganizing Map algorithm Summary ParameterLess SelfOrganizing Map PLSOM new neural network algorithm based SelfOrganizing Map SOM eliminates need learning rate annealing scheme learning rate neighbourhood size discus relative performance PLSOM SOM demonstrate task SOM fails PLSOM performs satisf... | [-0.03741709142923355, -0.0019337277626618743, 0.001419220701791346, -0.015870966017246246, 0.009401226416230202, -0.022940117865800858, 0.03638525679707527, -0.050921302288770676, 0.029635833576321602, 0.007876950316131115, -0.0322907380759716, 0.07134104520082474, 0.0012333877384662628, 0.015537651255726814, 0.017399... |
690 | 690 | ['Christian Napoli', 'Giuseppe Pappalardo', 'Emiliano Tramontana', 'Zbigniew Marszałek', 'Dawid Połap', 'Marcin Woźniak'] | 1412.6464v1 | In order to identify an object, human eyes firstly search the field of view
for points or areas which have particular properties. These properties are used
to recognise an image or an object. Then this process could be taken as a model
to develop computer algorithms for images identification. This paper proposes
the id... | Simplified firefly algorithm for 2D image key-points search | 2,014 | http://arxiv.org/pdf/1412.6464v1 | Title Simplified firefly algorithm 2D image keypoints search Summary order identify object human eye firstly search field view point area particular property property used recognise image object process could taken model develop computer algorithm image identification paper proposes idea applying simplified firefly alg... | [-0.00666049076244235, -0.007869566790759563, -0.003758190432563424, 0.035692159086465836, -0.020446600392460823, -0.0026361218187958, 3.1921383197186515e-05, 0.037386175245046616, 0.006876166444271803, -0.03355340287089348, 0.014403634704649448, 0.05032142996788025, 0.02732980065047741, -0.0015734510961920023, 0.00337... |
691 | 691 | ['Sue Han Lee', 'Chee Seng Chan', 'Paul Wilkin', 'Paolo Remagnino'] | 1506.08425v1 | This paper studies convolutional neural networks (CNN) to learn unsupervised
feature representations for 44 different plant species, collected at the Royal
Botanic Gardens, Kew, England. To gain intuition on the chosen features from
the CNN model (opposed to a 'black box' solution), a visualisation technique
based on t... | Deep-Plant: Plant Identification with convolutional neural networks | 2,015 | http://arxiv.org/pdf/1506.08425v1 | Title DeepPlant Plant Identification convolutional neural network Summary paper study convolutional neural network CNN learn unsupervised feature representation 44 different plant specie collected Royal Botanic Gardens Kew England gain intuition chosen feature CNN model opposed black box solution visualisation techniqu... | [0.006776423193514347, 0.017540596425533295, -0.013493082486093044, 0.09937453269958496, -0.015553660690784454, -0.001622108044102788, 0.02896924503147602, 0.015895109623670578, -0.029217569157481194, 0.021072691306471825, 0.00834373477846384, 0.033706702291965485, -0.00636943057179451, 0.03164862096309662, -0.00415315... |
692 | 692 | ['Joshua C. Peterson', 'Joshua T. Abbott', 'Thomas L. Griffiths'] | 1608.02164v1 | Deep neural networks have become increasingly successful at solving classic
perception problems such as object recognition, semantic segmentation, and
scene understanding, often reaching or surpassing human-level accuracy. This
success is due in part to the ability of DNNs to learn useful representations
of high-dimens... | Adapting Deep Network Features to Capture Psychological Representations | 2,016 | http://arxiv.org/pdf/1608.02164v1 | Title Adapting Deep Network Features Capture Psychological Representations Summary Deep neural network become increasingly successful solving classic perception problem object recognition semantic segmentation scene understanding often reaching surpassing humanlevel accuracy success due part ability DNNs learn useful r... | [0.013091064058244228, 0.07363045960664749, -0.03308088704943657, 0.0372568815946579, -0.01791868545114994, 0.02727382630109787, 0.03783806413412094, 0.00030163361225277185, -0.021288791671395302, -0.01098821684718132, -0.06715395301580429, -0.002083749743178487, 0.015625564381480217, 0.05016550421714783, 0.01228214800... |
693 | 693 | ['Esteban Real', 'Sherry Moore', 'Andrew Selle', 'Saurabh Saxena', 'Yutaka Leon Suematsu', 'Jie Tan', 'Quoc Le', 'Alex Kurakin'] | 1703.01041v2 | Neural networks have proven effective at solving difficult problems but
designing their architectures can be challenging, even for image classification
problems alone. Our goal is to minimize human participation, so we employ
evolutionary algorithms to discover such networks automatically. Despite
significant computati... | Large-Scale Evolution of Image Classifiers | 2,017 | http://arxiv.org/pdf/1703.01041v2 | Title LargeScale Evolution Image Classifiers Summary Neural network proven effective solving difficult problem designing architecture challenging even image classification problem alone goal minimize human participation employ evolutionary algorithm discover network automatically Despite significant computational requi... | [-0.011365228332579136, 0.07445085048675537, -0.04380756616592407, 0.036304619163274765, 0.010352357290685177, 0.0017203945899382234, 0.03758206218481064, 0.03093908540904522, -0.012030882760882378, 0.019904321059584618, 0.005386252887547016, 0.03029400296509266, -0.007501552812755108, 0.0726308524608612, 0.02211262099... |
694 | 694 | ['Chunpeng Wu', 'Wei Wen', 'Tariq Afzal', 'Yongmei Zhang', 'Yiran Chen', 'Hai Li'] | 1703.04071v4 | Recently, DNN model compression based on network architecture design, e.g.,
SqueezeNet, attracted a lot attention. No accuracy drop on image classification
is observed on these extremely compact networks, compared to well-known models.
An emerging question, however, is whether these model compression techniques
hurt DN... | A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification
and Domain Adaptation | 2,017 | http://arxiv.org/pdf/1703.04071v4 | Title Compact DNN Approaching GoogLeNetLevel Accuracy Classification Domain Adaptation Summary Recently DNN model compression based network architecture design eg SqueezeNet attracted lot attention accuracy drop image classification observed extremely compact network compared wellknown model emerging question however w... | [-0.012206400744616985, 0.05085603520274162, -0.028635334223508835, 0.0407564640045166, -0.010684553533792496, 0.003392936196178198, 0.05592728778719902, 0.02588380128145218, -0.03154008463025093, 0.02351333387196064, -0.022899260744452477, 0.01830415055155754, 0.018779441714286804, 0.07889341562986374, 0.0061516696587... |
695 | 695 | ['Mandar Haldekar', 'Ashwinkumar Ganesan', 'Tim Oates'] | 1706.04215v1 | Traditional approaches to building a large scale knowledge graph have usually
relied on extracting information (entities, their properties, and relations
between them) from unstructured text (e.g. Dbpedia). Recent advances in
Convolutional Neural Networks (CNN) allow us to shift our focus to learning
entities and relat... | Identifying Spatial Relations in Images using Convolutional Neural
Networks | 2,017 | http://arxiv.org/pdf/1706.04215v1 | Title Identifying Spatial Relations Images using Convolutional Neural Networks Summary Traditional approach building large scale knowledge graph usually relied extracting information entity property relation unstructured text eg Dbpedia Recent advance Convolutional Neural Networks CNN allow u shift focus learning entit... | [0.04092583432793617, 0.046911127865314484, -0.0037572721485048532, 0.0848197489976883, -0.026717787608504295, 0.005975028499960899, 0.010528143495321274, 0.008887460455298424, 0.015241685323417187, -0.03622554987668991, -0.016750892624258995, 0.05479459837079048, -0.003973840735852718, 0.024060949683189392, 0.03149246... |
696 | 696 | ['Adam R. Kosiorek', 'Alex Bewley', 'Ingmar Posner'] | 1706.09262v2 | Class-agnostic object tracking is particularly difficult in cluttered
environments as target specific discriminative models cannot be learned a
priori. Inspired by how the human visual cortex employs spatial attention and
separate "where" and "what" processing pathways to actively suppress irrelevant
visual features, t... | Hierarchical Attentive Recurrent Tracking | 2,017 | http://arxiv.org/pdf/1706.09262v2 | Title Hierarchical Attentive Recurrent Tracking Summary Classagnostic object tracking particularly difficult cluttered environment target specific discriminative model cannot learned priori Inspired human visual cortex employ spatial attention separate processing pathway actively suppress irrelevant visual feature work... | [0.01399760227650404, 0.014225639402866364, 0.017151782289147377, 0.07335303723812103, 0.02937290258705616, -0.009481535293161869, -0.004146979656070471, -0.015994062647223473, -0.03165788576006889, -0.03880343213677406, 0.004369891248643398, -0.02563496306538582, -0.006857514381408691, 0.0307307206094265, 0.0323091819... |
697 | 697 | ['Seungkyun Hong', 'Seongchan Kim', 'Minsu Joh', 'Sa-kwang Song'] | 1711.10644v2 | Predicting unseen weather phenomena is an important issue for disaster
management. In this paper, we suggest a model for a convolutional
sequence-to-sequence autoencoder for predicting undiscovered weather situations
from previous satellite images. We also propose a symmetric skip connection
between encoder and decoder... | PSIque: Next Sequence Prediction of Satellite Images using a
Convolutional Sequence-to-Sequence Network | 2,017 | http://arxiv.org/pdf/1711.10644v2 | Title PSIque Next Sequence Prediction Satellite Images using Convolutional SequencetoSequence Network Summary Predicting unseen weather phenomenon important issue disaster management paper suggest model convolutional sequencetosequence autoencoder predicting undiscovered weather situation previous satellite image also ... | [0.018754642456769943, 0.09524159133434296, -0.001475048717111349, 0.04831339046359062, 0.0031933083664625883, 0.010652001947164536, -0.0016888133250176907, -0.025227637961506844, -0.002432959619909525, 0.02826281450688839, 0.0632391944527626, -0.02445962280035019, 0.020399456843733788, 0.09698973596096039, 0.029526859... |
698 | 698 | ['Wellington Pinheiro dos Santos', 'Ricardo Emmanuel de Souza', 'Plínio B. dos Santos Filho'] | 1712.00712v1 | Alzheimer's disease is the most common cause of dementia, yet hard to
diagnose precisely without invasive techniques, particularly at the onset of
the disease. This work approaches image analysis and classification of
synthetic multispectral images composed by diffusion-weighted magnetic
resonance (MR) cerebral images ... | Evaluation of Alzheimer's Disease by Analysis of MR Images using
Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the
ADC Maps | 2,017 | http://arxiv.org/pdf/1712.00712v1 | Title Evaluation Alzheimers Disease Analysis MR Images using Multilayer Perceptrons Kohonen SOM Classifiers Alternative ADC Maps Summary Alzheimers disease common cause dementia yet hard diagnose precisely without invasive technique particularly onset disease work approach image analysis classification synthetic multis... | [-0.04594212397933006, 0.01745085045695305, -0.030492210760712624, 0.013584626838564873, 0.01706668548285961, 0.012701579369604588, 0.02677692100405693, 0.022421682253479958, 0.09287978708744049, 0.026735689491033554, 0.018283044919371605, 0.016190290451049805, 0.02528703771531582, 0.01034840289503336, 0.03909936919808... |
699 | 699 | ['Cheston Tan', 'Tomaso Poggio'] | 1406.3793v1 | Faces are a class of visual stimuli with unique significance, for a variety
of reasons. They are ubiquitous throughout the course of a person's life, and
face recognition is crucial for daily social interaction. Faces are also unlike
any other stimulus class in terms of certain physical stimulus characteristics.
Furthe... | Neural tuning size is a key factor underlying holistic face processing | 2,014 | http://arxiv.org/pdf/1406.3793v1 | Title Neural tuning size key factor underlying holistic face processing Summary Faces class visual stimulus unique significance variety reason ubiquitous throughout course person life face recognition crucial daily social interaction Faces also unlike stimulus class term certain physical stimulus characteristic Further... | [0.016153737902641296, -0.029728543013334274, -0.04741014167666435, 0.016164788976311684, 0.02687034383416176, 0.053367383778095245, 0.05962429940700531, 0.01388290524482727, -0.018673652783036232, 0.012384072877466679, -0.03280370309948921, -0.0035295968409627676, 0.045720186084508896, 0.08104520291090012, 0.082537136... |
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