Unnamed: 0.1 int64 0 41k | Unnamed: 0 int64 0 41k | author stringlengths 9 1.39k | id stringlengths 11 18 | summary stringlengths 25 3.66k | title stringlengths 4 258 | year int64 1.99k 2.02k | arxiv_url stringlengths 32 39 | info stringlengths 523 3.18k | embeddings stringlengths 16.9k 17.1k |
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400 | 400 | ['Volker Fischer', 'Mummadi Chaithanya Kumar', 'Jan Hendrik Metzen', 'Thomas Brox'] | 1703.01101v1 | Machine learning methods in general and Deep Neural Networks in particular
have shown to be vulnerable to adversarial perturbations. So far this
phenomenon has mainly been studied in the context of whole-image
classification. In this contribution, we analyse how adversarial perturbations
can affect the task of semantic... | Adversarial Examples for Semantic Image Segmentation | 2,017 | http://arxiv.org/pdf/1703.01101v1 | Title Adversarial Examples Semantic Image Segmentation Summary Machine learning method general Deep Neural Networks particular shown vulnerable adversarial perturbation far phenomenon mainly studied context wholeimage classification contribution analyse adversarial perturbation affect task semantic segmentation show ex... | [0.01410507783293724, 0.02849755436182022, -0.03793328255414963, 0.0631236806511879, -0.018297743052244186, -0.01985902152955532, 0.026824623346328735, -0.006789030507206917, -0.04114808514714241, -0.03241625055670738, 0.005177593789994717, 0.056785956025123596, 0.01314525492489338, 0.0018849825719371438, 0.06411287933... |
401 | 401 | ['Wieland Brendel', 'Jonas Rauber', 'Matthias Bethge'] | 1712.04248v2 | Many machine learning algorithms are vulnerable to almost imperceptible
perturbations of their inputs. So far it was unclear how much risk adversarial
perturbations carry for the safety of real-world machine learning applications
because most methods used to generate such perturbations rely either on
detailed model inf... | Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box
Machine Learning Models | 2,017 | http://arxiv.org/pdf/1712.04248v2 | Title DecisionBased Adversarial Attacks Reliable Attacks BlackBox Machine Learning Models Summary Many machine learning algorithm vulnerable almost imperceptible perturbation input far unclear much risk adversarial perturbation carry safety realworld machine learning application method used generate perturbation rely e... | [-0.010133789852261543, 0.041026026010513306, -0.024267854169011116, 0.025550389662384987, -0.023821262642741203, -0.0290058683604002, 0.07358074933290482, -0.026392171159386635, -0.04097146540880203, -0.04666865989565849, 0.029319528490304947, 0.0581333264708519, -0.00835547223687172, 0.06942620128393173, 0.0455332398... |
402 | 402 | ['Abien Fred Agarap', 'Francis John Hill Pepito'] | 1801.00318v1 | Effective and efficient mitigation of malware is a long-time endeavor in the
information security community. The development of an anti-malware system that
can counteract an unknown malware is a prolific activity that may benefit
several sectors. We envision an intelligent anti-malware system that utilizes
the power of... | Towards Building an Intelligent Anti-Malware System: A Deep Learning
Approach using Support Vector Machine (SVM) for Malware Classification | 2,017 | http://arxiv.org/pdf/1801.00318v1 | Title Towards Building Intelligent AntiMalware System Deep Learning Approach using Support Vector Machine SVM Malware Classification Summary Effective efficient mitigation malware longtime endeavor information security community development antimalware system counteract unknown malware prolific activity may benefit sev... | [0.042566921561956406, -0.009155770763754845, -0.037983544170856476, 0.01940065436065197, -0.02579900622367859, -0.03528895601630211, 0.03765113279223442, -0.016161033883690834, 0.017611956223845482, -0.012544902972877026, 0.05626830458641052, 0.010617326945066452, -0.003522921120747924, 0.10034801810979843, 0.05641409... |
403 | 403 | ['Despoina Christou'] | 1604.01272v1 | Feature extraction has gained increasing attention in the field of machine
learning, as in order to detect patterns, extract information, or predict
future observations from big data, the urge of informative features is crucial.
The process of extracting features is highly linked to dimensionality reduction
as it impli... | Feature extraction using Latent Dirichlet Allocation and Neural
Networks: A case study on movie synopses | 2,016 | http://arxiv.org/pdf/1604.01272v1 | Title Feature extraction using Latent Dirichlet Allocation Neural Networks case study movie synopsis Summary Feature extraction gained increasing attention field machine learning order detect pattern extract information predict future observation big data urge informative feature crucial process extracting feature high... | [0.05597390606999397, 0.044753823429346085, 0.003626729128882289, 0.05251197889447212, -0.031103627756237984, 0.002740079304203391, 0.05280204117298126, 0.013157839886844158, -0.015904661267995834, -0.03891849145293236, -0.01653800532221794, 0.008867749944329262, 0.008766223676502705, 0.04222093150019646, -0.0035424630... |
404 | 404 | ['Iulian Vlad Serban', 'Ryan Lowe', 'Peter Henderson', 'Laurent Charlin', 'Joelle Pineau'] | 1512.05742v3 | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheles... | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | 2,015 | http://arxiv.org/pdf/1512.05742v3 | Title Survey Available Corpora Building DataDriven Dialogue Systems Summary past decade several area speech language understanding witnessed substantial breakthrough use datadriven model area dialogue system trend le obvious practical system still built significant engineering expert knowledge Nevertheless several rece... | [0.03697967529296875, 0.03675778582692146, -0.020406929776072502, 0.07374028116464615, 0.0004538491484709084, -0.0036602874752134085, 0.0184808149933815, -0.023244986310601234, -0.03441104665398598, -0.05692259967327118, -0.04438852146267891, -0.000322436768328771, 0.023757394403219223, 0.037595484405756, 0.01447482965... |
405 | 405 | ['Shaohua Li', 'Tat-Seng Chua', 'Jun Zhu', 'Chunyan Miao'] | 1606.02979v2 | Word embedding maps words into a low-dimensional continuous embedding space
by exploiting the local word collocation patterns in a small context window. On
the other hand, topic modeling maps documents onto a low-dimensional topic
space, by utilizing the global word collocation patterns in the same document.
These two ... | Generative Topic Embedding: a Continuous Representation of Documents
(Extended Version with Proofs) | 2,016 | http://arxiv.org/pdf/1606.02979v2 | Title Generative Topic Embedding Continuous Representation Documents Extended Version Proofs Summary Word embedding map word lowdimensional continuous embedding space exploiting local word collocation pattern small context window hand topic modeling map document onto lowdimensional topic space utilizing global word col... | [0.012248310260474682, 0.016735142096877098, -0.0054135615937411785, 0.018747160211205482, -0.011101397685706615, 0.009182619862258434, 0.0185473021119833, -0.005352766718715429, -0.03516506403684616, -0.05392949655652046, 0.012300546281039715, 0.008827975951135159, 0.011603852733969688, 0.0505647175014019, 0.026435902... |
406 | 406 | ['Maxim Rabinovich', 'Dan Klein'] | 1704.07751v1 | As entity type systems become richer and more fine-grained, we expect the
number of types assigned to a given entity to increase. However, most
fine-grained typing work has focused on datasets that exhibit a low degree of
type multiplicity. In this paper, we consider the high-multiplicity regime
inherent in data source... | Fine-Grained Entity Typing with High-Multiplicity Assignments | 2,017 | http://arxiv.org/pdf/1704.07751v1 | Title FineGrained Entity Typing HighMultiplicity Assignments Summary entity type system become richer finegrained expect number type assigned given entity increase However finegrained typing work focused datasets exhibit low degree type multiplicity paper consider highmultiplicity regime inherent data source Wikipedia ... | [0.0005161462468095124, 0.026029350236058235, -0.01804785244166851, 0.00019324070308357477, 0.006674001459032297, 0.028266053646802902, 0.03170699253678322, 0.020208759233355522, -0.02069169096648693, -0.06634200364351273, 0.010585351847112179, -0.021944336593151093, 0.018650393933057785, 0.0321228913962841, 0.05646652... |
407 | 407 | ['Mateusz Malinowski', 'Mario Fritz'] | 1410.8027v3 | As language and visual understanding by machines progresses rapidly, we are
observing an increasing interest in holistic architectures that tightly
interlink both modalities in a joint learning and inference process. This trend
has allowed the community to progress towards more challenging and open tasks
and refueled t... | Towards a Visual Turing Challenge | 2,014 | http://arxiv.org/pdf/1410.8027v3 | Title Towards Visual Turing Challenge Summary language visual understanding machine progress rapidly observing increasing interest holistic architecture tightly interlink modality joint learning inference process trend allowed community progress towards challenging open task refueled hope achieving old AI dream buildin... | [0.0014101818669587374, 0.026177240535616875, -0.008235828951001167, 0.003800810780376196, -0.04008960723876953, -0.017938468605279922, 0.017642751336097717, 0.016304917633533478, -0.0054808142594993114, 0.02546154521405697, 0.004586879629641771, 0.07738128304481506, 0.013647174462676048, 0.10973093658685684, 0.0121642... |
408 | 408 | ['Giovanni Saponaro', 'Lorenzo Jamone', 'Alexandre Bernardino', 'Giampiero Salvi'] | 1711.09055v1 | A growing field in robotics and Artificial Intelligence (AI) research is
human-robot collaboration, whose target is to enable effective teamwork between
humans and robots. However, in many situations human teams are still superior
to human-robot teams, primarily because human teams can easily agree on a
common goal wit... | Interactive Robot Learning of Gestures, Language and Affordances | 2,017 | http://arxiv.org/pdf/1711.09055v1 | Title Interactive Robot Learning Gestures Language Affordances Summary growing field robotics Artificial Intelligence AI research humanrobot collaboration whose target enable effective teamwork human robot However many situation human team still superior humanrobot team primarily human team easily agree common goal lan... | [0.027822798117995262, 0.018308086320757866, 0.004649443551898003, 0.0024462572764605284, -0.014528930187225342, 0.050836119800806046, -0.0033683222718536854, 0.003362779738381505, 0.04727567732334137, -0.043453603982925415, -0.026441389694809914, 0.01374482549726963, 0.031093403697013855, 0.03950246050953865, -0.00082... |
409 | 409 | ['Abhinav Gupta', 'Yajie Miao', 'Leonardo Neves', 'Florian Metze'] | 1712.00489v1 | Automatic transcriptions of consumer-generated multi-media content such as
"Youtube" videos still exhibit high word error rates. Such data typically
occupies a very broad domain, has been recorded in challenging conditions, with
cheap hardware and a focus on the visual modality, and may have been
post-processed or edit... | Visual Features for Context-Aware Speech Recognition | 2,017 | http://arxiv.org/pdf/1712.00489v1 | Title Visual Features ContextAware Speech Recognition Summary Automatic transcription consumergenerated multimedia content Youtube video still exhibit high word error rate data typically occupies broad domain recorded challenging condition cheap hardware focus visual modality may postprocessed edited paper extend earli... | [0.0262033361941576, 0.04302217438817024, 0.01628383994102478, 0.030029460787773132, -0.01211551297456026, 0.0027189040556550026, 0.026020489633083344, 0.02941032312810421, -0.07764282822608948, -0.09758231788873672, -0.01810879074037075, 0.020536627620458603, 0.04438067972660065, 0.04513340815901756, 0.002862241351976... |
410 | 410 | ['Mircea Mironenco', 'Dana Kianfar', 'Ke Tran', 'Evangelos Kanoulas', 'Efstratios Gavves'] | 1712.01329v1 | In this work we propose a blackbox intervention method for visual dialog
models, with the aim of assessing the contribution of individual linguistic or
visual components. Concretely, we conduct structured or randomized
interventions that aim to impair an individual component of the model, and
observe changes in task pe... | Examining Cooperation in Visual Dialog Models | 2,017 | http://arxiv.org/pdf/1712.01329v1 | Title Examining Cooperation Visual Dialog Models Summary work propose blackbox intervention method visual dialog model aim assessing contribution individual linguistic visual component Concretely conduct structured randomized intervention aim impair individual component model observe change task performance reproduce s... | [0.08261365443468094, -0.001558822812512517, -0.053738925606012344, 0.013611875474452972, -0.054988399147987366, 0.022216124460101128, 0.04457809031009674, 0.045046523213386536, 0.017086386680603027, -0.04278905317187309, -0.01808173768222332, -0.020458683371543884, 0.0247054323554039, 0.07439517229795456, -0.000651815... |
411 | 411 | ['Cheng-Yang Fu', 'Joon Lee', 'Mohit Bansal', 'Alexander C. Berg'] | 1707.08559v1 | Sports channel video portals offer an exciting domain for research on
multimodal, multilingual analysis. We present methods addressing the problem of
automatic video highlight prediction based on joint visual features and textual
analysis of the real-world audience discourse with complex slang, in both
English and trad... | Video Highlight Prediction Using Audience Chat Reactions | 2,017 | http://arxiv.org/pdf/1707.08559v1 | Title Video Highlight Prediction Using Audience Chat Reactions Summary Sports channel video portal offer exciting domain research multimodal multilingual analysis present method addressing problem automatic video highlight prediction based joint visual feature textual analysis realworld audience discourse complex slang... | [0.03348467871546745, 0.007148250471800566, -0.01630496047437191, 0.0561295747756958, 0.026042751967906952, 0.004499316215515137, -0.009511063806712627, 0.03231172263622284, -0.019246330484747887, -0.07985155284404755, 0.009361283853650093, -0.05005268380045891, -0.02249239571392536, 0.08811645209789276, 0.024427358061... |
412 | 412 | ['Dmitriy Serdyuk', 'Kartik Audhkhasi', 'Philémon Brakel', 'Bhuvana Ramabhadran', 'Samuel Thomas', 'Yoshua Bengio'] | 1612.01928v1 | Modern automatic speech recognition (ASR) systems need to be robust under
acoustic variability arising from environmental, speaker, channel, and
recording conditions. Ensuring such robustness to variability is a challenge in
modern day neural network-based ASR systems, especially when all types of
variability are not s... | Invariant Representations for Noisy Speech Recognition | 2,016 | http://arxiv.org/pdf/1612.01928v1 | Title Invariant Representations Noisy Speech Recognition Summary Modern automatic speech recognition ASR system need robust acoustic variability arising environmental speaker channel recording condition Ensuring robustness variability challenge modern day neural networkbased ASR system especially type variability seen ... | [-0.0066824546083807945, 0.041211169213056564, -0.0013693394139409065, 0.020190533250570297, 0.03499221429228783, -0.011152408085763454, 0.05345822498202324, -0.023711681365966797, -0.03150981664657593, -0.020177843049168587, -0.06726479530334473, -0.011691833846271038, 0.016218282282352448, 0.02350870706140995, 0.0457... |
413 | 413 | ['Kalin Stefanov', 'Jonas Beskow', 'Giampiero Salvi'] | 1711.08992v1 | This paper presents a self-supervised method for detecting the active speaker
in a multi-person spoken interaction scenario. We argue that this capability is
a fundamental prerequisite for any artificial cognitive system attempting to
acquire language in social settings. Our methods are able to detect an
arbitrary numb... | Self-Supervised Vision-Based Detection of the Active Speaker as a
Prerequisite for Socially-Aware Language Acquisition | 2,017 | http://arxiv.org/pdf/1711.08992v1 | Title SelfSupervised VisionBased Detection Active Speaker Prerequisite SociallyAware Language Acquisition Summary paper present selfsupervised method detecting active speaker multiperson spoken interaction scenario argue capability fundamental prerequisite artificial cognitive system attempting acquire language social ... | [0.026632023975253105, -0.03483507037162781, -0.027896825224161148, 0.05321666598320007, -0.004127347841858864, 0.010405980981886387, 0.005493324249982834, -0.02799266017973423, 0.050446610897779465, -0.02850143425166607, -0.015240682289004326, 0.018956303596496582, 0.039852406829595566, 0.01963954232633114, 0.02493328... |
414 | 414 | ['Ângelo Cardoso', 'Fabio Daolio', 'Saúl Vargas'] | 1803.07679v1 | In this paper, we describe a solution to tackle a common set of challenges in
e-commerce, which arise from the fact that new products are continually being
added to the catalogue. The challenges involve properly personalising the
customer experience, forecasting demand and planning the product range. We
argue that the ... | Product Characterisation towards Personalisation: Learning Attributes
from Unstructured Data to Recommend Fashion Products | 2,018 | http://arxiv.org/pdf/1803.07679v1 | Title Product Characterisation towards Personalisation Learning Attributes Unstructured Data Recommend Fashion Products Summary paper describe solution tackle common set challenge ecommerce arise fact new product continually added catalogue challenge involve properly personalising customer experience forecasting demand... | [0.014864067547023296, 0.0035405221860855818, -0.019473256543278694, 0.013838011771440506, 0.015247411094605923, -0.018700018525123596, 0.04886321723461151, -0.003940575290471315, -0.02153913304209709, -0.03618629649281502, -0.019771840423345566, 0.027456816285848618, 0.003578206291422248, 0.10362014919519424, -0.03508... |
415 | 415 | ['Pierre-Yves Oudeyer'] | cs/0502086v1 | The speech code is a vehicle of language: it defines a set of forms used by a
community to carry information. Such a code is necessary to support the
linguistic interactions that allow humans to communicate. How then may a speech
code be formed prior to the existence of linguistic interactions? Moreover, the
human spee... | The Self-Organization of Speech Sounds | 2,005 | http://arxiv.org/pdf/cs/0502086v1 | Title SelfOrganization Speech Sounds Summary speech code vehicle language defines set form used community carry information code necessary support linguistic interaction allow human communicate may speech code formed prior existence linguistic interaction Moreover human speech code discrete compositional shared individ... | [0.004522000905126333, 0.037893615663051605, -0.02303241938352585, -0.00526499655097723, 0.0034601346123963594, -0.005060199648141861, 0.0422058142721653, 0.004528329242020845, -0.0005553242517635226, -0.027725689113140106, -0.04336715489625931, 0.05382722243666649, 0.034057144075632095, 0.04156311973929405, 0.05471818... |
416 | 416 | ['David M. W. Powers'] | 1503.06410v1 | The F-measure or F-score is one of the most commonly used single number
measures in Information Retrieval, Natural Language Processing and Machine
Learning, but it is based on a mistake, and the flawed assumptions render it
unsuitable for use in most contexts! Fortunately, there are better
alternatives. | What the F-measure doesn't measure: Features, Flaws, Fallacies and Fixes | 2,015 | http://arxiv.org/pdf/1503.06410v1 | Title Fmeasure doesnt measure Features Flaws Fallacies Fixes Summary Fmeasure Fscore one commonly used single number measure Information Retrieval Natural Language Processing Machine Learning based mistake flawed assumption render unsuitable use context Fortunately better alternative Authors 0 Ahmed Osman Wojciech Same... | [0.02853039652109146, 0.03242217376828194, -0.027932828292250633, -0.008576486259698868, -0.023038577288389206, 0.014914223924279213, 0.04338524490594864, 0.003689747303724289, -0.0029782666824758053, -0.015994569286704063, 0.022381436079740524, -0.04872889071702957, 0.0673162192106247, 0.061363864690065384, -0.0245049... |
417 | 417 | ['Byron Knoll', 'Nando de Freitas'] | 1108.3298v1 | PAQ8 is an open source lossless data compression algorithm that currently
achieves the best compression rates on many benchmarks. This report presents a
detailed description of PAQ8 from a statistical machine learning perspective.
It shows that it is possible to understand some of the modules of PAQ8 and use
this under... | A Machine Learning Perspective on Predictive Coding with PAQ | 2,011 | http://arxiv.org/pdf/1108.3298v1 | Title Machine Learning Perspective Predictive Coding PAQ Summary PAQ8 open source lossless data compression algorithm currently achieves best compression rate many benchmark report present detailed description PAQ8 statistical machine learning perspective show possible understand module PAQ8 use understanding improve m... | [-0.023787135258316994, 0.0296892449259758, -0.012251048348844051, 0.03194970637559891, -0.00995184201747179, 0.002444350393489003, -0.03844984620809555, 0.062469519674777985, -0.07234214246273041, -0.0002977386175189167, 0.019992707297205925, 0.013499472290277481, 0.03565507009625435, 0.08334913104772568, 0.0083262789... |
418 | 418 | ['Hector Allende', 'Emanuele Frandi', 'Ricardo Nanculef', 'Claudio Sartori'] | 1304.1014v2 | Recently, there has been a renewed interest in the machine learning community
for variants of a sparse greedy approximation procedure for concave
optimization known as {the Frank-Wolfe (FW) method}. In particular, this
procedure has been successfully applied to train large-scale instances of
non-linear Support Vector M... | A Novel Frank-Wolfe Algorithm. Analysis and Applications to Large-Scale
SVM Training | 2,013 | http://arxiv.org/pdf/1304.1014v2 | Title Novel FrankWolfe Algorithm Analysis Applications LargeScale SVM Training Summary Recently renewed interest machine learning community variant sparse greedy approximation procedure concave optimization known FrankWolfe FW method particular procedure successfully applied train largescale instance nonlinear Support ... | [0.00035654165549203753, -0.011363559402525425, 0.0011729124234989285, 0.04786599427461624, 0.008152477443218231, 0.0029361483175307512, -0.0018268664134666324, 0.023853057995438576, -0.0084852809086442, -0.02327011339366436, 0.007954495958983898, -0.032711416482925415, 0.02722095511853695, -0.009915714152157307, 0.001... |
419 | 419 | ['Yanwei Fu', 'Leonid Sigal'] | 1604.07093v1 | Despite significant progress in object categorization, in recent years, a
number of important challenges remain, mainly, ability to learn from limited
labeled data and ability to recognize object classes within large, potentially
open, set of labels. Zero-shot learning is one way of addressing these
challenges, but it ... | Semi-supervised Vocabulary-informed Learning | 2,016 | http://arxiv.org/pdf/1604.07093v1 | Title Semisupervised Vocabularyinformed Learning Summary Despite significant progress object categorization recent year number important challenge remain mainly ability learn limited labeled data ability recognize object class within large potentially open set label Zeroshot learning one way addressing challenge shown ... | [-0.019598769024014473, 0.014297908172011375, 0.0270952470600605, 0.08113818615674973, 0.007989886216819286, 0.011461552232503891, 0.046187061816453934, 0.01979505456984043, -0.005289588123559952, -0.0718226432800293, -0.030910111963748932, 0.01094746496528387, -0.07155538350343704, 0.03528866171836853, -0.010574105195... |
420 | 420 | ['Adarsh Prasad', 'Stefanie Jegelka', 'Dhruv Batra'] | 1411.1752v1 | To cope with the high level of ambiguity faced in domains such as Computer
Vision or Natural Language processing, robust prediction methods often search
for a diverse set of high-quality candidate solutions or proposals. In
structured prediction problems, this becomes a daunting task, as the solution
space (image label... | Submodular meets Structured: Finding Diverse Subsets in
Exponentially-Large Structured Item Sets | 2,014 | http://arxiv.org/pdf/1411.1752v1 | Title Submodular meet Structured Finding Diverse Subsets ExponentiallyLarge Structured Item Sets Summary cope high level ambiguity faced domain Computer Vision Natural Language processing robust prediction method often search diverse set highquality candidate solution proposal structured prediction problem becomes daun... | [0.04298301786184311, 0.014171047136187553, -0.023479577153921127, 0.036026570945978165, -0.012289566919207573, -0.012425921857357025, 0.007665161043405533, 0.02899244986474514, 0.035471104085445404, -0.07495713979005814, 0.005614526104182005, -0.035461537539958954, 0.016258588060736656, 0.08860262483358383, 0.00868114... |
421 | 421 | ['Hao Wang', 'Xiaodan Liang', 'Hao Zhang', 'Dit-Yan Yeung', 'Eric P. Xing'] | 1703.07255v2 | Many problems in image processing and computer vision (e.g. colorization,
style transfer) can be posed as 'manipulating' an input image into a
corresponding output image given a user-specified guiding signal. A holy-grail
solution towards generic image manipulation should be able to efficiently alter
an input image wit... | ZM-Net: Real-time Zero-shot Image Manipulation Network | 2,017 | http://arxiv.org/pdf/1703.07255v2 | Title ZMNet Realtime Zeroshot Image Manipulation Network Summary Many problem image processing computer vision eg colorization style transfer posed manipulating input image corresponding output image given userspecified guiding signal holygrail solution towards generic image manipulation able efficiently alter input im... | [-0.00017065303109120578, 0.02034536749124527, 0.028135545551776886, 0.03961324319243431, -0.01038052886724472, -0.014034738764166832, 0.04388752579689026, 0.024866612628102303, -0.10656560212373734, -0.01895078271627426, -0.053714558482170105, 0.03644692897796631, -0.023473083972930908, 0.024208905175328255, 0.0166290... |
422 | 422 | ['Arnab Ghosh', 'Viveka Kulharia', 'Vinay Namboodiri', 'Philip H. S. Torr', 'Puneet K. Dokania'] | 1704.02906v2 | We propose an intuitive generalization to the Generative Adversarial Networks
(GANs) and its conditional variants to address the well known mode collapse
problem. Firstly, we propose a multi-agent GAN architecture incorporating
multiple generators and one discriminator. Secondly, to enforce different
generators to capt... | Multi-Agent Diverse Generative Adversarial Networks | 2,017 | http://arxiv.org/pdf/1704.02906v2 | Title MultiAgent Diverse Generative Adversarial Networks Summary propose intuitive generalization Generative Adversarial Networks GANs conditional variant address well known mode collapse problem Firstly propose multiagent GAN architecture incorporating multiple generator one discriminator Secondly enforce different ge... | [-0.006788115948438644, 0.08157891780138016, -0.013384711928665638, 0.011438085697591305, 0.008202669210731983, 0.0033263543155044317, 0.07081679254770279, -0.004084078595042229, -0.036281030625104904, 0.029036708176136017, -0.0325741320848465, -0.011441606096923351, -0.03196254372596741, 0.018260378390550613, 0.076945... |
423 | 423 | ['Jae Hyun Lim', 'Jong Chul Ye'] | 1705.02894v2 | Generative Adversarial Nets (GANs) represent an important milestone for
effective generative models, which has inspired numerous variants seemingly
different from each other. One of the main contributions of this paper is to
reveal a unified geometric structure in GAN and its variants. Specifically, we
show that the ad... | Geometric GAN | 2,017 | http://arxiv.org/pdf/1705.02894v2 | Title Geometric GAN Summary Generative Adversarial Nets GANs represent important milestone effective generative model inspired numerous variant seemingly different One main contribution paper reveal unified geometric structure GAN variant Specifically show adversarial generative model training decomposed three geometri... | [0.02296755649149418, 0.03869876265525818, -0.02602660283446312, 0.03212890401482582, 0.008779403753578663, 0.005982937291264534, 0.03460725024342537, -0.03673403337597847, -0.03673959895968437, 0.0370747447013855, -0.0005408736760728061, 0.02223697490990162, -0.02752140536904335, 0.018071528524160385, 0.09001615643501... |
424 | 424 | ['Disha Shrivastava', 'Santanu Chaudhury', 'Dr. Jayadeva'] | 1708.05840v1 | Training deep networks is expensive and time-consuming with the training
period increasing with data size and growth in model parameters. In this paper,
we provide a framework for distributed training of deep networks over a cluster
of CPUs in Apache Spark. The framework implements both Data Parallelism and
Model Paral... | A Data and Model-Parallel, Distributed and Scalable Framework for
Training of Deep Networks in Apache Spark | 2,017 | http://arxiv.org/pdf/1708.05840v1 | Title Data ModelParallel Distributed Scalable Framework Training Deep Networks Apache Spark Summary Training deep network expensive timeconsuming training period increasing data size growth model parameter paper provide framework distributed training deep network cluster CPUs Apache Spark framework implement Data Paral... | [-0.0018520618323236704, 0.01606283150613308, -0.014184780418872833, 0.07739543914794922, 0.008176264353096485, 0.006769976112991571, 0.06531637161970139, -0.003001242643222213, 0.004967254586517811, -0.012263813987374306, -0.005408227909356356, 0.029699349775910378, -0.021033965051174164, 0.06446307897567749, -0.00516... |
425 | 425 | ['Sebastian Lapuschkin', 'Alexander Binder', 'Klaus-Robert Müller', 'Wojciech Samek'] | 1708.07689v1 | Recently, deep neural networks have demonstrated excellent performances in
recognizing the age and gender on human face images. However, these models were
applied in a black-box manner with no information provided about which facial
features are actually used for prediction and how these features depend on
image prepro... | Understanding and Comparing Deep Neural Networks for Age and Gender
Classification | 2,017 | http://arxiv.org/pdf/1708.07689v1 | Title Understanding Comparing Deep Neural Networks Age Gender Classification Summary Recently deep neural network demonstrated excellent performance recognizing age gender human face image However model applied blackbox manner information provided facial feature actually used prediction feature depend image preprocessi... | [0.026481812819838524, 0.03845563903450966, -0.015925094485282898, 0.024296240881085396, 0.008811231702566147, 0.03944318741559982, 0.0840742439031601, -0.01714557781815529, -0.006307459436357021, 0.015625886619091034, 0.0006790544139221311, -0.06746894866228104, 0.04517519101500511, 0.0567784383893013, 0.0085363583639... |
426 | 426 | ['Simon S. Du', 'Jason D. Lee', 'Yuandong Tian'] | 1709.06129v2 | We analyze the convergence of (stochastic) gradient descent algorithm for
learning a convolutional filter with Rectified Linear Unit (ReLU) activation
function. Our analysis does not rely on any specific form of the input
distribution and our proofs only use the definition of ReLU, in contrast with
previous works that ... | When is a Convolutional Filter Easy To Learn? | 2,017 | http://arxiv.org/pdf/1709.06129v2 | Title Convolutional Filter Easy Learn Summary analyze convergence stochastic gradient descent algorithm learning convolutional filter Rectified Linear Unit ReLU activation function analysis rely specific form input distribution proof use definition ReLU contrast previous work restricted standard Gaussian input show sto... | [-0.0328933484852314, 0.0046985335648059845, 0.0005247725057415664, 0.07850399613380432, 0.01807556487619877, -0.06688686460256577, 0.01794382743537426, 0.021067945286631584, -0.027484670281410217, 0.01705227978527546, 0.004229845944792032, 0.009164389222860336, -0.004456693306565285, 0.06976305693387985, -0.0015496979... |
427 | 427 | ['Jianqiao Wangni', 'Dahua Lin'] | 1711.02857v1 | Sparsity inducing regularization is an important part for learning
over-complete visual representations. Despite the popularity of $\ell_1$
regularization, in this paper, we investigate the usage of non-convex
regularizations in this problem. Our contribution consists of three parts.
First, we propose the leaky capped ... | Learning Sparse Visual Representations with Leaky Capped Norm
Regularizers | 2,017 | http://arxiv.org/pdf/1711.02857v1 | Title Learning Sparse Visual Representations Leaky Capped Norm Regularizers Summary Sparsity inducing regularization important part learning overcomplete visual representation Despite popularity ell1 regularization paper investigate usage nonconvex regularization problem contribution consists three part First propose l... | [-0.008767512626945972, 0.04537312686443329, 0.006528915371745825, 0.05196036025881767, 0.010393067263066769, -0.022254910320043564, -0.014145621098577976, -0.014949527569115162, 0.0015199027257040143, 0.03983422368764877, -0.002558992011472583, 0.005046372767537832, 0.012634659186005592, 0.06136992573738098, 0.0481484... |
428 | 428 | ['Pierre Stock', 'Moustapha Cisse'] | 1711.11443v1 | ConvNets and Imagenet have driven the recent success of deep learning for
image classification. However, the marked slowdown in performance improvement,
the recent studies on the lack of robustness of neural networks to adversarial
examples and their tendency to exhibit undesirable biases (e.g racial biases)
questioned... | ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection,
Adversarial Examples and Model Criticism | 2,017 | http://arxiv.org/pdf/1711.11443v1 | Title ConvNets ImageNet Beyond Accuracy Explanations Bias Detection Adversarial Examples Model Criticism Summary ConvNets Imagenet driven recent success deep learning image classification However marked slowdown performance improvement recent study lack robustness neural network adversarial example tendency exhibit und... | [0.013222314417362213, 0.05636214837431908, -0.03590932860970497, 0.04753301665186882, 0.016025608405470848, -0.0021171909756958485, 0.0635276809334755, -0.0063713788986206055, -0.00709938257932663, -0.0005602426826953888, -0.012481095269322395, 0.024519531056284904, -0.0032368318643420935, 0.02208569459617138, 0.01956... |
429 | 429 | ['Simon S. Du', 'Jason D. Lee', 'Yuandong Tian', 'Barnabas Poczos', 'Aarti Singh'] | 1712.00779v1 | We consider the problem of learning a one-hidden-layer neural network with
non-overlapping convolutional layer and ReLU activation function, i.e.,
$f(\mathbf{Z}; \mathbf{w}, \mathbf{a}) = \sum_j
a_j\sigma(\mathbf{w}^\top\mathbf{Z}_j)$, in which both the convolutional
weights $\mathbf{w}$ and the output weights $\mathbf... | Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of
Spurious Local Minima | 2,017 | http://arxiv.org/pdf/1712.00779v1 | Title Gradient Descent Learns Onehiddenlayer CNN Dont Afraid Spurious Local Minima Summary consider problem learning onehiddenlayer neural network nonoverlapping convolutional layer ReLU activation function ie fmathbfZ mathbfw mathbfa sumj ajsigmamathbfwtopmathbfZj convolutional weight mathbfw output weight mathbfa par... | [-0.015815701335668564, 0.0034348166082054377, 0.004034135490655899, 0.0038254817482084036, 0.00422449316829443, -0.04407276213169098, -0.015035745687782764, 0.004122546873986721, -0.06227981299161911, 0.05880111828446388, 0.027219809591770172, 0.01928306370973587, 0.016228588297963142, -0.0013058217009529471, -0.00276... |
430 | 430 | ['Deepak Pathak', 'Pulkit Agrawal', 'Alexei A. Efros', 'Trevor Darrell'] | 1705.05363v1 | In many real-world scenarios, rewards extrinsic to the agent are extremely
sparse, or absent altogether. In such cases, curiosity can serve as an
intrinsic reward signal to enable the agent to explore its environment and
learn skills that might be useful later in its life. We formulate curiosity as
the error in an agen... | Curiosity-driven Exploration by Self-supervised Prediction | 2,017 | http://arxiv.org/pdf/1705.05363v1 | Title Curiositydriven Exploration Selfsupervised Prediction Summary many realworld scenario reward extrinsic agent extremely sparse absent altogether case curiosity serve intrinsic reward signal enable agent explore environment learn skill might useful later life formulate curiosity error agent ability predict conseque... | [0.019236771389842033, 0.03240903839468956, -0.024170326068997383, 0.008853362873196602, 0.015394032001495361, -0.02874867245554924, 0.008909371681511402, -0.008354834280908108, -0.021054735407233238, 0.03710471838712692, -0.008091621100902557, 0.0486241839826107, -0.06268144398927689, 0.08134281635284424, 0.0042772707... |
431 | 431 | ['Moustapha Cisse', 'Yossi Adi', 'Natalia Neverova', 'Joseph Keshet'] | 1707.05373v1 | Generating adversarial examples is a critical step for evaluating and
improving the robustness of learning machines. So far, most existing methods
only work for classification and are not designed to alter the true performance
measure of the problem at hand. We introduce a novel flexible approach named
Houdini for gene... | Houdini: Fooling Deep Structured Prediction Models | 2,017 | http://arxiv.org/pdf/1707.05373v1 | Title Houdini Fooling Deep Structured Prediction Models Summary Generating adversarial example critical step evaluating improving robustness learning machine far existing method work classification designed alter true performance measure problem hand introduce novel flexible approach named Houdini generating adversaria... | [0.011369560845196247, 0.05320367589592934, -0.024191319942474365, 0.04087435454130173, -0.0031127736438065767, -0.005763102788478136, 0.016725635156035423, -0.0004394883580971509, 0.008062866516411304, -0.031695492565631866, -0.045212436467409134, 0.03004785254597664, 0.015374635346233845, 0.02745014615356922, 0.03978... |
432 | 432 | ['Yanwei Fu', 'Tao Xiang', 'Yu-Gang Jiang', 'Xiangyang Xue', 'Leonid Sigal', 'Shaogang Gong'] | 1710.04837v1 | With the recent renaissance of deep convolution neural networks, encouraging
breakthroughs have been achieved on the supervised recognition tasks, where
each class has sufficient training data and fully annotated training data.
However, to scale the recognition to a large number of classes with few or now
training samp... | Recent Advances in Zero-shot Recognition | 2,017 | http://arxiv.org/pdf/1710.04837v1 | Title Recent Advances Zeroshot Recognition Summary recent renaissance deep convolution neural network encouraging breakthrough achieved supervised recognition task class sufficient training data fully annotated training data However scale recognition large number class training sample class remains unsolved problem One... | [-0.07141465693712234, -0.005048695486038923, 0.027985263615846634, 0.04772936925292015, -0.01042885147035122, -0.005197441205382347, 0.09112165123224258, 0.05420561134815216, -0.058816999197006226, -0.053750135004520416, 0.0024923193268477917, 0.00012013706145808101, -0.0037958689499646425, 0.0007211341289803386, 0.00... |
433 | 433 | ['Quynh Nguyen', 'Matthias Hein'] | 1710.10928v1 | We analyze the expressiveness and loss surface of practical deep
convolutional neural networks (CNNs) with shared weights and max pooling
layers. We show that such CNNs produce linearly independent features at a
"wide" layer which has more neurons than the number of training samples. This
condition holds e.g. for the V... | The loss surface and expressivity of deep convolutional neural networks | 2,017 | http://arxiv.org/pdf/1710.10928v1 | Title loss surface expressivity deep convolutional neural network Summary analyze expressiveness loss surface practical deep convolutional neural network CNNs shared weight max pooling layer show CNNs produce linearly independent feature wide layer neuron number training sample condition hold eg VGG network Furthermore... | [-0.004566425923258066, 0.00406654505059123, 0.002062950748950243, 0.0939943864941597, 0.02027236297726631, -0.02716669999063015, 0.024386368691921234, -0.003845380851998925, -0.05316748842597008, 0.019360898062586784, -0.007746878080070019, 0.041482966393232346, -0.020502062514424324, 0.039715688675642014, 0.050672046... |
434 | 434 | ['Anuj Karpatne', 'William Watkins', 'Jordan Read', 'Vipin Kumar'] | 1710.11431v2 | This paper introduces a novel framework for combining scientific knowledge of
physics-based models with neural networks to advance scientific discovery. This
framework, termed as physics-guided neural network (PGNN), leverages the output
of physics-based model simulations along with observational features to
generate p... | Physics-guided Neural Networks (PGNN): An Application in Lake
Temperature Modeling | 2,017 | http://arxiv.org/pdf/1710.11431v2 | Title Physicsguided Neural Networks PGNN Application Lake Temperature Modeling Summary paper introduces novel framework combining scientific knowledge physicsbased model neural network advance scientific discovery framework termed physicsguided neural network PGNN leverage output physicsbased model simulation along obs... | [-0.02649982087314129, -0.029062263667583466, -0.009417790919542313, 0.02430092729628086, -0.005859453696757555, -0.029969457536935806, 0.025071565061807632, -0.02109672501683235, 0.05818287283182144, -0.0058889687061309814, -0.0374637246131897, 0.025711659342050552, 0.06218906119465828, 0.03147970139980316, 0.05102292... |
435 | 435 | ['Zhao Kang', 'Chong Peng', 'Qiang Cheng', 'Zenglin Xu'] | 1711.04258v1 | Spectral clustering has found extensive use in many areas. Most traditional
spectral clustering algorithms work in three separate steps: similarity graph
construction; continuous labels learning; discretizing the learned labels by
k-means clustering. Such common practice has two potential flaws, which may
lead to sever... | Unified Spectral Clustering with Optimal Graph | 2,017 | http://arxiv.org/pdf/1711.04258v1 | Title Unified Spectral Clustering Optimal Graph Summary Spectral clustering found extensive use many area traditional spectral clustering algorithm work three separate step similarity graph construction continuous label learning discretizing learned label kmeans clustering common practice two potential flaw may lead se... | [-0.015670178458094597, -0.024089861661195755, -0.012890690937638283, 0.03483209013938904, -0.009417342022061348, -0.02201218530535698, 0.02096923440694809, -0.0009460219880566001, 0.029986942186951637, -0.0007581560057587922, -0.035129230469465256, 0.04066723585128784, 0.016056865453720093, -0.004265537951141596, -0.0... |
436 | 436 | ['David P. Helmbold', 'Philip M. Long'] | 1412.4736v4 | Dropout is a simple but effective technique for learning in neural networks
and other settings. A sound theoretical understanding of dropout is needed to
determine when dropout should be applied and how to use it most effectively. In
this paper we continue the exploration of dropout as a regularizer pioneered by
Wager,... | On the Inductive Bias of Dropout | 2,014 | http://arxiv.org/pdf/1412.4736v4 | Title Inductive Bias Dropout Summary Dropout simple effective technique learning neural network setting sound theoretical understanding dropout needed determine dropout applied use effectively paper continue exploration dropout regularizer pioneered Wager etal focus linear classification convex proxy misclassification ... | [-0.005859412252902985, 0.052522558718919754, -0.025943441316485405, 0.017594603821635246, 0.007508797105401754, -0.026685962453484535, 0.06674736738204956, -8.999822603072971e-05, -0.0064962864853441715, 0.02773449383676052, 0.019221490249037743, 0.07637382298707962, 0.01978726126253605, 0.025818446651101112, 0.030248... |
437 | 437 | ['David P. Helmbold', 'Philip M. Long'] | 1602.04484v5 | We analyze dropout in deep networks with rectified linear units and the
quadratic loss. Our results expose surprising differences between the behavior
of dropout and more traditional regularizers like weight decay. For example, on
some simple data sets dropout training produces negative weights even though
the output i... | Surprising properties of dropout in deep networks | 2,016 | http://arxiv.org/pdf/1602.04484v5 | Title Surprising property dropout deep network Summary analyze dropout deep network rectified linear unit quadratic loss result expose surprising difference behavior dropout traditional regularizers like weight decay example simple data set dropout training produce negative weight even though output sum input provides ... | [-0.03788214549422264, 0.061066094785928726, -0.014872054569423199, 0.028878727927803993, 0.01833534799516201, -0.010069230571389198, 0.030231766402721405, -0.006657672580331564, -0.01702852174639702, 0.052752669900655746, 0.009050716646015644, 0.06037207692861557, 0.00027876460808329284, 0.08301247656345367, 0.0140492... |
438 | 438 | ['Alireza Bagheri', 'Osvaldo Simeone', 'Bipin Rajendran'] | 1710.10704v3 | Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at
harnessing the energy efficiency of spike-domain processing by building on
computing elements that operate on, and exchange, spikes. In this paper, the
problem of training a two-layer SNN is studied for the purpose of
classification, under a Ge... | Training Probabilistic Spiking Neural Networks with First-to-spike
Decoding | 2,017 | http://arxiv.org/pdf/1710.10704v3 | Title Training Probabilistic Spiking Neural Networks Firsttospike Decoding Summary Thirdgeneration neural network Spiking Neural Networks SNNs aim harnessing energy efficiency spikedomain processing building computing element operate exchange spike paper problem training twolayer SNN studied purpose classification Gene... | [-0.039866380393505096, -0.04846152663230896, -0.022309591993689537, 0.05436500534415245, 0.01894194819033146, 0.0014488634187728167, 0.01863725483417511, -0.0003226301050744951, -0.001143521280027926, -0.008925831876695156, -0.03076167032122612, 0.022754700854420662, 0.022677382454276085, 0.08209702372550964, 0.028869... |
439 | 439 | ['Qiang Li', 'Yan He', 'Jing-ping Jiang'] | 0812.0743v2 | Enormous successes have been made by quantum algorithms during the last
decade. In this paper, we combine the quantum game with the problem of data
clustering, and then develop a quantum-game-based clustering algorithm, in
which data points in a dataset are considered as players who can make decisions
and implement qua... | A Novel Clustering Algorithm Based on Quantum Games | 2,008 | http://arxiv.org/pdf/0812.0743v2 | Title Novel Clustering Algorithm Based Quantum Games Summary Enormous success made quantum algorithm last decade paper combine quantum game problem data clustering develop quantumgamebased clustering algorithm data point dataset considered player make decision implement quantum strategy quantum game round quantum game ... | [-0.02627340331673622, 0.014408312737941742, -0.07075487822294235, 0.05268136039376259, -0.03348598629236221, -0.019654547795653343, -0.02003326825797558, 0.035398319363594055, 0.042569659650325775, 0.011598685756325722, -0.029186438769102097, -0.009110826067626476, -0.011148972436785698, -0.0004399906028993428, 0.0229... |
440 | 440 | ['Andrew M. Saxe', 'James L. McClelland', 'Surya Ganguli'] | 1312.6120v3 | Despite the widespread practical success of deep learning methods, our
theoretical understanding of the dynamics of learning in deep neural networks
remains quite sparse. We attempt to bridge the gap between the theory and
practice of deep learning by systematically analyzing learning dynamics for the
restricted case o... | Exact solutions to the nonlinear dynamics of learning in deep linear
neural networks | 2,013 | http://arxiv.org/pdf/1312.6120v3 | Title Exact solution nonlinear dynamic learning deep linear neural network Summary Despite widespread practical success deep learning method theoretical understanding dynamic learning deep neural network remains quite sparse attempt bridge gap theory practice deep learning systematically analyzing learning dynamic rest... | [-0.014215430244803429, 0.001987025374546647, -0.04314752668142319, 0.006781932432204485, 0.023280799388885498, -0.014689714647829533, -0.00482304347679019, -0.021557992324233055, -0.043977826833724976, 0.03281954675912857, 0.010095802135765553, 0.03388497233390808, -0.020579464733600616, 0.04592586308717728, 0.0031665... |
441 | 441 | ['Paul Honeine'] | 1411.0161v1 | In signal analysis and synthesis, linear approximation theory considers a
linear decomposition of any given signal in a set of atoms, collected into a
so-called dictionary. Relevant sparse representations are obtained by relaxing
the orthogonality condition of the atoms, yielding overcomplete dictionaries
with an exten... | Entropy of Overcomplete Kernel Dictionaries | 2,014 | http://arxiv.org/pdf/1411.0161v1 | Title Entropy Overcomplete Kernel Dictionaries Summary signal analysis synthesis linear approximation theory considers linear decomposition given signal set atom collected socalled dictionary Relevant sparse representation obtained relaxing orthogonality condition atom yielding overcomplete dictionary extended number a... | [-0.04676356166601181, 0.012102759443223476, -7.161893881857395e-05, 0.022028762847185135, 0.007572753354907036, -0.018062887713313103, -0.006840858142822981, 0.03293129429221153, -0.07297536730766296, -0.006776285357773304, 0.028882235288619995, 0.03125356510281563, 0.027265353128314018, 0.024965962395071983, -0.01227... |
442 | 442 | ['Sander Dieleman', 'Kyle W. Willett', 'Joni Dambre'] | 1503.07077v1 | Measuring the morphological parameters of galaxies is a key requirement for
studying their formation and evolution. Surveys such as the Sloan Digital Sky
Survey (SDSS) have resulted in the availability of very large collections of
images, which have permitted population-wide analyses of galaxy morphology.
Morphological... | Rotation-invariant convolutional neural networks for galaxy morphology
prediction | 2,015 | http://arxiv.org/pdf/1503.07077v1 | Title Rotationinvariant convolutional neural network galaxy morphology prediction Summary Measuring morphological parameter galaxy key requirement studying formation evolution Surveys Sloan Digital Sky Survey SDSS resulted availability large collection image permitted populationwide analysis galaxy morphology Morpholog... | [0.03055027313530445, -0.02055300958454609, -0.008313288912177086, 0.007910335436463356, -0.0001570279273437336, 0.0033753109164536, 0.049835000187158585, 0.009784825146198273, -0.0711127445101738, 0.025293055921792984, -0.02783234603703022, -0.02360847406089306, 0.0166450385004282, -0.011844807304441929, -0.0017254312... |
443 | 443 | ['Fei Zhu', 'Paul Honeine', 'Maya Kallas'] | 1407.4420v2 | The nonnegative matrix factorization (NMF) is widely used in signal and image
processing, including bio-informatics, blind source separation and
hyperspectral image analysis in remote sensing. A great challenge arises when
dealing with a nonlinear formulation of the NMF. Within the framework of kernel
machines, the mod... | Kernel Nonnegative Matrix Factorization Without the Curse of the
Pre-image - Application to Unmixing Hyperspectral Images | 2,014 | http://arxiv.org/pdf/1407.4420v2 | Title Kernel Nonnegative Matrix Factorization Without Curse Preimage Application Unmixing Hyperspectral Images Summary nonnegative matrix factorization NMF widely used signal image processing including bioinformatics blind source separation hyperspectral image analysis remote sensing great challenge arises dealing nonl... | [0.0001514033938292414, -0.00814250111579895, -0.006567203905433416, 0.03778566047549248, 0.03802884742617607, -0.014019215479493141, -0.0038603588473051786, 0.017052078619599342, -0.035356305539608, -0.00044711376540362835, -0.006089904811233282, 0.06272603571414948, 0.04076651856303215, 0.058199603110551834, -0.01359... |
444 | 444 | ['Paul Honeine'] | 1409.6046v1 | Many machine learning frameworks, such as resource-allocating networks,
kernel-based methods, Gaussian processes, and radial-basis-function networks,
require a sparsification scheme in order to address the online learning
paradigm. For this purpose, several online sparsification criteria have been
proposed to restrict ... | Approximation errors of online sparsification criteria | 2,014 | http://arxiv.org/pdf/1409.6046v1 | Title Approximation error online sparsification criterion Summary Many machine learning framework resourceallocating network kernelbased method Gaussian process radialbasisfunction network require sparsification scheme order address online learning paradigm purpose several online sparsification criterion proposed restr... | [-0.031394410878419876, 0.04585995152592659, -0.023531474173069, 0.027557507157325745, 0.022309741005301476, -0.027468828484416008, 0.04130548611283302, 0.01737407222390175, -0.037962645292282104, 0.02389407530426979, 0.03831920400261879, 0.019206129014492035, 0.05357345566153526, 0.03625645488500595, -0.02155859395861... |
445 | 445 | ['Thomas Wiatowski', 'Michael Tschannen', 'Aleksandar Stanić', 'Philipp Grohs', 'Helmut Bölcskei'] | 1605.08283v1 | First steps towards a mathematical theory of deep convolutional neural
networks for feature extraction were made---for the continuous-time case---in
Mallat, 2012, and Wiatowski and B\"olcskei, 2015. This paper considers the
discrete case, introduces new convolutional neural network architectures, and
proposes a mathema... | Discrete Deep Feature Extraction: A Theory and New Architectures | 2,016 | http://arxiv.org/pdf/1605.08283v1 | Title Discrete Deep Feature Extraction Theory New Architectures Summary First step towards mathematical theory deep convolutional neural network feature extraction madefor continuoustime casein Mallat 2012 Wiatowski Bolcskei 2015 paper considers discrete case introduces new convolutional neural network architecture pro... | [0.0036944542080163956, 0.035360075533390045, -0.01799187995493412, 0.0591338612139225, 0.007811818737536669, 0.011754624545574188, 0.07544642686843872, 0.013092435896396637, -0.021839654073119164, 0.020087871700525284, 0.03943182900547981, -0.00460813008248806, 0.011793920770287514, 0.0609690397977829, 0.0283645372837... |
446 | 446 | ['Lifeng Shang', 'Zhengdong Lu', 'Hang Li'] | 1503.02364v2 | We propose Neural Responding Machine (NRM), a neural network-based response
generator for Short-Text Conversation. NRM takes the general encoder-decoder
framework: it formalizes the generation of response as a decoding process based
on the latent representation of the input text, while both encoding and
decoding are re... | Neural Responding Machine for Short-Text Conversation | 2,015 | http://arxiv.org/pdf/1503.02364v2 | Title Neural Responding Machine ShortText Conversation Summary propose Neural Responding Machine NRM neural networkbased response generator ShortText Conversation NRM take general encoderdecoder framework formalizes generation response decoding process based latent representation input text encoding decoding realized r... | [0.09230727702379227, 0.020093947649002075, 0.005071334540843964, 0.04573854058980942, -0.011932484805583954, 0.014874564483761787, -0.03324092924594879, -0.00833804626017809, -0.004842789843678474, -0.04736252501606941, 0.022856203839182854, -0.007944663986563683, -0.01691340282559395, 0.062297068536281586, -0.0084837... |
447 | 447 | ['Nabiha Asghar', 'Pascal Poupart', 'Xin Jiang', 'Hang Li'] | 1612.03929v5 | We propose an online, end-to-end, neural generative conversational model for
open-domain dialogue. It is trained using a unique combination of offline
two-phase supervised learning and online human-in-the-loop active learning.
While most existing research proposes offline supervision or hand-crafted
reward functions fo... | Deep Active Learning for Dialogue Generation | 2,016 | http://arxiv.org/pdf/1612.03929v5 | Title Deep Active Learning Dialogue Generation Summary propose online endtoend neural generative conversational model opendomain dialogue trained using unique combination offline twophase supervised learning online humanintheloop active learning existing research proposes offline supervision handcrafted reward function... | [0.0674745962023735, 0.03524432331323624, -0.004978268872946501, -0.04062667116522789, 0.012914099730551243, 0.01766110025346279, 0.02251812256872654, -0.014819574542343616, 0.029238346964120865, -0.049489010125398636, 0.0007450484554283321, -0.025146983563899994, -0.032757505774497986, 0.1011180505156517, 0.0071744625... |
448 | 448 | ['Karl Moritz Hermann', 'Tomáš Kočiský', 'Edward Grefenstette', 'Lasse Espeholt', 'Will Kay', 'Mustafa Suleyman', 'Phil Blunsom'] | 1506.03340v3 | Teaching machines to read natural language documents remains an elusive
challenge. Machine reading systems can be tested on their ability to answer
questions posed on the contents of documents that they have seen, but until now
large scale training and test datasets have been missing for this type of
evaluation. In thi... | Teaching Machines to Read and Comprehend | 2,015 | http://arxiv.org/pdf/1506.03340v3 | Title Teaching Machines Read Comprehend Summary Teaching machine read natural language document remains elusive challenge Machine reading system tested ability answer question posed content document seen large scale training test datasets missing type evaluation work define new methodology resolve bottleneck provides l... | [0.04625609517097473, -0.012580386362969875, -0.0031117768958210945, 0.05506395176053047, -0.008554920554161072, 0.014452523551881313, 0.04525618627667427, -0.025313731282949448, 0.010950825177133083, -0.037284497171640396, -0.014430267736315727, 0.00496969697996974, 0.015908658504486084, 0.042184483259916306, 0.017601... |
449 | 449 | ['Jianpeng Cheng', 'Dimitri Kartsaklis'] | 1508.02354v2 | Deep compositional models of meaning acting on distributional representations
of words in order to produce vectors of larger text constituents are evolving
to a popular area of NLP research. We detail a compositional distributional
framework based on a rich form of word embeddings that aims at facilitating the
interact... | Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models
of Meaning | 2,015 | http://arxiv.org/pdf/1508.02354v2 | Title SyntaxAware MultiSense Word Embeddings Deep Compositional Models Meaning Summary Deep compositional model meaning acting distributional representation word order produce vector larger text constituent evolving popular area NLP research detail compositional distributional framework based rich form word embeddings ... | [0.02820216491818428, 0.01467214897274971, -0.020512329414486885, 0.049273815006017685, -0.019087502732872963, 0.01134074479341507, -0.03433859720826149, -0.05767248570919037, -0.03139980137348175, -0.05740887299180031, 0.030665528029203415, -0.029023347422480583, 0.048987794667482376, 0.047548871487379074, 0.024283042... |
450 | 450 | ['Shengxian Wan', 'Yanyan Lan', 'Jiafeng Guo', 'Jun Xu', 'Liang Pang', 'Xueqi Cheng'] | 1511.08277v1 | Matching natural language sentences is central for many applications such as
information retrieval and question answering. Existing deep models rely on a
single sentence representation or multiple granularity representations for
matching. However, such methods cannot well capture the contextualized local
information in... | A Deep Architecture for Semantic Matching with Multiple Positional
Sentence Representations | 2,015 | http://arxiv.org/pdf/1511.08277v1 | Title Deep Architecture Semantic Matching Multiple Positional Sentence Representations Summary Matching natural language sentence central many application information retrieval question answering Existing deep model rely single sentence representation multiple granularity representation matching However method cannot w... | [0.03854307904839516, 0.0361115038394928, -0.009932505898177624, 0.07326403260231018, -0.04087432473897934, 0.002543353708460927, -0.003332993946969509, -0.0274368766695261, -0.016328807920217514, -0.05835443735122681, -0.00845292117446661, -0.010525465942919254, -0.013230201788246632, 0.016002319753170013, 0.021724427... |
451 | 451 | ['Yiming Cui', 'Shijin Wang', 'Jianfeng Li'] | 1512.00177v3 | Artificial neural networks are powerful models, which have been widely
applied into many aspects of machine translation, such as language modeling and
translation modeling. Though notable improvements have been made in these
areas, the reordering problem still remains a challenge in statistical machine
translations. In... | LSTM Neural Reordering Feature for Statistical Machine Translation | 2,015 | http://arxiv.org/pdf/1512.00177v3 | Title LSTM Neural Reordering Feature Statistical Machine Translation Summary Artificial neural network powerful model widely applied many aspect machine translation language modeling translation modeling Though notable improvement made area reordering problem still remains challenge statistical machine translation pape... | [0.01523587480187416, 0.0033701767679303885, 0.001420486718416214, 0.0695304200053215, -0.0205757487565279, -0.013303345069289207, -0.0031198582146316767, -0.013461161404848099, -0.025361331179738045, -0.00802953913807869, -0.011103509925305843, -0.06025703623890877, 0.08550680428743362, -0.004754986613988876, 0.033382... |
452 | 452 | ['Shuohang Wang', 'Jing Jiang'] | 1512.08849v2 | Natural language inference (NLI) is a fundamentally important task in natural
language processing that has many applications. The recently released Stanford
Natural Language Inference (SNLI) corpus has made it possible to develop and
evaluate learning-centered methods such as deep neural networks for natural
language i... | Learning Natural Language Inference with LSTM | 2,015 | http://arxiv.org/pdf/1512.08849v2 | Title Learning Natural Language Inference LSTM Summary Natural language inference NLI fundamentally important task natural language processing many application recently released Stanford Natural Language Inference SNLI corpus made possible develop evaluate learningcentered method deep neural network natural language in... | [0.05412283167243004, 0.020508384332060814, -0.0009332384215667844, 0.0482247993350029, -0.05180315300822258, 0.00462347362190485, 0.027472184970974922, -0.001867720391601324, 0.036760490387678146, -0.050992630422115326, 0.012076687067747116, 0.0026270465459674597, 0.014237107709050179, 0.03020339272916317, 0.010326889... |
453 | 453 | ['Phong Le', 'Willem Zuidema'] | 1603.00423v1 | Recursive neural networks (RNN) and their recently proposed extension
recursive long short term memory networks (RLSTM) are models that compute
representations for sentences, by recursively combining word embeddings
according to an externally provided parse tree. Both models thus, unlike
recurrent networks, explicitly ... | Quantifying the vanishing gradient and long distance dependency problem
in recursive neural networks and recursive LSTMs | 2,016 | http://arxiv.org/pdf/1603.00423v1 | Title Quantifying vanishing gradient long distance dependency problem recursive neural network recursive LSTMs Summary Recursive neural network RNN recently proposed extension recursive long short term memory network RLSTM model compute representation sentence recursively combining word embeddings according externally ... | [0.03300371766090393, 0.015306524001061916, 0.01262767892330885, 0.06967125833034515, -0.03792081028223038, -0.01024245098233223, -0.047460637986660004, 0.026505716145038605, -0.008483223617076874, -0.0677141547203064, -0.003822858678176999, -0.014204154722392559, 0.033913105726242065, -0.007055546622723341, -0.0216247... |
454 | 454 | ['Yang Liu', 'Sujian Li', 'Xiaodong Zhang', 'Zhifang Sui'] | 1603.02776v1 | Without discourse connectives, classifying implicit discourse relations is a
challenging task and a bottleneck for building a practical discourse parser.
Previous research usually makes use of one kind of discourse framework such as
PDTB or RST to improve the classification performance on discourse relations.
Actually,... | Implicit Discourse Relation Classification via Multi-Task Neural
Networks | 2,016 | http://arxiv.org/pdf/1603.02776v1 | Title Implicit Discourse Relation Classification via MultiTask Neural Networks Summary Without discourse connective classifying implicit discourse relation challenging task bottleneck building practical discourse parser Previous research usually make use one kind discourse framework PDTB RST improve classification perf... | [0.03985503688454628, 0.014695283025503159, 0.005900284741073847, 0.05481695383787155, -0.01843344420194626, 0.01809452660381794, 0.027498049661517143, -0.02179475873708725, 0.004185981582850218, -0.09269911050796509, -0.0570853017270565, 0.01660793460905552, -0.0031876862049102783, -0.0018962051253765821, -0.025644404... |
455 | 455 | ['Peng Li', 'Heng Huang'] | 1603.09405v1 | Neural network based approaches for sentence relation modeling automatically
generate hidden matching features from raw sentence pairs. However, the quality
of matching feature representation may not be satisfied due to complex semantic
relations such as entailment or contradiction. To address this challenge, we
propos... | Enhancing Sentence Relation Modeling with Auxiliary Character-level
Embedding | 2,016 | http://arxiv.org/pdf/1603.09405v1 | Title Enhancing Sentence Relation Modeling Auxiliary Characterlevel Embedding Summary Neural network based approach sentence relation modeling automatically generate hidden matching feature raw sentence pair However quality matching feature representation may satisfied due complex semantic relation entailment contradic... | [0.045276906341314316, 0.07869525253772736, -0.01902376301586628, 0.08849707990884781, -0.04558378458023071, -0.014144666492938995, -0.01226112898439169, 0.008166917599737644, 0.027594882994890213, -0.029279377311468124, 0.002444598823785782, -0.0108577786013484, -0.008450926281511784, 0.02788546308875084, -0.002628518... |
456 | 456 | ['Byungsoo Kim', 'Hwanjo Yu', 'Gary Geunbae Lee'] | 1605.07918v1 | Previous studies in Open Information Extraction (Open IE) are mainly based on
extraction patterns. They manually define patterns or automatically learn them
from a large corpus. However, these approaches are limited when grasping the
context of a sentence, and they fail to capture implicit relations. In this
paper, we ... | Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks
with Feedback Negative Sampling | 2,016 | http://arxiv.org/pdf/1605.07918v1 | Title Automatic Open Knowledge Acquisition via Long ShortTerm Memory Networks Feedback Negative Sampling Summary Previous study Open Information Extraction Open IE mainly based extraction pattern manually define pattern automatically learn large corpus However approach limited grasping context sentence fail capture imp... | [0.032870106399059296, 0.010768353007733822, 0.016338851302862167, 0.07687193155288696, -0.04875437170267105, -0.01448119431734085, -0.021911071613430977, 0.034561749547719955, -0.0004447177634574473, -0.07236410677433014, -0.04646538197994232, -0.0294781643897295, -0.0017716458532959223, 0.05546487495303154, -0.036198... |
457 | 457 | ['Yuanzhe Zhang', 'Kang Liu', 'Shizhu He', 'Guoliang Ji', 'Zhanyi Liu', 'Hua Wu', 'Jun Zhao'] | 1606.00979v1 | With the rapid growth of knowledge bases (KBs) on the web, how to take full
advantage of them becomes increasingly important. Knowledge base-based question
answering (KB-QA) is one of the most promising approaches to access the
substantial knowledge. Meantime, as the neural network-based (NN-based) methods
develop, NN-... | Question Answering over Knowledge Base with Neural Attention Combining
Global Knowledge Information | 2,016 | http://arxiv.org/pdf/1606.00979v1 | Title Question Answering Knowledge Base Neural Attention Combining Global Knowledge Information Summary rapid growth knowledge base KBs web take full advantage becomes increasingly important Knowledge basebased question answering KBQA one promising approach access substantial knowledge Meantime neural networkbased NNba... | [0.06426072120666504, 0.007134913932532072, -0.027330251410603523, 0.04187152162194252, 0.0013349675573408604, -0.00547182047739625, -0.014516432769596577, 0.013532851822674274, 0.0057568978518247604, -0.05016082152724266, 0.013598648831248283, -0.011826754547655582, -0.04393204674124718, 0.03993247076869011, 0.0685734... |
458 | 458 | ['Vladyslav Kolesnyk', 'Tim Rocktäschel', 'Sebastian Riedel'] | 1606.01404v1 | The ability to reason with natural language is a fundamental prerequisite for
many NLP tasks such as information extraction, machine translation and question
answering. To quantify this ability, systems are commonly tested whether they
can recognize textual entailment, i.e., whether one sentence can be inferred
from an... | Generating Natural Language Inference Chains | 2,016 | http://arxiv.org/pdf/1606.01404v1 | Title Generating Natural Language Inference Chains Summary ability reason natural language fundamental prerequisite many NLP task information extraction machine translation question answering quantify ability system commonly tested whether recognize textual entailment ie whether one sentence inferred another one Howeve... | [0.06048582121729851, 0.025336531922221184, -0.0040178438648581505, 0.04967367276549339, -0.08092920482158661, 0.008150935173034668, -0.006997069343924522, -0.012140650302171707, 0.04903362691402435, -0.028601769357919693, 0.03629592806100845, 0.10279527306556702, -0.006563949398696423, 0.031010763719677925, 0.02452779... |
459 | 459 | ['Dirk Weissenborn', 'Tim Rocktäschel'] | 1606.03002v1 | Recurrent neural networks such as the GRU and LSTM found wide adoption in
natural language processing and achieve state-of-the-art results for many
tasks. These models are characterized by a memory state that can be written to
and read from by applying gated composition operations to the current input and
the previous ... | MuFuRU: The Multi-Function Recurrent Unit | 2,016 | http://arxiv.org/pdf/1606.03002v1 | Title MuFuRU MultiFunction Recurrent Unit Summary Recurrent neural network GRU LSTM found wide adoption natural language processing achieve stateoftheart result many task model characterized memory state written read applying gated composition operation current input previous state However cover small subset potentiall... | [0.03487687185406685, 0.019420964643359184, 0.0063804457895457745, 0.012132386676967144, -0.05353996157646179, 0.0033452834468334913, 0.018502071499824524, -0.0281940009444952, 0.008634557016193867, -0.06641259044408798, 0.04120872542262077, -0.06622032076120377, 0.04509202390909195, 0.0946258157491684, 0.0180994383990... |
460 | 460 | ['Hendrik Strobelt', 'Sebastian Gehrmann', 'Hanspeter Pfister', 'Alexander M. Rush'] | 1606.07461v2 | Recurrent neural networks, and in particular long short-term memory (LSTM)
networks, are a remarkably effective tool for sequence modeling that learn a
dense black-box hidden representation of their sequential input. Researchers
interested in better understanding these models have studied the changes in
hidden state re... | LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in
Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1606.07461v2 | Title LSTMVis Tool Visual Analysis Hidden State Dynamics Recurrent Neural Networks Summary Recurrent neural network particular long shortterm memory LSTM network remarkably effective tool sequence modeling learn dense blackbox hidden representation sequential input Researchers interested better understanding model stud... | [-0.006692376919090748, -0.013991089537739754, -0.043199650943279266, 0.05151674896478653, -0.03188309445977211, -0.030941110104322433, 0.008468410931527615, 0.04561137780547142, -0.06617894768714905, 0.004464995581656694, -0.0233839713037014, 0.0016762190498411655, 0.04935676231980324, 0.045944906771183014, 0.01374636... |
461 | 461 | ['Abigail See', 'Minh-Thang Luong', 'Christopher D. Manning'] | 1606.09274v1 | Neural Machine Translation (NMT), like many other deep learning domains,
typically suffers from over-parameterization, resulting in large storage sizes.
This paper examines three simple magnitude-based pruning schemes to compress
NMT models, namely class-blind, class-uniform, and class-distribution, which
differ in ter... | Compression of Neural Machine Translation Models via Pruning | 2,016 | http://arxiv.org/pdf/1606.09274v1 | Title Compression Neural Machine Translation Models via Pruning Summary Neural Machine Translation NMT like many deep learning domain typically suffers overparameterization resulting large storage size paper examines three simple magnitudebased pruning scheme compress NMT model namely classblind classuniform classdistr... | [-0.013870018534362316, 0.03950463607907295, -0.0017748986138030887, 0.03428439423441887, -0.017992300912737846, 0.00034034220152534544, 0.008371628820896149, 0.044367920607328415, -0.0744989737868309, 0.02078443393111229, -0.024357156828045845, -0.02196624130010605, 0.0016537809278815985, -0.02828974835574627, 0.01635... |
462 | 462 | ['Janez Starc', 'Dunja Mladenić'] | 1607.06025v2 | Natural Language Inference is an important task for Natural Language
Understanding. It is concerned with classifying the logical relation between
two sentences. In this paper, we propose several text generative neural
networks for generating text hypothesis, which allows construction of new
Natural Language Inference d... | Constructing a Natural Language Inference Dataset using Generative
Neural Networks | 2,016 | http://arxiv.org/pdf/1607.06025v2 | Title Constructing Natural Language Inference Dataset using Generative Neural Networks Summary Natural Language Inference important task Natural Language Understanding concerned classifying logical relation two sentence paper propose several text generative neural network generating text hypothesis allows construction ... | [0.05523627623915672, 0.07741091400384903, 0.0064531653188169, 0.047283172607421875, -0.06524530053138733, 0.014730148017406464, 0.01783418469130993, -0.008247275836765766, 0.04907894507050514, -0.01197761856019497, -0.003073606174439192, 0.015673432499170303, 0.014606429263949394, 0.027546612545847893, 0.0313973799347... |
463 | 463 | ['Peng Li', 'Wei Li', 'Zhengyan He', 'Xuguang Wang', 'Ying Cao', 'Jie Zhou', 'Wei Xu'] | 1607.06275v2 | While question answering (QA) with neural network, i.e. neural QA, has
achieved promising results in recent years, lacking of large scale real-word QA
dataset is still a challenge for developing and evaluating neural QA system. To
alleviate this problem, we propose a large scale human annotated real-world QA
dataset We... | Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain
Factoid Question Answering | 2,016 | http://arxiv.org/pdf/1607.06275v2 | Title Dataset Neural Recurrent Sequence Labeling Model OpenDomain Factoid Question Answering Summary question answering QA neural network ie neural QA achieved promising result recent year lacking large scale realword QA dataset still challenge developing evaluating neural QA system alleviate problem propose large scal... | [0.0691339522600174, 0.0513947531580925, 0.0172710157930851, 0.021274419501423836, 0.009584801271557808, 0.012726183980703354, 0.02850259840488434, -0.006918837316334248, 0.015944670885801315, -0.03456156328320503, 0.009860329329967499, -0.01773625984787941, -0.016980916261672974, 0.0616447851061821, 0.0348480343818664... |
464 | 464 | ['Soroush Vosoughi', 'Prashanth Vijayaraghavan', 'Deb Roy'] | 1607.07514v1 | We present Tweet2Vec, a novel method for generating general-purpose vector
representation of tweets. The model learns tweet embeddings using
character-level CNN-LSTM encoder-decoder. We trained our model on 3 million,
randomly selected English-language tweets. The model was evaluated using two
methods: tweet semantic s... | Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM
Encoder-Decoder | 2,016 | http://arxiv.org/pdf/1607.07514v1 | Title Tweet2Vec Learning Tweet Embeddings Using Characterlevel CNNLSTM EncoderDecoder Summary present Tweet2Vec novel method generating generalpurpose vector representation tweet model learns tweet embeddings using characterlevel CNNLSTM encoderdecoder trained model 3 million randomly selected Englishlanguage tweet mod... | [0.04644796624779701, 0.02613394893705845, 0.01620151475071907, 0.04045052453875542, -0.0040725297294557095, 0.06053799018263817, -0.009877495467662811, 0.023585766553878784, 0.0002846387214958668, -0.023674432188272476, 0.032815784215927124, -0.017511209473013878, -0.05169404298067093, 0.03193754330277443, 0.030918573... |
465 | 465 | ['Lei Yu', 'Jan Buys', 'Phil Blunsom'] | 1609.08194v1 | We introduce an online neural sequence to sequence model that learns to
alternate between encoding and decoding segments of the input as it is read. By
independently tracking the encoding and decoding representations our algorithm
permits exact polynomial marginalization of the latent segmentation during
training, and ... | Online Segment to Segment Neural Transduction | 2,016 | http://arxiv.org/pdf/1609.08194v1 | Title Online Segment Segment Neural Transduction Summary introduce online neural sequence sequence model learns alternate encoding decoding segment input read independently tracking encoding decoding representation algorithm permit exact polynomial marginalization latent segmentation training decoding beam search emplo... | [0.014687427319586277, 0.08479885011911392, 0.022117841988801956, 0.019355658441781998, -0.021395109593868256, -0.004048784729093313, -0.001354938605800271, -0.010278400033712387, -0.08875929564237595, -0.012650947086513042, -0.002195000648498535, -0.02880951762199402, 0.031131576746702194, 0.09306752681732178, 0.03184... |
466 | 466 | ['Tomáš Kočiský', 'Gábor Melis', 'Edward Grefenstette', 'Chris Dyer', 'Wang Ling', 'Phil Blunsom', 'Karl Moritz Hermann'] | 1609.09315v1 | We present a novel semi-supervised approach for sequence transduction and
apply it to semantic parsing. The unsupervised component is based on a
generative model in which latent sentences generate the unpaired logical forms.
We apply this method to a number of semantic parsing tasks focusing on domains
with limited acc... | Semantic Parsing with Semi-Supervised Sequential Autoencoders | 2,016 | http://arxiv.org/pdf/1609.09315v1 | Title Semantic Parsing SemiSupervised Sequential Autoencoders Summary present novel semisupervised approach sequence transduction apply semantic parsing unsupervised component based generative model latent sentence generate unpaired logical form apply method number semantic parsing task focusing domain limited access l... | [0.018922995775938034, 0.0721251368522644, -0.022611845284700394, 0.04588526487350464, -0.03592238202691078, 0.013234042562544346, 0.02491137385368347, -0.060034338384866714, -0.0037507028318941593, -0.05410213768482208, 0.02314099483191967, 0.021151628345251083, -0.01956808753311634, 0.09992970526218414, 0.00460032233... |
467 | 467 | ['Lina M. Rojas Barahona', 'Milica Gasic', 'Nikola Mrkšić', 'Pei-Hao Su', 'Stefan Ultes', 'Tsung-Hsien Wen', 'Steve Young'] | 1610.04120v1 | This paper presents a deep learning architecture for the semantic decoder
component of a Statistical Spoken Dialogue System. In a slot-filling dialogue,
the semantic decoder predicts the dialogue act and a set of slot-value pairs
from a set of n-best hypotheses returned by the Automatic Speech Recognition.
Most current... | Exploiting Sentence and Context Representations in Deep Neural Models
for Spoken Language Understanding | 2,016 | http://arxiv.org/pdf/1610.04120v1 | Title Exploiting Sentence Context Representations Deep Neural Models Spoken Language Understanding Summary paper present deep learning architecture semantic decoder component Statistical Spoken Dialogue System slotfilling dialogue semantic decoder predicts dialogue act set slotvalue pair set nbest hypothesis returned A... | [0.025100350379943848, 0.030358703806996346, 0.002529171761125326, 0.06476062536239624, -0.023387886583805084, 0.012930279597640038, 0.028537053614854813, -0.02853717841207981, -0.04127072915434837, -0.09590990096330643, -0.038283973932266235, -0.009800244122743607, 0.015018795616924763, 0.04422890394926071, -0.0282749... |
468 | 468 | ['Lei Yu', 'Phil Blunsom', 'Chris Dyer', 'Edward Grefenstette', 'Tomas Kocisky'] | 1611.02554v2 | We formulate sequence to sequence transduction as a noisy channel decoding
problem and use recurrent neural networks to parameterise the source and
channel models. Unlike direct models which can suffer from explaining-away
effects during training, noisy channel models must produce outputs that explain
their inputs, and... | The Neural Noisy Channel | 2,016 | http://arxiv.org/pdf/1611.02554v2 | Title Neural Noisy Channel Summary formulate sequence sequence transduction noisy channel decoding problem use recurrent neural network parameterise source channel model Unlike direct model suffer explainingaway effect training noisy channel model must produce output explain input component model trained paired trainin... | [0.0011456308420747519, 0.06471269577741623, 0.021822137758135796, 0.01673102006316185, 0.013105972670018673, 0.012457650154829025, -0.027519145980477333, 0.00708532752469182, -0.12257658690214157, 0.004137362819164991, -0.014020216651260853, -4.6241359086707234e-05, 0.03299223631620407, 0.03300931304693222, 0.02741978... |
469 | 469 | ['Iulian Vlad Serban', 'Ryan Lowe', 'Laurent Charlin', 'Joelle Pineau'] | 1611.06216v1 | Researchers have recently started investigating deep neural networks for
dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq)
models have shown promising results for unstructured tasks, such as word-level
dialogue response generation. The hope is that such models will be able to
leverage mass... | Generative Deep Neural Networks for Dialogue: A Short Review | 2,016 | http://arxiv.org/pdf/1611.06216v1 | Title Generative Deep Neural Networks Dialogue Short Review Summary Researchers recently started investigating deep neural network dialogue application particular generative sequencetosequence Seq2Seq model shown promising result unstructured task wordlevel dialogue response generation hope model able leverage massive ... | [0.06390269100666046, 0.0733368992805481, -0.011288669891655445, 0.029294809326529503, 0.005731811746954918, -0.008158352226018906, 0.013075281865894794, -0.015365160070359707, -0.004767810460180044, -0.02165645733475685, 0.020351719111204147, -0.03490282595157623, 0.005236675031483173, 0.08828261494636536, 0.004751257... |
470 | 470 | ['Avishkar Bhoopchand', 'Tim Rocktäschel', 'Earl Barr', 'Sebastian Riedel'] | 1611.08307v1 | To enhance developer productivity, all modern integrated development
environments (IDEs) include code suggestion functionality that proposes likely
next tokens at the cursor. While current IDEs work well for statically-typed
languages, their reliance on type annotations means that they do not provide
the same level of ... | Learning Python Code Suggestion with a Sparse Pointer Network | 2,016 | http://arxiv.org/pdf/1611.08307v1 | Title Learning Python Code Suggestion Sparse Pointer Network Summary enhance developer productivity modern integrated development environment IDEs include code suggestion functionality proposes likely next token cursor current IDEs work well staticallytyped language reliance type annotation mean provide level support d... | [0.04339522495865822, 0.024847663938999176, -0.014519850723445415, 0.019994234666228294, -0.00523100420832634, -0.015087258070707321, -0.006111054215580225, 0.03958819806575775, 0.0005323246587067842, -0.01784777268767357, -0.015427467413246632, 0.033090364187955856, 0.0035767473746091127, 0.05823474004864693, 0.026940... |
471 | 471 | ['Guillaume Klein', 'Yoon Kim', 'Yuntian Deng', 'Jean Senellart', 'Alexander M. Rush'] | 1701.02810v2 | We describe an open-source toolkit for neural machine translation (NMT). The
toolkit prioritizes efficiency, modularity, and extensibility with the goal of
supporting NMT research into model architectures, feature representations, and
source modalities, while maintaining competitive performance and reasonable
training ... | OpenNMT: Open-Source Toolkit for Neural Machine Translation | 2,017 | http://arxiv.org/pdf/1701.02810v2 | Title OpenNMT OpenSource Toolkit Neural Machine Translation Summary describe opensource toolkit neural machine translation NMT toolkit prioritizes efficiency modularity extensibility goal supporting NMT research model architecture feature representation source modality maintaining competitive performance reasonable tra... | [0.012157713994383812, 0.026993492618203163, -0.00046563352225348353, 0.02612263523042202, -0.035118117928504944, 0.012179067358374596, 0.02470211684703827, 0.02371455542743206, -0.05222651734948158, -0.014534087851643562, -0.015913058072328568, -0.021140730008482933, 0.040106549859046936, 0.0255400612950325, 0.0481840... |
472 | 472 | ['Dirk Weissenborn', 'Georg Wiese', 'Laura Seiffe'] | 1703.04816v3 | Recent development of large-scale question answering (QA) datasets triggered
a substantial amount of research into end-to-end neural architectures for QA.
Increasingly complex systems have been conceived without comparison to simpler
neural baseline systems that would justify their complexity. In this work, we
propose ... | Making Neural QA as Simple as Possible but not Simpler | 2,017 | http://arxiv.org/pdf/1703.04816v3 | Title Making Neural QA Simple Possible Simpler Summary Recent development largescale question answering QA datasets triggered substantial amount research endtoend neural architecture QA Increasingly complex system conceived without comparison simpler neural baseline system would justify complexity work propose simple h... | [0.06591969728469849, 0.029870105907320976, -0.015024044550955296, 0.04606082662940025, 0.0002484614960849285, 0.008769730105996132, -0.015431920997798443, -0.008799200877547264, -0.0110649224370718, -0.033688630908727646, -0.002634844509884715, -0.02954694628715515, -0.02410193532705307, 0.06254766881465912, 0.0399308... |
473 | 473 | ['Albert Gatt', 'Emiel Krahmer'] | 1703.09902v4 | This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) ... | Survey of the State of the Art in Natural Language Generation: Core
tasks, applications and evaluation | 2,017 | http://arxiv.org/pdf/1703.09902v4 | Title Survey State Art Natural Language Generation Core task application evaluation Summary paper survey current state art Natural Language Generation NLG defined task generating text speech nonlinguistic input survey NLG timely view change field undergone past decade especially relation new usually datadriven method w... | [0.06841406226158142, 0.0017079530516639352, 0.0012855371460318565, 0.028355872258543968, -0.041360627859830856, -0.03688322380185127, 0.03379391133785248, -0.003396128537133336, 0.00891396775841713, -0.057099319994449615, 0.025376342236995697, -0.010400498285889626, 0.01604187861084938, 0.04415862634778023, 0.00330479... |
474 | 474 | ['Yaoyuan Zhang', 'Zhenxu Ye', 'Yansong Feng', 'Dongyan Zhao', 'Rui Yan'] | 1704.02312v1 | Sentence simplification reduces semantic complexity to benefit people with
language impairments. Previous simplification studies on the sentence level and
word level have achieved promising results but also meet great challenges. For
sentence-level studies, sentences after simplification are fluent but sometimes
are no... | A Constrained Sequence-to-Sequence Neural Model for Sentence
Simplification | 2,017 | http://arxiv.org/pdf/1704.02312v1 | Title Constrained SequencetoSequence Neural Model Sentence Simplification Summary Sentence simplification reduces semantic complexity benefit people language impairment Previous simplification study sentence level word level achieved promising result also meet great challenge sentencelevel study sentence simplification... | [0.037985920906066895, 0.08970826864242554, -0.02927764318883419, 0.006693972274661064, -0.04820210114121437, -0.029439451172947884, 0.030323663726449013, 0.007917067036032677, -0.03963814303278923, -0.009375032037496567, 0.036269597709178925, 0.005774978548288345, 0.03666891157627106, 0.013577942736446857, 0.043408662... |
475 | 475 | ['Mo Yu', 'Wenpeng Yin', 'Kazi Saidul Hasan', 'Cicero dos Santos', 'Bing Xiang', 'Bowen Zhou'] | 1704.06194v2 | Relation detection is a core component for many NLP applications including
Knowledge Base Question Answering (KBQA). In this paper, we propose a
hierarchical recurrent neural network enhanced by residual learning that
detects KB relations given an input question. Our method uses deep residual
bidirectional LSTMs to com... | Improved Neural Relation Detection for Knowledge Base Question Answering | 2,017 | http://arxiv.org/pdf/1704.06194v2 | Title Improved Neural Relation Detection Knowledge Base Question Answering Summary Relation detection core component many NLP application including Knowledge Base Question Answering KBQA paper propose hierarchical recurrent neural network enhanced residual learning detects KB relation given input question method us dee... | [0.06825186312198639, 0.008547118864953518, 0.010991828516125679, 0.10218099504709244, -0.004965547937899828, 0.02915715053677559, -0.0367828905582428, 0.04724039137363434, 0.02808644063770771, -0.043460968881845474, -0.006258853245526552, -0.00458939652889967, -0.03285800665616989, 0.0028230154421180487, 0.00364264682... |
476 | 476 | ['Edwin Simonnet', 'Sahar Ghannay', 'Nathalie Camelin', 'Yannick Estève', 'Renato De Mori'] | 1705.09515v1 | This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic inform... | ASR error management for improving spoken language understanding | 2,017 | http://arxiv.org/pdf/1705.09515v1 | Title ASR error management improving spoken language understanding Summary paper address problem automatic speech recognition ASR error detection use improving spoken language understanding SLU system study SLU task consists automatically extracting ASR transcription semantic concept conceptvalues pair eg touristic inf... | [0.03429742529988289, 0.023915894329547882, 0.03340352326631546, 0.06692226231098175, 0.042175084352493286, 0.008284401148557663, -0.022708090022206306, 0.011733021587133408, -0.04916686564683914, -0.07305515557527542, -0.026697484776377678, -0.026233786717057228, 0.041347064077854156, 0.022614168003201485, 0.002321220... |
477 | 477 | ['Dirk Weissenborn', 'Tomáš Kočiský', 'Chris Dyer'] | 1706.02596v2 | Common-sense or background knowledge is required to understand natural
language, but in most neural natural language understanding (NLU) systems, the
requisite background knowledge is indirectly acquired from static corpora. We
develop a new reading architecture for the dynamic integration of explicit
background knowle... | Dynamic Integration of Background Knowledge in Neural NLU Systems | 2,017 | http://arxiv.org/pdf/1706.02596v2 | Title Dynamic Integration Background Knowledge Neural NLU Systems Summary Commonsense background knowledge required understand natural language neural natural language understanding NLU system requisite background knowledge indirectly acquired static corpus develop new reading architecture dynamic integration explicit ... | [0.0630718320608139, -0.016112931072711945, -0.01979481801390648, 0.012163262814283371, -0.04035535082221031, 0.03136575594544411, 0.002129240892827511, -0.05056740343570709, 0.04119458422064781, -0.09115668386220932, 0.030752604827284813, 0.025859611108899117, 0.005785737186670303, 0.09818432480096817, 0.0120423212647... |
478 | 478 | ['Shuai Tang', 'Hailin Jin', 'Chen Fang', 'Zhaowen Wang', 'Virginia R. de Sa'] | 1706.03146v1 | We study the skip-thought model with neighborhood information as weak
supervision. More specifically, we propose a skip-thought neighbor model to
consider the adjacent sentences as a neighborhood. We train our skip-thought
neighbor model on a large corpus with continuous sentences, and then evaluate
the trained model o... | Rethinking Skip-thought: A Neighborhood based Approach | 2,017 | http://arxiv.org/pdf/1706.03146v1 | Title Rethinking Skipthought Neighborhood based Approach Summary study skipthought model neighborhood information weak supervision specifically propose skipthought neighbor model consider adjacent sentence neighborhood train skipthought neighbor model large corpus continuous sentence evaluate trained model 7 task inclu... | [0.07042957842350006, 0.026709692552685738, -0.023683154955506325, 0.05327232927083969, -0.08143114298582077, 0.015316244214773178, 0.005118263885378838, 0.02174159325659275, -0.007665194105356932, -0.0659298449754715, -0.010834223590791225, 0.05457272380590439, -0.03386347368359566, -0.02613106369972229, -0.0139488968... |
479 | 479 | ['Georg Wiese', 'Dirk Weissenborn', 'Mariana Neves'] | 1706.03610v2 | Factoid question answering (QA) has recently benefited from the development
of deep learning (DL) systems. Neural network models outperform traditional
approaches in domains where large datasets exist, such as SQuAD (ca. 100,000
questions) for Wikipedia articles. However, these systems have not yet been
applied to QA i... | Neural Domain Adaptation for Biomedical Question Answering | 2,017 | http://arxiv.org/pdf/1706.03610v2 | Title Neural Domain Adaptation Biomedical Question Answering Summary Factoid question answering QA recently benefited development deep learning DL system Neural network model outperform traditional approach domain large datasets exist SQuAD ca 100000 question Wikipedia article However system yet applied QA specific dom... | [0.04262051731348038, 0.05837785452604294, -0.011497436091303825, -0.008565559983253479, 0.009716584347188473, 0.011876585893332958, 0.03312915936112404, 0.03677397593855858, -0.02092050202190876, -0.03381045162677765, 0.014537196606397629, 0.011613793671131134, -0.026118028908967972, 0.0651947557926178, 0.024646587669... |
480 | 480 | ['Sandeep Subramanian', 'Tong Wang', 'Xingdi Yuan', 'Saizheng Zhang', 'Adam Trischler', 'Yoshua Bengio'] | 1706.04560v2 | We propose a two-stage neural model to tackle question generation from
documents. Our model first estimates the probability that word sequences in a
document compose "interesting" answers using a neural model trained on a
question-answering corpus. We thus take a data-driven approach to
interestingness. Predicted key p... | Neural Models for Key Phrase Detection and Question Generation | 2,017 | http://arxiv.org/pdf/1706.04560v2 | Title Neural Models Key Phrase Detection Question Generation Summary propose twostage neural model tackle question generation document model first estimate probability word sequence document compose interesting answer using neural model trained questionanswering corpus thus take datadriven approach interestingness Pred... | [0.08843544125556946, -0.004547679331153631, 0.000339330465067178, 0.03676971048116684, -0.02507578767836094, 0.02954575978219509, -0.02305273525416851, 0.019697800278663635, 0.019017862156033516, -0.03619661554694176, 0.01438960526138544, -0.011492978781461716, -0.049285974353551865, 0.07849238067865372, 0.03141279518... |
481 | 481 | ['Georg Wiese', 'Dirk Weissenborn', 'Mariana Neves'] | 1706.08568v1 | This paper describes our submission to the 2017 BioASQ challenge. We
participated in Task B, Phase B which is concerned with biomedical question
answering (QA). We focus on factoid and list question, using an extractive QA
model, that is, we restrict our system to output substrings of the provided
text snippets. At the... | Neural Question Answering at BioASQ 5B | 2,017 | http://arxiv.org/pdf/1706.08568v1 | Title Neural Question Answering BioASQ 5B Summary paper describes submission 2017 BioASQ challenge participated Task B Phase B concerned biomedical question answering QA focus factoid list question using extractive QA model restrict system output substring provided text snippet core system use FastQA stateoftheart neur... | [0.06912880390882492, 0.03574235364794731, 0.018872641026973724, 0.007223246153444052, -0.0027934906538575888, 0.009961670264601707, 0.0015119727468118072, 0.036029551178216934, 0.0037093721330165863, -0.004548780154436827, -0.003829070134088397, -0.011537163518369198, -0.01790100336074829, 0.030550070106983185, 0.0534... |
482 | 482 | ['Hongyu Guo'] | 1707.01555v1 | While natural languages are compositional, how state-of-the-art neural models
achieve compositionality is still unclear. We propose a deep network, which not
only achieves competitive accuracy for text classification, but also exhibits
compositional behavior. That is, while creating hierarchical representations of
a pi... | A Deep Network with Visual Text Composition Behavior | 2,017 | http://arxiv.org/pdf/1707.01555v1 | Title Deep Network Visual Text Composition Behavior Summary natural language compositional stateoftheart neural model achieve compositionality still unclear propose deep network achieves competitive accuracy text classification also exhibit compositional behavior creating hierarchical representation piece text sentence... | [0.03845909610390663, 0.029409339651465416, -0.015025964938104153, 0.031386107206344604, -0.046831391751766205, 0.0086111044511199, 0.004960916005074978, 0.0012206637766212225, -0.04155999794602394, -0.017742177471518517, 0.003201203653588891, -0.0288506131619215, 0.044291768223047256, 0.058290015906095505, 0.032687511... |
483 | 483 | ['Mario Giulianelli'] | 1708.03910v1 | There exist two main approaches to automatically extract affective
orientation: lexicon-based and corpus-based. In this work, we argue that these
two methods are compatible and show that combining them can improve the
accuracy of emotion classifiers. In particular, we introduce a novel variant of
the Label Propagation ... | Semi-supervised emotion lexicon expansion with label propagation and
specialized word embeddings | 2,017 | http://arxiv.org/pdf/1708.03910v1 | Title Semisupervised emotion lexicon expansion label propagation specialized word embeddings Summary exist two main approach automatically extract affective orientation lexiconbased corpusbased work argue two method compatible show combining improve accuracy emotion classifier particular introduce novel variant Label P... | [0.02880338951945305, 0.05068530887365341, 0.008375333622097969, 0.07251240313053131, -0.009983974508941174, 0.02534208446741104, -0.035031095147132874, -0.03878992050886154, 0.03784232586622238, -0.05756577104330063, -0.045940157026052475, 0.020680682733654976, -0.05398101359605789, 0.009172444231808186, -0.0398169606... |
484 | 484 | ['Elahe Rahimtoroghi', 'Jiaqi Wu', 'Ruimin Wang', 'Pranav Anand', 'Marilyn A Walker'] | 1708.09040v1 | Many genres of natural language text are narratively structured, a testament
to our predilection for organizing our experiences as narratives. There is
broad consensus that understanding a narrative requires identifying and
tracking the goals and desires of the characters and their narrative outcomes.
However, to date,... | Modelling Protagonist Goals and Desires in First-Person Narrative | 2,017 | http://arxiv.org/pdf/1708.09040v1 | Title Modelling Protagonist Goals Desires FirstPerson Narrative Summary Many genre natural language text narratively structured testament predilection organizing experience narrative broad consensus understanding narrative requires identifying tracking goal desire character narrative outcome However date limited work c... | [0.057369738817214966, 0.05930757895112038, -0.02542848140001297, 0.02270907163619995, -0.05294479429721832, 0.004344637040048838, 0.005316389724612236, -0.02398129366338253, 0.0035211159847676754, -0.0430864617228508, 0.053379856050014496, -0.04525022953748703, 0.035953864455223083, 0.06503397971391678, -0.04321665316... |
485 | 485 | ['Felix Hill', 'Karl Moritz Hermann', 'Phil Blunsom', 'Stephen Clark'] | 1710.09867v1 | Neural network-based systems can now learn to locate the referents of words
and phrases in images, answer questions about visual scenes, and even execute
symbolic instructions as first-person actors in partially-observable worlds. To
achieve this so-called grounded language learning, models must overcome certain
well-s... | Understanding Grounded Language Learning Agents | 2,017 | http://arxiv.org/pdf/1710.09867v1 | Title Understanding Grounded Language Learning Agents Summary Neural networkbased system learn locate referent word phrase image answer question visual scene even execute symbolic instruction firstperson actor partiallyobservable world achieve socalled grounded language learning model must overcome certain wellstudied ... | [0.039170317351818085, -0.01116965338587761, -0.02970067784190178, 0.024066317826509476, -0.014153378084301949, 0.00025646082940511405, -0.009390239603817463, -0.029126809909939766, -0.04228420928120613, -0.058828309178352356, -0.010626576840877533, 0.03133980184793472, 0.023358115926384926, 0.06796994805335999, 0.0125... |
486 | 486 | ['Anjishnu Kumar', 'Arpit Gupta', 'Julian Chan', 'Sam Tucker', 'Bjorn Hoffmeister', 'Markus Dreyer', 'Stanislav Peshterliev', 'Ankur Gandhe', 'Denis Filiminov', 'Ariya Rastrow', 'Christian Monson', 'Agnika Kumar'] | 1711.00549v4 | This paper presents the design of the machine learning architecture that
underlies the Alexa Skills Kit (ASK) a large scale Spoken Language
Understanding (SLU) Software Development Kit (SDK) that enables developers to
extend the capabilities of Amazon's virtual assistant, Alexa. At Amazon, the
infrastructure powers ove... | Just ASK: Building an Architecture for Extensible Self-Service Spoken
Language Understanding | 2,017 | http://arxiv.org/pdf/1711.00549v4 | Title ASK Building Architecture Extensible SelfService Spoken Language Understanding Summary paper present design machine learning architecture underlies Alexa Skills Kit ASK large scale Spoken Language Understanding SLU Software Development Kit SDK enables developer extend capability Amazons virtual assistant Alexa Am... | [0.049611739814281464, 0.004777115304023027, -0.041534118354320526, 0.024687061086297035, 0.0015271311858668923, -0.010411644354462624, 0.041570961475372314, -0.04209189862012863, 0.027954082936048508, -0.04792386293411255, 0.006490337662398815, 0.011914246715605259, -0.004317923448979855, 0.059516340494155884, 0.00837... |
487 | 487 | ['Tomáš Kočiský', 'Jonathan Schwarz', 'Phil Blunsom', 'Chris Dyer', 'Karl Moritz Hermann', 'Gábor Melis', 'Edward Grefenstette'] | 1712.07040v1 | Reading comprehension (RC)---in contrast to information retrieval---requires
integrating information and reasoning about events, entities, and their
relations across a full document. Question answering is conventionally used to
assess RC ability, in both artificial agents and children learning to read.
However, existin... | The NarrativeQA Reading Comprehension Challenge | 2,017 | http://arxiv.org/pdf/1712.07040v1 | Title NarrativeQA Reading Comprehension Challenge Summary Reading comprehension RCin contrast information retrievalrequires integrating information reasoning event entity relation across full document Question answering conventionally used ass RC ability artificial agent child learning read However existing RC datasets... | [0.0365140326321125, 0.02348371408879757, -0.015957601368427277, 0.04249544069170952, -0.04983700439333916, 0.03662506863474846, 0.02855737879872322, -0.0033085576724261045, -0.004678906407207251, -0.010802393779158592, 0.0009602278587408364, 0.002951742149889469, 0.01648826152086258, 0.07350882887840271, -0.0335029475... |
488 | 488 | ['Rajesh Bordawekar', 'Bortik Bandyopadhyay', 'Oded Shmueli'] | 1712.07199v1 | We propose Cognitive Databases, an approach for transparently enabling
Artificial Intelligence (AI) capabilities in relational databases. A novel
aspect of our design is to first view the structured data source as meaningful
unstructured text, and then use the text to build an unsupervised neural
network model using a ... | Cognitive Database: A Step towards Endowing Relational Databases with
Artificial Intelligence Capabilities | 2,017 | http://arxiv.org/pdf/1712.07199v1 | Title Cognitive Database Step towards Endowing Relational Databases Artificial Intelligence Capabilities Summary propose Cognitive Databases approach transparently enabling Artificial Intelligence AI capability relational database novel aspect design first view structured data source meaningful unstructured text use te... | [0.02193552628159523, 0.0656096488237381, -0.026182014495134354, 0.01106394175440073, -0.025526393204927444, 0.019003618508577347, -0.006873030215501785, 0.016169670969247818, 0.07242733240127563, -0.02251298353075981, 0.01099012978374958, 0.023311303928494453, -0.032131098210811615, 0.07732986658811569, 0.032609082758... |
489 | 489 | ['Iñigo Casanueva', 'Paweł Budzianowski', 'Pei-Hao Su', 'Stefan Ultes', 'Lina Rojas-Barahona', 'Bo-Hsiang Tseng', 'Milica Gašić'] | 1803.03232v1 | Reinforcement learning (RL) is a promising approach to solve dialogue policy
optimisation. Traditional RL algorithms, however, fail to scale to large
domains due to the curse of dimensionality. We propose a novel Dialogue
Management architecture, based on Feudal RL, which decomposes the decision into
two steps; a first... | Feudal Reinforcement Learning for Dialogue Management in Large Domains | 2,018 | http://arxiv.org/pdf/1803.03232v1 | Title Feudal Reinforcement Learning Dialogue Management Large Domains Summary Reinforcement learning RL promising approach solve dialogue policy optimisation Traditional RL algorithm however fail scale large domain due curse dimensionality propose novel Dialogue Management architecture based Feudal RL decomposes decisi... | [0.06175444647669792, 0.07851953059434891, -0.017721027135849, 0.0245374646037817, -0.022221723571419716, -0.0063163479790091515, 0.02900431491434574, -0.017272096127271652, -0.010333647951483727, -0.024914268404245377, -0.043090615421533585, -0.013307959772646427, -0.036684438586235046, 0.05240204185247421, -0.0522249... |
490 | 490 | ['Stephen Merity', 'Nitish Shirish Keskar', 'Richard Socher'] | 1803.08240v1 | Many of the leading approaches in language modeling introduce novel, complex
and specialized architectures. We take existing state-of-the-art word level
language models based on LSTMs and QRNNs and extend them to both larger
vocabularies as well as character-level granularity. When properly tuned, LSTMs
and QRNNs achie... | An Analysis of Neural Language Modeling at Multiple Scales | 2,018 | http://arxiv.org/pdf/1803.08240v1 | Title Analysis Neural Language Modeling Multiple Scales Summary Many leading approach language modeling introduce novel complex specialized architecture take existing stateoftheart word level language model based LSTMs QRNNs extend larger vocabulary well characterlevel granularity properly tuned LSTMs QRNNs achieve sta... | [0.029181942343711853, 0.03138417750597, -0.012979317456483841, 0.04047025367617607, -0.006074283737689257, 0.014389488846063614, 0.03928348794579506, 0.02027115412056446, -0.018421638756990433, -0.015293341130018234, -0.03649844974279404, -0.08334321528673172, 0.03409227356314659, 0.0900942012667656, 0.012963213957846... |
491 | 491 | ['Andreas Schwarz', 'Christian Huemmer', 'Roland Maas', 'Walter Kellermann'] | 1410.2479v2 | We propose a spatial diffuseness feature for deep neural network (DNN)-based
automatic speech recognition to improve recognition accuracy in reverberant and
noisy environments. The feature is computed in real-time from multiple
microphone signals without requiring knowledge or estimation of the direction
of arrival, an... | Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy
and Reverberant Environments | 2,014 | http://arxiv.org/pdf/1410.2479v2 | Title Spatial Diffuseness Features DNNBased Speech Recognition Noisy Reverberant Environments Summary propose spatial diffuseness feature deep neural network DNNbased automatic speech recognition improve recognition accuracy reverberant noisy environment feature computed realtime multiple microphone signal without requ... | [-0.03159436956048012, 0.041587281972169876, 0.01891152933239937, 0.022850507870316505, -0.021994175389409065, -0.01445208303630352, 0.03328992426395416, -0.02419726923108101, -0.05811168625950813, 0.009771770797669888, -0.06108737364411354, 0.005841633304953575, 0.042951326817274094, -0.03019733354449272, 0.0137249864... |
492 | 492 | ['Yoon Kim', 'Yacine Jernite', 'David Sontag', 'Alexander M. Rush'] | 1508.06615v4 | We describe a simple neural language model that relies only on
character-level inputs. Predictions are still made at the word-level. Our model
employs a convolutional neural network (CNN) and a highway network over
characters, whose output is given to a long short-term memory (LSTM) recurrent
neural network language mo... | Character-Aware Neural Language Models | 2,015 | http://arxiv.org/pdf/1508.06615v4 | Title CharacterAware Neural Language Models Summary describe simple neural language model relies characterlevel input Predictions still made wordlevel model employ convolutional neural network CNN highway network character whose output given long shortterm memory LSTM recurrent neural network language model RNNLM Engli... | [0.05119018629193306, 0.09103494882583618, -0.0036308716516941786, 0.0491948276758194, -0.012303113006055355, 0.0032413697335869074, 0.03976443409919739, 0.03735227882862091, -0.06278343498706818, -0.028260039165616035, -0.0029754447750747204, -0.10466524213552475, 0.058151911944150925, 0.05166538804769516, 0.058905340... |
493 | 493 | ['Krzysztof Wołk', 'Krzysztof Marasek'] | 1509.08644v1 | The quality of machine translation is rapidly evolving. Today one can find
several machine translation systems on the web that provide reasonable
translations, although the systems are not perfect. In some specific domains,
the quality may decrease. A recently proposed approach to this domain is neural
machine translat... | Neural-based machine translation for medical text domain. Based on
European Medicines Agency leaflet texts | 2,015 | http://arxiv.org/pdf/1509.08644v1 | Title Neuralbased machine translation medical text domain Based European Medicines Agency leaflet text Summary quality machine translation rapidly evolving Today one find several machine translation system web provide reasonable translation although system perfect specific domain quality may decrease recently proposed ... | [0.04181399196386337, -0.018864141777157784, 0.005011287517845631, 0.016684800386428833, -0.025218678638339043, 0.023490937426686287, 0.034301429986953735, 0.0345165841281414, -0.03873180225491524, -0.013261262327432632, 0.016608713194727898, -0.04808464273810387, 0.05090376362204552, 0.018950333818793297, 0.0345067940... |
494 | 494 | ['Tsung-Hsien Wen', 'Milica Gasic', 'Nikola Mrksic', 'Lina M. Rojas-Barahona', 'Pei-Hao Su', 'Stefan Ultes', 'David Vandyke', 'Steve Young'] | 1606.03352v1 | Recently a variety of LSTM-based conditional language models (LM) have been
applied across a range of language generation tasks. In this work we study
various model architectures and different ways to represent and aggregate the
source information in an end-to-end neural dialogue system framework. A method
called snaps... | Conditional Generation and Snapshot Learning in Neural Dialogue Systems | 2,016 | http://arxiv.org/pdf/1606.03352v1 | Title Conditional Generation Snapshot Learning Neural Dialogue Systems Summary Recently variety LSTMbased conditional language model LM applied across range language generation task work study various model architecture different way represent aggregate source information endtoend neural dialogue system framework metho... | [0.04596489667892456, 0.041274070739746094, 0.00603374931961298, 0.029419537633657455, -0.02946520410478115, -0.009778846055269241, 0.04388587176799774, 0.012670128606259823, 0.017349235713481903, -0.04972049221396446, 0.004751638974994421, -0.061895038932561874, -0.008012899197638035, 0.05074413865804672, 0.0026315236... |
495 | 495 | ['Julien Perez', 'Fei Liu'] | 1606.04052v5 | In an end-to-end dialog system, the aim of dialog state tracking is to
accurately estimate a compact representation of the current dialog status from
a sequence of noisy observations produced by the speech recognition and the
natural language understanding modules. This paper introduces a novel method of
dialog state t... | Dialog state tracking, a machine reading approach using Memory Network | 2,016 | http://arxiv.org/pdf/1606.04052v5 | Title Dialog state tracking machine reading approach using Memory Network Summary endtoend dialog system aim dialog state tracking accurately estimate compact representation current dialog status sequence noisy observation produced speech recognition natural language understanding module paper introduces novel method d... | [0.01852702721953392, 0.059703826904296875, -0.005396231543272734, 0.07672544568777084, -0.0025937859900295734, 0.010729890316724777, -0.011404688470065594, -0.0321563258767128, 0.0547000952064991, -0.051784448325634, 0.01922069676220417, -0.050492946058511734, -0.020785711705684662, 0.10536794364452362, -0.00875034835... |
496 | 496 | ['Sándor Darányi', 'Peter Wittek', 'Konstantinos Konstantinidis', 'Symeon Papadopoulos', 'Efstratios Kontopoulos'] | 1608.01298v1 | In accessibility tests for digital preservation, over time we experience
drifts of localized and labelled content in statistical models of evolving
semantics represented as a vector field. This articulates the need to detect,
measure, interpret and model outcomes of knowledge dynamics. To this end we
employ a high-perf... | A Physical Metaphor to Study Semantic Drift | 2,016 | http://arxiv.org/pdf/1608.01298v1 | Title Physical Metaphor Study Semantic Drift Summary accessibility test digital preservation time experience drift localized labelled content statistical model evolving semantics represented vector field articulates need detect measure interpret model outcome knowledge dynamic end employ highperformance machine learnin... | [0.051582202315330505, 0.01668226160109043, -0.030742881819605827, 0.01801236905157566, -0.024954671040177345, -0.012800347059965134, 0.025951949879527092, 0.013821380212903023, -0.045094944536685944, -0.032826557755470276, 0.028548436239361763, 0.020404718816280365, 0.018836017698049545, 0.004220754373818636, 0.009514... |
497 | 497 | ['Franck Dernoncourt', 'Ji Young Lee'] | 1609.08703v1 | Systems based on artificial neural networks (ANNs) have achieved
state-of-the-art results in many natural language processing tasks. Although
ANNs do not require manually engineered features, ANNs have many
hyperparameters to be optimized. The choice of hyperparameters significantly
impacts models' performances. Howeve... | Optimizing Neural Network Hyperparameters with Gaussian Processes for
Dialog Act Classification | 2,016 | http://arxiv.org/pdf/1609.08703v1 | Title Optimizing Neural Network Hyperparameters Gaussian Processes Dialog Act Classification Summary Systems based artificial neural network ANNs achieved stateoftheart result many natural language processing task Although ANNs require manually engineered feature ANNs many hyperparameters optimized choice hyperparamete... | [0.027815915644168854, 0.043047185987234116, -0.011858375743031502, 0.0192045196890831, -0.01971953921020031, 0.0035111058969050646, 0.01757357455790043, 0.0236978679895401, 0.011024895124137402, -0.06809913367033005, -0.024109693244099617, 0.021360300481319427, 0.027190634980797768, 0.07266440242528915, -0.01505499333... |
498 | 498 | ['Hans Krupakar', 'Keerthika Rajvel', 'Bharathi B', 'Angel Deborah S', 'Vallidevi Krishnamurthy'] | 1610.03934v1 | Speech Translation has always been about giving source text or audio input
and waiting for system to give translated output in desired form. In this
paper, we present the Acoustic Dialect Decoder (ADD) - a voice to voice
ear-piece translation device. We introduce and survey the recent advances made
in the field of Spee... | A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder | 2,016 | http://arxiv.org/pdf/1610.03934v1 | Title Survey Voice Translation Methodologies Acoustic Dialect Decoder Summary Speech Translation always giving source text audio input waiting system give translated output desired form paper present Acoustic Dialect Decoder ADD voice voice earpiece translation device introduce survey recent advance made field Speech E... | [0.02819700725376606, -0.0026545983273535967, 0.007242600433528423, 0.019687477499246597, -0.040881067514419556, -0.018122613430023193, 0.034728530794382095, 0.007429505232721567, -0.03262017294764519, -0.02146136946976185, -0.03796542435884476, -0.008969255723059177, 0.08321838080883026, 0.0006240934017114341, 0.00728... |
499 | 499 | ['Mark Neumann', 'Pontus Stenetorp', 'Sebastian Riedel'] | 1610.07647v2 | Multi-hop inference is necessary for machine learning systems to successfully
solve tasks such as Recognising Textual Entailment and Machine Reading. In this
work, we demonstrate the effectiveness of adaptive computation for learning the
number of inference steps required for examples of different complexity and
that l... | Learning to Reason With Adaptive Computation | 2,016 | http://arxiv.org/pdf/1610.07647v2 | Title Learning Reason Adaptive Computation Summary Multihop inference necessary machine learning system successfully solve task Recognising Textual Entailment Machine Reading work demonstrate effectiveness adaptive computation learning number inference step required example different complexity learning correct number ... | [0.04260840639472008, 0.00564073771238327, -0.030513713136315346, 0.040240053087472916, -0.06597141921520233, 0.028324350714683533, 0.013550831936299801, 0.02068973146378994, 0.01817084476351738, -0.041810546070337296, 0.07300622016191483, 0.061497610062360764, 0.02069713920354843, 0.046717461198568344, -0.010532931424... |
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