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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0 | 0 | ['Ahmed Osman', 'Wojciech Samek'] | 1802.00209v1 | We propose an architecture for VQA which utilizes recurrent layers to
generate visual and textual attention. The memory characteristic of the
proposed recurrent attention units offers a rich joint embedding of visual and
textual features and enables the model to reason relations between several
parts of the image and q... | Dual Recurrent Attention Units for Visual Question Answering | 2,018 | http://arxiv.org/pdf/1802.00209v1 | Title Dual Recurrent Attention Units Visual Question Answering Summary propose architecture VQA utilizes recurrent layer generate visual textual attention memory characteristic proposed recurrent attention unit offer rich joint embedding visual textual feature enables model reason relation several part image question s... | [0.030238820239901543, 0.016444332897663116, -0.016811851412057877, 0.06810687482357025, 0.0009400892304256558, 0.01178388949483633, -0.0018757805228233337, -0.009158705361187458, 0.002846865216270089, -0.038819942623376846, 0.0050158933736383915, -0.03350161761045456, -0.021443059667944908, 0.050835151225328445, 0.053... |
1 | 1 | ['Ji Young Lee', 'Franck Dernoncourt'] | 1603.03827v1 | Recent approaches based on artificial neural networks (ANNs) have shown
promising results for short-text classification. However, many short texts
occur in sequences (e.g., sentences in a document or utterances in a dialog),
and most existing ANN-based systems do not leverage the preceding short texts
when classifying ... | Sequential Short-Text Classification with Recurrent and Convolutional
Neural Networks | 2,016 | http://arxiv.org/pdf/1603.03827v1 | Title Sequential ShortText Classification Recurrent Convolutional Neural Networks Summary Recent approach based artificial neural network ANNs shown promising result shorttext classification However many short text occur sequence eg sentence document utterance dialog existing ANNbased system leverage preceding short te... | [0.040623173117637634, 0.010163335129618645, 0.0038399931509047747, 0.06766032427549362, -0.01700439304113388, -0.003362833522260189, 0.025043299421668053, 0.020645055919885635, 0.03404254466295242, -0.081425741314888, -0.029075222089886665, -0.04023962467908859, 0.01441923063248396, 0.06081303581595421, 0.004792141262... |
2 | 2 | ['Iulian Vlad Serban', 'Tim Klinger', 'Gerald Tesauro', 'Kartik Talamadupula', 'Bowen Zhou', 'Yoshua Bengio', 'Aaron Courville'] | 1606.00776v2 | We introduce the multiresolution recurrent neural network, which extends the
sequence-to-sequence framework to model natural language generation as two
parallel discrete stochastic processes: a sequence of high-level coarse tokens,
and a sequence of natural language tokens. There are many ways to estimate or
learn the ... | Multiresolution Recurrent Neural Networks: An Application to Dialogue
Response Generation | 2,016 | http://arxiv.org/pdf/1606.00776v2 | Title Multiresolution Recurrent Neural Networks Application Dialogue Response Generation Summary introduce multiresolution recurrent neural network extends sequencetosequence framework model natural language generation two parallel discrete stochastic process sequence highlevel coarse token sequence natural language to... | [0.07278219610452652, 0.03251509740948677, -0.0008744823280721903, 0.02868502028286457, -0.0180113073438406, -0.007289289031177759, -0.0035347219090908766, -0.01468235719949007, -0.006232827436178923, -0.07925242930650711, 0.02225460298359394, -0.025085624307394028, 0.0075123305432498455, 0.07406775653362274, -0.001531... |
3 | 3 | ['Sebastian Ruder', 'Joachim Bingel', 'Isabelle Augenstein', 'Anders Søgaard'] | 1705.08142v2 | Multi-task learning is motivated by the observation that humans bring to bear
what they know about related problems when solving new ones. Similarly, deep
neural networks can profit from related tasks by sharing parameters with other
networks. However, humans do not consciously decide to transfer knowledge
between task... | Learning what to share between loosely related tasks | 2,017 | http://arxiv.org/pdf/1705.08142v2 | Title Learning share loosely related task Summary Multitask learning motivated observation human bring bear know related problem solving new one Similarly deep neural network profit related task sharing parameter network However human consciously decide transfer knowledge task Natural Language Processing NLP hard predi... | [0.022487860172986984, 0.03934193029999733, -0.032645177096128464, 0.00894354097545147, -0.02416212111711502, -0.02859516069293022, 0.05892830342054367, -0.02223842963576317, -0.02291261963546276, -0.0076596797443926334, -0.08599571883678436, 0.01687566377222538, -0.04040209576487541, 0.07899221777915955, 0.02695311792... |
4 | 4 | ['Iulian V. Serban', 'Chinnadhurai Sankar', 'Mathieu Germain', 'Saizheng Zhang', 'Zhouhan Lin', 'Sandeep Subramanian', 'Taesup Kim', 'Michael Pieper', 'Sarath Chandar', 'Nan Rosemary Ke', 'Sai Rajeshwar', 'Alexandre de Brebisson', 'Jose M. R. Sotelo', 'Dendi Suhubdy', 'Vincent Michalski', 'Alexandre Nguyen', 'Joelle Pi... | 1709.02349v2 | We present MILABOT: a deep reinforcement learning chatbot developed by the
Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize
competition. MILABOT is capable of conversing with humans on popular small talk
topics through both speech and text. The system consists of an ensemble of
natural langu... | A Deep Reinforcement Learning Chatbot | 2,017 | http://arxiv.org/pdf/1709.02349v2 | Title Deep Reinforcement Learning Chatbot Summary present MILABOT deep reinforcement learning chatbot developed Montreal Institute Learning Algorithms MILA Amazon Alexa Prize competition MILABOT capable conversing human popular small talk topic speech text system consists ensemble natural language generation retrieval ... | [0.08369601517915726, 0.020538926124572754, -0.006166993174701929, -0.02317694202065468, -0.016374515369534492, 0.007429464254528284, -0.004607527516782284, 0.004775070585310459, 0.014056166633963585, -0.020789040252566338, -0.023264795541763306, -0.004501406103372574, -0.025124873965978622, 0.09215951710939407, -0.004... |
5 | 5 | ['Kelvin Guu', 'Tatsunori B. Hashimoto', 'Yonatan Oren', 'Percy Liang'] | 1709.08878v1 | We propose a new generative model of sentences that first samples a prototype
sentence from the training corpus and then edits it into a new sentence.
Compared to traditional models that generate from scratch either left-to-right
or by first sampling a latent sentence vector, our prototype-then-edit model
improves perp... | Generating Sentences by Editing Prototypes | 2,017 | http://arxiv.org/pdf/1709.08878v1 | Title Generating Sentences Editing Prototypes Summary propose new generative model sentence first sample prototype sentence training corpus edits new sentence Compared traditional model generate scratch either lefttoright first sampling latent sentence vector prototypethenedit model improves perplexity language modelin... | [0.08618932217359543, 0.04899665713310242, -0.02246158942580223, 0.019272757694125175, -0.03703729435801506, 0.01352265290915966, 0.037966545671224594, -0.024638528004288673, -0.027826817706227303, -0.03432176634669304, 0.004921475891023874, -0.011469664983451366, -0.023666782304644585, 0.02391095645725727, 0.040919352... |
6 | 6 | ['Iulian V. Serban', 'Chinnadhurai Sankar', 'Mathieu Germain', 'Saizheng Zhang', 'Zhouhan Lin', 'Sandeep Subramanian', 'Taesup Kim', 'Michael Pieper', 'Sarath Chandar', 'Nan Rosemary Ke', 'Sai Rajeswar', 'Alexandre de Brebisson', 'Jose M. R. Sotelo', 'Dendi Suhubdy', 'Vincent Michalski', 'Alexandre Nguyen', 'Joelle Pin... | 1801.06700v1 | We present MILABOT: a deep reinforcement learning chatbot developed by the
Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize
competition. MILABOT is capable of conversing with humans on popular small talk
topics through both speech and text. The system consists of an ensemble of
natural langu... | A Deep Reinforcement Learning Chatbot (Short Version) | 2,018 | http://arxiv.org/pdf/1801.06700v1 | Title Deep Reinforcement Learning Chatbot Short Version Summary present MILABOT deep reinforcement learning chatbot developed Montreal Institute Learning Algorithms MILA Amazon Alexa Prize competition MILABOT capable conversing human popular small talk topic speech text system consists ensemble natural language generat... | [0.07682596892118454, 0.024237127974629402, -0.0078873410820961, -0.021940061822533607, -0.00957291666418314, 0.009843532927334309, 0.0012712820898741484, -0.0006560627953149378, 0.006107945926487446, -0.02412767894566059, -0.021866416558623314, -0.002529066288843751, -0.02169090323150158, 0.08880618214607239, -0.00294... |
7 | 7 | ['Darko Brodic', 'Alessia Amelio', 'Zoran N. Milivojevic', 'Milena Jevtic'] | 1609.06492v1 | The paper introduces a new method for discrimination of documents given in
different scripts. The document is mapped into a uniformly coded text of
numerical values. It is derived from the position of the letters in the text
line, based on their typographical characteristics. Each code is considered as
a gray level. Ac... | Document Image Coding and Clustering for Script Discrimination | 2,016 | http://arxiv.org/pdf/1609.06492v1 | Title Document Image Coding Clustering Script Discrimination Summary paper introduces new method discrimination document given different script document mapped uniformly coded text numerical value derived position letter text line based typographical characteristic code considered gray level Accordingly coded text dete... | [0.015126252546906471, 0.0003478110593277961, -0.015845399349927902, 0.04707137867808342, -0.02666432224214077, 0.02335183322429657, 0.03511674329638481, 0.1077132597565651, 0.03802177309989929, -0.0408138744533062, 0.0360984206199646, 0.020254211500287056, 0.042206279933452606, 0.03312941640615463, -0.0029164124280214... |
8 | 8 | ['Mateusz Malinowski', 'Mario Fritz'] | 1610.01076v1 | Together with the development of more accurate methods in Computer Vision and
Natural Language Understanding, holistic architectures that answer on questions
about the content of real-world images have emerged. In this tutorial, we build
a neural-based approach to answer questions about images. We base our tutorial
on ... | Tutorial on Answering Questions about Images with Deep Learning | 2,016 | http://arxiv.org/pdf/1610.01076v1 | Title Tutorial Answering Questions Images Deep Learning Summary Together development accurate method Computer Vision Natural Language Understanding holistic architecture answer question content realworld image emerged tutorial build neuralbased approach answer question image base tutorial two datasets mostly DAQUAR bit... | [0.05145927891135216, 0.03849175572395325, -0.02072332054376602, 0.07118497788906097, -0.004714971873909235, -0.005271739326417446, 0.017641786485910416, -0.0026514835190027952, -0.03388672694563866, -0.031107222661376, -0.0179398525506258, -0.010696107521653175, 0.008540982380509377, 0.07507918775081635, 0.00251430040... |
9 | 9 | ['Tony Beltramelli'] | 1705.07962v2 | Transforming a graphical user interface screenshot created by a designer into
computer code is a typical task conducted by a developer in order to build
customized software, websites, and mobile applications. In this paper, we show
that deep learning methods can be leveraged to train a model end-to-end to
automatically... | pix2code: Generating Code from a Graphical User Interface Screenshot | 2,017 | http://arxiv.org/pdf/1705.07962v2 | Title pix2code Generating Code Graphical User Interface Screenshot Summary Transforming graphical user interface screenshot created designer computer code typical task conducted developer order build customized software website mobile application paper show deep learning method leveraged train model endtoend automatica... | [0.020227601751685143, 0.03282645717263222, -0.03515835851430893, 0.026191987097263336, -0.02822529338300228, -0.009803039021790028, 0.056261561810970306, 0.0431673526763916, -0.04715876281261444, -0.03450101986527443, -0.0003499208833090961, 0.03791843354701996, 0.019206956028938293, 0.16242928802967072, 0.01970106549... |
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