MachineLearning.bib

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@inproceedings{Talman:2019aa,
	Author = {Aarne Talman and Antti Suni and Hande Celikkanat and Sofoklis Kakouros and J{\"o}rg Tiedemann and Martti Vainio},
	Booktitle = {NoDaLiDa 2019},
	Date-Added = {2019-08-06 22:29:02 -0400},
	Date-Modified = {2019-08-06 22:31:58 -0400},
	Keywords = {Deep Learning - Speech},
	Title = {Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations},
	Year = {2019},
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@article{DBLP:journals/corr/abs-1901-11390,
	Archiveprefix = {arXiv},
	Author = {Christopher P. Burgess and Lo{\"{\i}}c Matthey and Nicholas Watters and Rishabh Kabra and Irina Higgins and Matthew Botvinick and Alexander Lerchner},
	Bibsource = {dblp computer science bibliography, https://dblp.org},
	Biburl = {https://dblp.org/rec/bib/journals/corr/abs-1901-11390},
	Date-Added = {2019-08-05 19:26:17 -0400},
	Date-Modified = {2019-08-05 19:26:25 -0400},
	Eprint = {1901.11390},
	Journal = {CoRR},
	Keywords = {Deep Learning - General Intelligence},
	Timestamp = {Mon, 04 Feb 2019 08:11:03 +0100},
	Title = {MONet: Unsupervised Scene Decomposition and Representation},
	Url = {http://arxiv.org/abs/1901.11390},
	Volume = {abs/1901.11390},
	Year = {2019},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1901.11390}}

@article{DBLP:journals/corr/abs-1901-11373,
	Archiveprefix = {arXiv},
	Author = {Dani Yogatama and Cyprien de Masson d'Autume and Jerome Connor and Tom{\'{a}}s Kocisk{\'{y}} and Mike Chrzanowski and Lingpeng Kong and Angeliki Lazaridou and Wang Ling and Lei Yu and Chris Dyer and Phil Blunsom},
	Bibsource = {dblp computer science bibliography, https://dblp.org},
	Biburl = {https://dblp.org/rec/bib/journals/corr/abs-1901-11373},
	Date-Added = {2019-08-05 19:21:59 -0400},
	Date-Modified = {2019-08-05 19:22:49 -0400},
	Eprint = {1901.11373},
	Journal = {CoRR},
	Keywords = {Deep Learning - General Intelligence},
	Timestamp = {Mon, 04 Feb 2019 08:11:03 +0100},
	Title = {Learning and Evaluating General Linguistic Intelligence},
	Url = {http://arxiv.org/abs/1901.11373},
	Volume = {abs/1901.11373},
	Year = {2019},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1901.11373}}

@article{tan2019efficientnet,
	Annote = {Medium article: https://medium.com/@nainaakash012/efficientnet-rethinking-model-scaling-for-convolutional-neural-networks-92941c5bfb95
https://pypi.org/project/efficientnet/

https://pypi.org/project/keras-efficientnets/
},
	Author = {Tan, Mingxing and Le, Quoc V},
	Date-Added = {2019-06-07 13:35:59 -0400},
	Date-Modified = {2019-06-07 13:45:08 -0400},
	Journal = {arXiv preprint arXiv:1905.11946},
	Keywords = {Deep Learning - Image},
	Title = {EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
	Year = {2019},
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@article{Qi:2019aa,
	Author = {Qi, Feng and Wu, Wenchuan},
	Date-Added = {2019-06-07 11:15:40 -0400},
	Date-Modified = {2019-06-07 11:16:30 -0400},
	Journal = {arXiv preprint arXiv:1905.07562},
	Keywords = {Deep Learning - General Intelligence},
	Title = {Human-like machine thinking: Language guided imagination},
	Year = {2019},
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@article{Porto-Ferreira:2018aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180104554P},
	Archiveprefix = {arXiv},
	Author = {{Porto Ferreira}, C.~H. and {Rossi de Medeiros}, D.~M. and {Olivetti de Fran{\c c}a}, F.},
	Date-Added = {2018-01-24 17:23:20 +0000},
	Date-Modified = {2018-01-24 17:23:43 +0000},
	Eprint = {1801.04554},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Text},
	Month = jan,
	Primaryclass = {cs.IR},
	Title = {{DCDistance: A Supervised Text Document Feature extraction based on class labels}},
	Year = 2018,
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@article{Hoang:2018aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180104813H},
	Archiveprefix = {arXiv},
	Author = {{Hoang}, Q.},
	Date-Added = {2018-01-24 17:20:40 +0000},
	Date-Modified = {2018-01-24 17:21:03 +0000},
	Eprint = {1801.04813},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Text},
	Month = jan,
	Primaryclass = {cs.CL},
	Title = {{Predicting Movie Genres Based on Plot Summaries}},
	Year = 2018,
	Bdsk-File-1 = {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}}

@article{Giaquinto:2018aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180104958G},
	Archiveprefix = {arXiv},
	Author = {{Giaquinto}, R. and {Banerjee}, A.},
	Date-Added = {2018-01-24 17:19:11 +0000},
	Date-Modified = {2018-01-24 17:19:29 +0000},
	Eprint = {1801.04958},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Text},
	Month = jan,
	Primaryclass = {cs.CL},
	Title = {{Topic Modeling on Health Journals with Regularized Variational Inference}},
	Year = 2018,
	Bdsk-File-1 = {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}}

@article{Toral:2018aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180104962T},
	Archiveprefix = {arXiv},
	Author = {{Toral}, A. and {Way}, A.},
	Date-Added = {2018-01-24 17:16:15 +0000},
	Date-Modified = {2018-01-24 17:16:41 +0000},
	Eprint = {1801.04962},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Text},
	Month = jan,
	Primaryclass = {cs.CL},
	Title = {{What Level of Quality can Neural Machine Translation Attain on Literary Text?}},
	Year = 2018,
	Bdsk-File-1 = {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}}

@article{Sugiura:2018aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180105096S},
	Archiveprefix = {arXiv},
	Author = {{Sugiura}, K. and {Kawai}, H.},
	Date-Added = {2018-01-24 16:50:00 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Eprint = {1801.05096},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Perception},
	Month = jan,
	Primaryclass = {cs.RO},
	Title = {{Grounded Language Understanding for Manipulation Instructions Using GAN-Based Classification}},
	Year = 2018,
	Bdsk-File-1 = {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}}

@article{Kim:2018aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180105149K},
	Archiveprefix = {arXiv},
	Author = {{Kim}, Y.-B. and {Lee}, S. and {Stratos}, K.},
	Date-Added = {2018-01-24 13:39:29 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1801.05149},
	Journal = {ArXiv e-prints},
	Keywords = {Computer Science - Computation and Language},
	Month = jan,
	Primaryclass = {cs.CL},
	Title = {{OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding}},
	Year = 2018,
	Bdsk-File-1 = {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}}

@article{Murdoch:2018aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180105453M},
	Archiveprefix = {arXiv},
	Author = {{Murdoch}, W.~J. and {Liu}, P.~J. and {Yu}, B.},
	Date-Added = {2018-01-24 13:37:12 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Eprint = {1801.05453},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Sentiment_Analysis},
	Month = jan,
	Primaryclass = {cs.CL},
	Title = {{Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs}},
	Year = 2018}

@article{Li:2018aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180105032L},
	Archiveprefix = {arXiv},
	Author = {{Li}, F.-L. and {Qiu}, M. and {Chen}, H. and {Wang}, X. and {Gao}, X. and {Huang}, J. and {Ren}, J. and {Zhao}, Z. and {Zhao}, W. and {Wang}, L. and {Jin}, G. and {Chu}, W.},
	Date-Added = {2018-01-24 13:35:23 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1801.05032},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Conversation},
	Month = jan,
	Primaryclass = {cs.CL},
	Title = {{AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience}},
	Year = 2018}

@article{Breiman:2001aa,
	Abstract = {Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund {\&} R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148--156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.},
	Author = {Breiman, Leo},
	Date-Added = {2018-01-09 22:45:10 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Day = {01},
	Doi = {10.1023/A:1010933404324},
	Issn = {1573-0565},
	Journal = {Machine Learning},
	Keywords = {Deep Learning - Other Machine Learning},
	Number = {1},
	Pages = {5--32},
	Title = {Random Forests},
	Url = {https://doi.org/10.1023/A:1010933404324},
	Volume = {45},
	Year = {2001},
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	Bdsk-Url-1 = {https://doi.org/10.1023/A:1010933404324}}

@book{Hastie:2009aa,
	Address = {New York},
	Author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
	Date-Added = {2018-01-09 22:36:42 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Isbn = {9780387848587},
	Keywords = {Deep Learning - Books},
	Lccn = {2008941148},
	Publisher = {Springer},
	Series = {Springer Series in Statistics},
	Title = {The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition},
	Url = {https://books.google.se/books?id=tVIjmNS3Ob8C},
	Year = {2009},
	Bdsk-File-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAbLi4vcGFwZXJzL0VTTElJX3ByaW50MTIucGRmTxEBiAAAAAABiAACAAAMTWFjaW50b3NoIEhEAAAAAAAAAAAAAAAAAAAAAAAAAEJEAAH/////EUVTTElJX3ByaW50MTIucGRmAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP////8AAAAAAAAAAAAAAAAAAQACAAAKIGN1AAAAAAAAAAAAAAAAAAZwYXBlcnMAAgBGLzpVc2VyczpoYXJpc3RoZW1pc3RvY2xlb3VzOkRvY3VtZW50czpsaWJyYXJ5OnBhcGVyczpFU0xJSV9wcmludDEyLnBkZgAOACQAEQBFAFMATABJAEkAXwBwAHIAaQBuAHQAMQAyAC4AcABkAGYADwAaAAwATQBhAGMAaQBuAHQAbwBzAGgAIABIAEQAEgBEVXNlcnMvaGFyaXN0aGVtaXN0b2NsZW91cy9Eb2N1bWVudHMvbGlicmFyeS9wYXBlcnMvRVNMSUlfcHJpbnQxMi5wZGYAEwABLwAAFQACABr//wAAAAgADQAaACQAQgAAAAAAAAIBAAAAAAAAAAUAAAAAAAAAAAAAAAAAAAHO},
	Bdsk-Url-1 = {https://books.google.se/books?id=tVIjmNS3Ob8C}}

@inproceedings{Cao:2017aa,
	Author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
	Booktitle = {CVPR},
	Date-Added = {2017-12-05 13:49:24 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Keywords = {Deep Learning - Perception},
	Title = {Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
	Year = {2017},
	Bdsk-File-1 = {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}}

@inproceedings{Simon:2017aa,
	Author = {Tomas Simon and Hanbyul Joo and Iain Matthews and Yaser Sheikh},
	Booktitle = {CVPR},
	Date-Added = {2017-12-05 13:49:24 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Keywords = {Deep Learning - Perception},
	Title = {Hand Keypoint Detection in Single Images using Multiview Bootstrapping},
	Year = {2017},
	Bdsk-File-1 = {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}}

@inproceedings{Wei:2016aa,
	Author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},
	Booktitle = {CVPR},
	Date-Added = {2017-12-05 13:49:24 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Keywords = {Deep Learning - Perception},
	Title = {Convolutional pose machines},
	Year = {2016},
	Bdsk-File-1 = {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}}

@article{Widrow:1960aa,
	Author = {Bernard Widrow},
	Date-Added = {2017-10-30 19:37:53 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {Stanford Electronics Laboratories Technical Report 1553-2},
	Keywords = {Deep Learning - History},
	Month = {October},
	Title = {An Adaptive `Adaline' Neuron Using Chemical `Memistors'},
	Year = {1960},
	Bdsk-File-1 = {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}}

@article{Malinowski:2015aa,
	Archiveprefix = {arXiv},
	Author = {Mateusz Malinowski and Marcus Rohrbach and Mario Fritz},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.org/rec/bib/journals/corr/MalinowskiRF15},
	Date-Added = {2017-10-23 17:55:40 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1505.01121},
	Journal = {CoRR},
	Keywords = {Deep Learning - Conversation},
	Timestamp = {Wed, 07 Jun 2017 14:41:30 +0200},
	Title = {Ask Your Neurons: {A} Neural-based Approach to Answering Questions about Images},
	Url = {http://arxiv.org/abs/1505.01121},
	Volume = {abs/1505.01121},
	Year = {2015},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1505.01121}}

@article{Silver:2017aa,
	Abstract = {A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100--0 against the previously published, champion-defeating AlphaGo.},
	Author = {Silver, David and Schrittwieser, Julian and Simonyan, Karen and Antonoglou, Ioannis and Huang, Aja and Guez, Arthur and Hubert, Thomas and Baker, Lucas and Lai, Matthew and Bolton, Adrian and Chen, Yutian and Lillicrap, Timothy and Hui, Fan and Sifre, Laurent and van den Driessche, George and Graepel, Thore and Hassabis, Demis},
	Date = {2017/10/19/print},
	Date-Added = {2017-10-22 15:46:42 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Day = {19},
	Isbn = {0028-0836},
	Journal = {Nature},
	Keywords = {Deep Learning - General Intelligence, Deep Learning - Reinforcement Learning},
	L3 = {10.1038/nature24270; http://www.nature.com/nature/journal/v550/n7676/abs/nature24270.html#supplementary-information},
	M3 = {Article},
	Month = {10},
	Number = {7676},
	Pages = {354--359},
	Publisher = {Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
	Title = {Mastering the game of Go without human knowledge},
	Ty = {JOUR},
	Url = {http://dx.doi.org/10.1038/nature24270},
	Volume = {550},
	Year = {2017},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nature24270}}

@book{Goodfellow:2016aa,
	Address = {Cambridge, MA},
	Author = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
	Date-Added = {2017-10-16 09:37:00 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - Books},
	Publisher = {MIT Press},
	Title = {Deep Learning},
	Year = {2016}}

@article{Christiansen:2008ab,
	Author = {Morten H. Christiansen and Nick Chater},
	Date-Added = {2017-10-15 15:20:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {Behavioral and Brain Sciences},
	Keywords = {Deep Learning - Theory},
	Pages = {489--558},
	Title = {Language as shaped by the brain},
	Volume = {31},
	Year = {2008},
	Bdsk-File-1 = {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}}

@book{Rumelhart:1986aa,
	Address = {Cambridge, MA},
	Author = {Rumelhart, David E and McClelland, James L and PDP Research Group and others},
	Date-Added = {2017-09-25 18:54:39 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - History},
	Publisher = {The MIT Press},
	Title = {Parallel distributed processing: Explorations in the microstructures of cognition. Volume 1: Foundations},
	Year = {1986},
	Bdsk-File-1 = {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}}

@article{Rumelhart:1988aa,
	Author = {Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J and others},
	Date-Added = {2017-09-25 18:51:57 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {Cognitive modeling},
	Keywords = {Deep Learning - History},
	Number = {3},
	Pages = {1},
	Title = {Learning representations by back-propagating errors},
	Volume = {5},
	Year = {1988},
	Bdsk-File-1 = {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}}

@techreport{Rumelhart:1985aa,
	Author = {Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J},
	Date-Added = {2017-09-25 18:47:00 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Institution = {California Univ San Diego La Jolla Inst for Cognitive Science},
	Keywords = {Deep Learning - History},
	Title = {Learning internal representations by error propagation},
	Year = {1985},
	Bdsk-File-1 = {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}}

@article{Schmidhuber:2014aa,
	Author = {J{\"{u}}rgen Schmidhuber},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/Schmidhuber14},
	Date-Added = {2017-09-25 18:38:10 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - History},
	Timestamp = {Wed, 07 Jun 2017 14:40:13 +0200},
	Title = {Deep Learning in Neural Networks: An Overview},
	Url = {http://arxiv.org/abs/1404.7828},
	Volume = {abs/1404.7828},
	Year = {2014},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1404.7828}}

@book{Hebb:1949aa,
	Address = {New York},
	Author = {Hebb, Donald Olding},
	Date-Added = {2017-09-25 16:51:08 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - History},
	Publisher = {John Wiley \& Sons Inc},
	Title = {The organization of behavior: A neuropsychological theory},
	Year = {1949},
	Bdsk-File-1 = {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}}

@article{Turing:1936aa,
	Annote = {Turing's famous demonstration of the formal limits on computation based on a proof that the {\em halting problem} is undecidable.},
	Author = {Turing, Alan M.},
	Date-Added = {2017-09-25 16:46:27 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {Proceedings of the London Mathematical Society},
	Keywords = {Deep Learning - History},
	Number = 42,
	Pages = {230--265},
	Title = {On Computable Numbers, with an Application to the {E}ntscheidungsproblem},
	Volume = 2,
	Year = 1936,
	Bdsk-File-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAccGFwZXJzL1R1cmluZ19QYXBlcl8xOTM2LnBkZk8RAdgAAAAAAdgAAgAADE1hY2ludG9zaCBIRAAAAAAAAAAAAAAAAAAAAAAAAABCRAAB/////xVUdXJpbmdfUGFwZXJfMTkzNi5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD/////AAAAAAAAAAAAAAAAAAEAAwAACiBjdQAAAAAAAAAAAAAAAAAGcGFwZXJzAAIAai86VXNlcnM6aGFyaXN0aGVtaXN0b2NsZW91czpEb2N1bWVudHM6ZGV2ZWxvcG1lbnQtb25saW5lOmRlZXBfbGVhcm5pbmdfcGFwZXJzOnBhcGVyczpUdXJpbmdfUGFwZXJfMTkzNi5wZGYADgAsABUAVAB1AHIAaQBuAGcAXwBQAGEAcABlAHIAXwAxADkAMwA2AC4AcABkAGYADwAaAAwATQBhAGMAaQBuAHQAbwBzAGgAIABIAEQAEgBoVXNlcnMvaGFyaXN0aGVtaXN0b2NsZW91cy9Eb2N1bWVudHMvZGV2ZWxvcG1lbnQtb25saW5lL2RlZXBfbGVhcm5pbmdfcGFwZXJzL3BhcGVycy9UdXJpbmdfUGFwZXJfMTkzNi5wZGYAEwABLwAAFQACABr//wAAAAgADQAaACQAQwAAAAAAAAIBAAAAAAAAAAUAAAAAAAAAAAAAAAAAAAIf},
	Bdsk-Url-1 = {http://www.cs.helsinki.fi/u/gionis/cc05/OnComputableNumbers.pdf}}

@article{Turing:1950aa,
	Author = {Turing, A. M.},
	Date-Added = {2017-09-25 16:42:06 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1093/mind/LIX.236.433},
	Eprint = {/oup/backfile/content_public/journal/mind/lix/236/10.1093_mind_lix.236.433/1/433.pdf},
	Journal = {Mind},
	Keywords = {Deep Learning - History},
	Number = {236},
	Pages = {433-460},
	Title = {I. Computing Machinery and Intelligence},
	Url = {+ http://dx.doi.org/10.1093/mind/LIX.236.433},
	Volume = {LIX},
	Year = {1950},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {+%20http://dx.doi.org/10.1093/mind/LIX.236.433},
	Bdsk-Url-2 = {http://dx.doi.org/10.1093/mind/LIX.236.433}}

@article{Rosenblatt:1958aa,
	Author = {F. Rosenblatt},
	Date-Added = {2017-09-25 16:39:36 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {Psychological Review},
	Keywords = {Deep Learning - History},
	Pages = {65--386},
	Title = {The Perceptron: A Probabilistic Model for Information Storage and Organization in The Brain},
	Year = {1958},
	Bdsk-File-1 = {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}}

@article{McCulloch:1943aa,
	Abstract = {Because of the ``all-or-none'' character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed.},
	Annote = {At the beginning there was MCP!},
	Author = {McCulloch, Warren S. and Pitts, Walter},
	Date-Added = {2017-09-25 16:32:42 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Day = {01},
	Doi = {10.1007/BF02478259},
	Issn = {1522-9602},
	Journal = {The bulletin of mathematical biophysics},
	Keywords = {Deep Learning - History},
	Month = {Dec},
	Number = {4},
	Pages = {115--133},
	Title = {A logical calculus of the ideas immanent in nervous activity},
	Url = {https://doi.org/10.1007/BF02478259},
	Volume = {5},
	Year = {1943},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {https://doi.org/10.1007/BF02478259},
	Bdsk-Url-2 = {http://dx.doi.org/10.1007/BF02478259}}

@inproceedings{Ai:2007aa,
	Author = {Hua Ai and Diane J. Litman},
	Booktitle = {In Proc. of Interspeech},
	Date-Added = {2017-09-24 10:34:00 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - Interaction},
	Title = {Knowledge Consistent User Simulations for Dialog Systems},
	Year = {2007},
	Bdsk-File-1 = {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}}

@inbook{Ai:2010aa,
	Abstract = {In this study, we describe a conversational agent designed to support collaborative learning interactions between pairs of students. We describe a study in which we independently manipulate the social capability and goal alignment of the agent in order to investigate the impact on student learning outcomes and student perceptions. Our results show a significant interaction effect between the two independent variables on student learning outcomes. While there are only a few perceived differences related to student satisfaction and tutor performance as evidenced in the questionnaire data, we observe significant differences in student conversational behavior, which offer tentative explanations for the learning outcomes we will investigate in subsequent work.},
	Address = {Berlin, Heidelberg},
	Author = {Ai, Hua and Kumar, Rohit and Nguyen, Dong and Nagasunder, Amrut and Ros{\'e}, Carolyn P.},
	Booktitle = {Intelligent Tutoring Systems: 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, Part II},
	Date-Added = {2017-09-24 10:30:50 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1007/978-3-642-13437-1_14},
	Editor = {Aleven, Vincent and Kay, Judy and Mostow, Jack},
	Isbn = {978-3-642-13437-1},
	Keywords = {Deep Learning - Interaction},
	Pages = {134--143},
	Publisher = {Springer Berlin Heidelberg},
	Title = {Exploring the Effectiveness of Social Capabilities and Goal Alignment in Computer Supported Collaborative Learning},
	Url = {https://doi.org/10.1007/978-3-642-13437-1_14},
	Year = {2010},
	Bdsk-File-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAhcGFwZXJzL0l0czIwMTBfc3VibWlzc2lvbl8xNzIucGRmTxEB7gAAAAAB7gACAAAMTWFjaW50b3NoIEhEAAAAAAAAAAAAAAAAAAAAAAAAAEJEAAH/////Gkl0czIwMTBfc3VibWlzc2lvbl8xNzIucGRmAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP////8AAAAAAAAAAAAAAAAAAQADAAAKIGN1AAAAAAAAAAAAAAAAAAZwYXBlcnMAAgBvLzpVc2VyczpoYXJpc3RoZW1pc3RvY2xlb3VzOkRvY3VtZW50czpkZXZlbG9wbWVudC1vbmxpbmU6ZGVlcF9sZWFybmluZ19wYXBlcnM6cGFwZXJzOkl0czIwMTBfc3VibWlzc2lvbl8xNzIucGRmAAAOADYAGgBJAHQAcwAyADAAMQAwAF8AcwB1AGIAbQBpAHMAcwBpAG8AbgBfADEANwAyAC4AcABkAGYADwAaAAwATQBhAGMAaQBuAHQAbwBzAGgAIABIAEQAEgBtVXNlcnMvaGFyaXN0aGVtaXN0b2NsZW91cy9Eb2N1bWVudHMvZGV2ZWxvcG1lbnQtb25saW5lL2RlZXBfbGVhcm5pbmdfcGFwZXJzL3BhcGVycy9JdHMyMDEwX3N1Ym1pc3Npb25fMTcyLnBkZgAAEwABLwAAFQACABr//wAAAAgADQAaACQASAAAAAAAAAIBAAAAAAAAAAUAAAAAAAAAAAAAAAAAAAI6},
	Bdsk-Url-1 = {https://doi.org/10.1007/978-3-642-13437-1_14},
	Bdsk-Url-2 = {http://dx.doi.org/10.1007/978-3-642-13437-1_14}}

@article{Hassabis:2017aa,
	Abstract = {The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields.},
	Annote = {doi: 10.1016/j.neuron.2017.06.011
see also the post in: https://deepmind.com/blog/ai-and-neuroscience-virtuous-circle/},
	Author = {Hassabis, Demis and Kumaran, Dharshan and Summerfield, Christopher and Botvinick, Matthew},
	Booktitle = {Neuron},
	Date-Added = {2017-09-24 10:12:46 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1016/j.neuron.2017.06.011},
	Isbn = {0896-6273},
	Journal = {Neuron},
	Keywords = {Deep Learning - General Intelligence},
	M3 = {doi: 10.1016/j.neuron.2017.06.011},
	Month = {2017/09/24},
	N2 = {The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields.},
	Number = {2},
	Pages = {245--258},
	Publisher = {Elsevier},
	Title = {Neuroscience-Inspired Artificial Intelligence},
	Ty = {JOUR},
	Url = {http://dx.doi.org/10.1016/j.neuron.2017.06.011},
	Volume = {95},
	Year = {2017},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2017.06.011}}

@article{Pusiol:2014aa,
	Author = {Pusiol, Guido and Soriano, Laura and Frank, Michael C. and Fei-Fei, Li},
	Date-Added = {2017-09-21 14:15:34 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {Proceedings of the Cognitive Science Society},
	Keywords = {Deep Learning - Speech_and_Video},
	Number = {36},
	Title = {Discovering the Signatures of Joint Attention in Child-Caregiver Interaction},
	Ty = {JOUR},
	Url = {http://www.escholarship.org/uc/item/8z45f7cw},
	Volume = {36},
	Year = {2014},
	Year1 = {2014-01-01},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://www.escholarship.org/uc/item/8z45f7cw}}

@phdthesis{Rytting:2007aa,
	Author = {Rytting, Christopher Anton},
	Date-Added = {2017-09-21 07:28:41 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Id = {osu1167698589},
	Keywords = {Deep Learning - Language Acquisition},
	N2 = {<p>Many computational models have been developed to show how infants break apart utterances into words prior to building a vocabulary&#160;&#8211; the <em>word segmentation task</em>. However, these models have been tested in relatively few languages, with little attention paid to how different phonological structures may affect the relative effectiveness of particular statistical heuristics. Moreover, even for English, since these models generally rely on transcriptions rather than on speech for input, they have shown little regard for the subsegmental variation naturally found in the speech signal. A model using transcriptional input makes unrealistic assumptions which may overestimate the model's effectiveness, relative to how it would perform on more variable input such as that found in speech.</p> <p>This dissertation addresses the first of these two issues by comparing the performance of two classes of distribution-based statistical cues on a corpus of Modern Greek, a language with a phonotactic structure significantly different from that of English, and shows how these differences change the relative effectiveness of two classes of statistical heuristics, compared to their performance in English.</p> <p>To address the second issue, this dissertation proposes an improved representation of the input that preserves the subsegmental variation inherently present in natural speech while maintaining sufficient similarity with previous models to allow for straightforward, meaningful comparisons of performance. The proposed input representation uses an automatic phone classifier to replace the transcription-based phone labels in a corpus of English child-directed speech with real-valued phone probability vectors. These vectors are then used to provide input for a previously-proposed connectionist model of word segmentation, in place of the invariant, transcription-based binary input vectors used in the original model.</p> <p>The performance of the connectionist model as reimplemented here suggests that real-valued inputs present a harder learning task than idealized inputs. In other words, the subsegmental variation hinders the model more than it helps. This may help explain why English-learning infants soon gravitate toward other, potentially more salient cues, such as lexical stress. However, the model still performs above chance even with very noisy input, consistent with studies showing that children can learn from distributional segmental cues alone.</p>},
	Publisher = {The Ohio State University},
	School = {The Ohio State University},
	Title = {Preserving subsegmental variation in modeling word segmentation (or, the raising of baby Mondegreen)},
	Ty = {THES},
	Url = {http://rave.ohiolink.edu/etdc/view?acc_num=osu1167698589},
	Year = {2007},
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@phdthesis{Marcken:1996aa,
	Author = {Carl de Marcken},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/cmp-lg-9611002},
	Date-Added = {2017-09-21 07:12:13 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language Acquisition},
	School = {MIT},
	Timestamp = {Wed, 07 Jun 2017 14:42:30 +0200},
	Title = {Unsupervised Language Acquisition},
	Url = {http://arxiv.org/abs/cmp-lg/9611002},
	Volume = {cmp-lg/9611002},
	Year = {1996},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/cmp-lg/9611002}}

@article{Grohmann:2017aa,
	Author = {Grohmann, Kleanthes K. and Papadopoulou, Elena and Themistocleous, Charalambos},
	Date-Added = {2017-09-20 10:55:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {10.3389/fcomm.2017.00005},
	Issn = {2297-900X},
	Journal = {Frontiers in Communication},
	Keywords = {Deep Learning - Language Acquisition},
	Number = {5},
	Title = {Acquiring Clitic Placement in Bilectal Settings: Interactions between Social Factors},
	Type = {Journal Article},
	Url = {http://journal.frontiersin.org/article/10.3389/fcomm.2017.00005},
	Volume = {2},
	Year = {2017},
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	Bdsk-Url-1 = {http://journal.frontiersin.org/article/10.3389/fcomm.2017.00005},
	Bdsk-Url-2 = {http://dx.doi.org/10.3389/fcomm.2017.00005}}

@article{Themistocleous:2017aa,
	Author = {Themistocleous, Charalambos},
	Date-Added = {2017-09-20 10:55:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {https://doi.org/10.1016/j.specom.2017.05.003},
	Issn = {0167-6393},
	Journal = {Speech Communication},
	Keywords = {Deep Learning - Speech},
	Pages = {13-22},
	Title = {Dialect classification using vowel acoustic parameters},
	Type = {Journal Article},
	Url = {http://www.sciencedirect.com/science/article/pii/S0167639316303132},
	Volume = {92},
	Year = {2017},
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	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0167639316303132},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.specom.2017.05.003}}

@article{Bernardi:2017aa,
	Author = {Bernardi, Jean-Philippe and Themistocleous, Charalambos},
	Date-Added = {2017-09-20 10:55:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {Experimental Linguistics 2017},
	Keywords = {Deep Learning - Speech},
	Pages = {17--20},
	Title = {Modelling prosodic structure using Artificial Neural Networks.},
	Type = {Journal Article},
	Year = {2017},
	Bdsk-File-1 = {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}}

@article{Themistocleous:2017ab,
	Author = {Themistocleous, C.},
	Date-Added = {2017-09-20 10:55:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Issn = {0031-8388},
	Journal = {Phonetica},
	Keywords = {Deep Learning - Speech},
	Number = {3},
	Pages = {157-172},
	Title = {The Nature of Phonetic Gradience across a Dialect Continuum: Evidence from Modern Greek Vowels},
	Type = {Journal Article},
	Url = {http://www.karger.com/DOI/10.1159/000450554},
	Volume = {74},
	Year = {2017},
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	Bdsk-Url-1 = {http://www.karger.com/DOI/10.1159/000450554}}

@inproceedings{Themistocleous:2016ac,
	Author = {Themistocleous, Charalambos and Savva, Angelandria and Aristodemou, Andrie},
	Booktitle = {Interspeech 2016},
	Date-Added = {2017-09-20 10:55:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Keywords = {Deep Learning - Speech},
	Pages = {1-4},
	Title = {Effects of stress on fricatives: Evidence from {S}tandard {M}odern {G}reek},
	Type = {Conference Proceedings},
	Year = {2016},
	Bdsk-File-1 = {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}}

@article{Themistocleous:2016aa,
	Author = {Themistocleous, Charalambos},
	Date-Added = {2017-09-20 10:55:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {doi: 10.1177/0023830915614602},
	Journal = {Language and Speech},
	Keywords = {Deep Learning - Speech},
	Number = {4},
	Pages = {433-461},
	Title = {Seeking an Anchorage. Stability and Variability in Tonal Alignment of Rising Prenuclear Pitch Accents in Cypriot Greek},
	Type = {Journal Article},
	Volume = {59},
	Year = {2016},
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	Bdsk-Url-1 = {http://dx.doi.org/10.1177/0023830915614602}}

@article{Themistocleous:2016ab,
	Author = {Charalambos Themistocleous},
	Date-Added = {2017-09-20 10:55:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {10.1121/1.4964818},
	Eprint = {http://dx.doi.org/10.1121/1.4964818},
	Journal = {The Journal of the Acoustical Society of America},
	Keywords = {Deep Learning - Speech},
	Number = {4},
	Pages = {EL334-EL339},
	Title = {The bursts of stops can convey dialectal information},
	Url = {http://dx.doi.org/10.1121/1.4964818},
	Volume = {140},
	Year = {2016},
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	Bdsk-Url-1 = {http://dx.doi.org/10.1121/1.4964818}}

@article{Lee:2012aa,
	Annote = {See also their github page at https://github.com/jacquelineCelia/dphmm_silence

},
	Author = {Chia-ying Lee and James Glass},
	Date-Added = {2017-09-12 20:11:11 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {ACL},
	Keywords = {Deep Learning - Speech},
	Title = {A Nonparametric Bayesian Approach to Acoustic Model Discovery},
	Year = {2012},
	Bdsk-File-1 = {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}}

@article{Elsner:2017aa,
	Author = {Micha Elsner and Cory Shain},
	Date-Added = {2017-09-12 20:01:42 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {Empirical Methods in Natural Language Processing (EMNLP 2017)},
	Keywords = {Deep Learning - Speech},
	Title = {Speech segmentation with a neural encoder model of working memory},
	Year = {2017},
	Bdsk-File-1 = {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},
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@article{Serban:2017aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170902349S},
	Archiveprefix = {arXiv},
	Author = {{Serban}, I.~V. and {Sankar}, C. and {Germain}, M. and {Zhang}, S. and {Lin}, Z. and {Subramanian}, S. and {Kim}, T. and {Pieper}, M. and {Chandar}, S. and {Ke}, N.~R. and {Mudumba}, S. and {de Brebisson}, A. and {Sotelo}, J.~M.~R. and {Suhubdy}, D. and {Michalski}, V. and {Nguyen}, A. and {Pineau}, J. and {Bengio}, Y.},
	Date-Added = {2017-09-10 15:07:01 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1709.02349},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Conversation; Deep Learning - Reinforcement Learning},
	Month = sep,
	Primaryclass = {cs.CL},
	Title = {{A Deep Reinforcement Learning Chatbot}},
	Year = 2017,
	Bdsk-File-1 = {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}}

@article{Owens:2015aa,
	Author = {Andrew Owens and Phillip Isola and Josh H. McDermott and Antonio Torralba and Edward H. Adelson and William T. Freeman},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/OwensIMTAF15},
	Date-Added = {2017-09-09 17:52:43 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Speech_and_Video},
	Timestamp = {Wed, 12 Jul 2017 09:11:26 +0200},
	Title = {Visually Indicated Sounds},
	Url = {http://arxiv.org/abs/1512.08512},
	Volume = {abs/1512.08512},
	Year = {2015},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1512.08512}}

@article{Harwath:2016aa,
	Author = {David Harwath and Antonio Torralba and James R. Glass},
	Date-Added = {2017-09-09 17:50:17 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {NIPS 2016},
	Keywords = {Deep Learning - Speech_and_Video},
	Title = {Unsupervised Learning of Spoken Language with Visual Context},
	Year = {2016},
	Bdsk-File-1 = {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}}

@article{Aytar:2016aa,
	Author = {Yusuf Aytar and Carl Vondrick and Antonio Torralba},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/AytarVT16},
	Date-Added = {2017-09-09 17:44:55 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Speech_and_Video},
	Timestamp = {Thu, 13 Jul 2017 11:49:40 +0200},
	Title = {SoundNet: Learning Sound Representations from Unlabeled Video},
	Url = {http://arxiv.org/abs/1610.09001},
	Volume = {abs/1610.09001},
	Year = {2016},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1610.09001}}

@article{Arandjelovic:2017aa,
	Author = {Relja Arandjelovic and Andrew Zisserman},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/ArandjelovicZ17},
	Date-Added = {2017-09-09 17:39:27 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Speech_and_Video},
	Timestamp = {Wed, 07 Jun 2017 14:40:41 +0200},
	Title = {Look, Listen and Learn},
	Url = {http://arxiv.org/abs/1705.08168},
	Volume = {abs/1705.08168},
	Year = {2017},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1705.08168}}

@article{Vinyals:2017aa,
	Author = {Oriol Vinyals and Timo Ewalds and Sergey Bartunov and Petko Georgiev and Alexander Sasha Vezhnevets and Michelle Yeo and Alireza Makhzani and John Agapiou and Julian Schrittwieser and Stephen Gaffney and Karen Simonyan and Tom Schaul and Hado van Hasselt and David Silver and Heinrich K\"uttler and Stig Petersen and Timothy Lillicrap and Kevin Calderone and Paul Keet and Anthony Brunasso and David Lawrence and Anders Ekermo and Jacob Repp and Rodney Tsing},
	Date-Added = {2017-08-11 19:23:55 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - General Intelligence, Deep Learning - Environments; Deep Learning - Reinforcement Learning},
	Title = {StarCraft II: A New Challenge for Reinforcement Learning},
	Year = {2017},
	Bdsk-File-1 = {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}}

@article{Adams:2017aa,
	Author = {Terrence Adams},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/Adams17},
	Date-Added = {2017-08-06 20:07:17 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - General Intelligence, Deep Learning - Conversation},
	Timestamp = {Mon, 03 Jul 2017 13:29:02 +0200},
	Title = {AI-Powered Social Bots},
	Url = {http://arxiv.org/abs/1706.05143},
	Volume = {abs/1706.05143},
	Year = {2017},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1706.05143}}

@article{Kaiser:2017aa,
	Author = {Lukasz Kaiser and Aidan N. Gomez and Noam Shazeer and Ashish Vaswani and Niki Parmar and Llion Jones and Jakob Uszkoreit},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KaiserGSVPJU17},
	Date-Added = {2017-08-06 20:02:06 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - General Intelligence},
	Timestamp = {Mon, 03 Jul 2017 13:29:02 +0200},
	Title = {One Model To Learn Them All},
	Url = {http://arxiv.org/abs/1706.05137},
	Volume = {abs/1706.05137},
	Year = {2017},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1706.05137}}

@article{Weber:2017aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170706203W},
	Archiveprefix = {arXiv},
	Author = {{Weber}, T. and {Racani{\`e}re}, S. and {Reichert}, D.~P. and {Buesing}, L. and {Guez}, A. and {Jimenez Rezende}, D. and {Puigdom{\`e}nech Badia}, A. and {Vinyals}, O. and {Heess}, N. and {Li}, Y. and {Pascanu}, R. and {Battaglia}, P. and {Silver}, D. and {Wierstra}, D.},
	Date-Added = {2017-07-29 20:03:14 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1707.06203},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - General Intelligence; Deep Learning - Reinforcement Learning},
	Month = jul,
	Primaryclass = {cs.LG},
	Title = {{Imagination-Augmented Agents for Deep Reinforcement Learning}},
	Year = 2017,
	Bdsk-File-1 = {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}}

@article{Pascanu:2017aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170706170P},
	Archiveprefix = {arXiv},
	Author = {{Pascanu}, R. and {Li}, Y. and {Vinyals}, O. and {Heess}, N. and {Buesing}, L. and {Racani{\`e}re}, S. and {Reichert}, D. and {Weber}, T. and {Wierstra}, D. and {Battaglia}, P.},
	Date-Added = {2017-07-29 20:01:08 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1707.06170},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - General Intelligence},
	Month = jul,
	Primaryclass = {cs.AI},
	Title = {{Learning model-based planning from scratch}},
	Year = 2017,
	Bdsk-File-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAYLi4vcGFwZXJzLzE3MDcuMDYxNzAucGRmTxEBfgAAAAABfgACAAAMTWFjaW50b3NoIEhEAAAAAAAAAAAAAAAAAAAAAAAAAEJEAAH/////DjE3MDcuMDYxNzAucGRmAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP////8AAAAAAAAAAAAAAAAAAQACAAAKIGN1AAAAAAAAAAAAAAAAAAZwYXBlcnMAAgBDLzpVc2VyczpoYXJpc3RoZW1pc3RvY2xlb3VzOkRvY3VtZW50czpsaWJyYXJ5OnBhcGVyczoxNzA3LjA2MTcwLnBkZgAADgAeAA4AMQA3ADAANwAuADAANgAxADcAMAAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIAQVVzZXJzL2hhcmlzdGhlbWlzdG9jbGVvdXMvRG9jdW1lbnRzL2xpYnJhcnkvcGFwZXJzLzE3MDcuMDYxNzAucGRmAAATAAEvAAAVAAIAGv//AAAACAANABoAJAA/AAAAAAAAAgEAAAAAAAAABQAAAAAAAAAAAAAAAAAAAcE=},
	Bdsk-Url-1 = {https://arxiv.org/pdf/1707.06170}}

@article{Shen:2017aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170706519S},
	Archiveprefix = {arXiv},
	Author = {{Shen}, C.-H. and {Sung}, J.~Y. and {Lee}, H.-Y.},
	Date-Added = {2017-07-27 21:45:41 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Eprint = {1707.06519},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Speech},
	Month = jul,
	Primaryclass = {cs.CL},
	Title = {{Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data}},
	Year = 2017,
	Bdsk-File-1 = {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}}

@article{Taigman:2017aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170706588T},
	Archiveprefix = {arXiv},
	Author = {{Taigman}, Y. and {Wolf}, L. and {Polyak}, A. and {Nachmani}, E.},
	Date-Added = {2017-07-27 21:43:01 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Eprint = {1707.06588},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Speech},
	Month = jul,
	Primaryclass = {cs.LG},
	Title = {{Voice Synthesis for in-the-Wild Speakers via a Phonological Loop}},
	Year = 2017,
	Bdsk-File-1 = {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}}

@article{Rekabsaz:2017aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170706598R},
	Archiveprefix = {arXiv},
	Author = {{Rekabsaz}, N. and {Mitra}, B. and {Lupu}, M. and {Hanbury}, A.},
	Date-Added = {2017-07-27 21:40:25 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1707.06598},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Language},
	Month = jul,
	Primaryclass = {cs.IR},
	Title = {{Toward Incorporation of Relevant Documents in word2vec}},
	Year = 2017,
	Bdsk-File-1 = {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}}

@article{Assylbekov:2017aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170706480A},
	Archiveprefix = {arXiv},
	Author = {{Assylbekov}, Z. and {Takhanov}, R. and {Myrzakhmetov}, B. and {Washington}, J.~N.},
	Date-Added = {2017-07-27 21:38:06 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1707.06480},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Language},
	Month = jul,
	Primaryclass = {cs.CL},
	Title = {{Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones}},
	Year = 2017,
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@article{Bojanowski:2017aa,
	Author = {Piotr Bojanowski and Edouard Grave and Armand Joulin and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/tacl/BojanowskiGJM17},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {{TACL}},
	Keywords = {Deep Learning - Language},
	Pages = {135--146},
	Timestamp = {Wed, 28 Jun 2017 01:00:00 +0200},
	Title = {Enriching Word Vectors with Subword Information},
	Url = {https://transacl.org/ojs/index.php/tacl/article/view/999},
	Volume = {5},
	Year = {2017},
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	Bdsk-Url-1 = {https://transacl.org/ojs/index.php/tacl/article/view/999}}

@article{Bojanowski:2015aa,
	Author = {Piotr Bojanowski and Armand Joulin and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/BojanowskiJM15},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {Alternative structures for character-level RNNs},
	Url = {http://arxiv.org/abs/1511.06303},
	Volume = {abs/1511.06303},
	Year = {2015},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1511.06303}}

@article{Deoras:2013aa,
	Author = {Anoop Deoras and Tomas Mikolov and Stefan Kombrink and Kenneth Church},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/speech/DeorasMKC13},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1016/j.specom.2012.08.004},
	Journal = {Speech Communication},
	Keywords = {Deep Learning - Language},
	Number = {1},
	Pages = {162--177},
	Timestamp = {Sat, 20 May 2017 01:00:00 +0200},
	Title = {Approximate inference: {A} sampling based modeling technique to capture complex dependencies in a language model},
	Url = {https://doi.org/10.1016/j.specom.2012.08.004},
	Volume = {55},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.specom.2012.08.004},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.specom.2012.08.004}}

@inproceedings{Frome:2013aa,
	Author = {Andrea Frome and Gregory S. Corrado and Jonathon Shlens and Samy Bengio and Jeffrey Dean and Marc\'Aurelio Ranzato and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/conf/nips/FromeCSBDRM13},
	Booktitle = {Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States.},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - Language},
	Pages = {2121--2129},
	Timestamp = {Fri, 31 Jan 2014 12:11:40 +0100},
	Title = {DeViSE: {A} Deep Visual-Semantic Embedding Model},
	Url = {http://papers.nips.cc/paper/5204-devise-a-deep-visual-semantic-embedding-model},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://papers.nips.cc/paper/5204-devise-a-deep-visual-semantic-embedding-model}}

@inproceedings{Glembek:2008aa,
	Author = {Ondrej Glembek and Pavel Matejka and Luk{\'{a}}s Burget and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/conf/interspeech/GlembekMBM08},
	Booktitle = {{INTERSPEECH} 2008, 9th Annual Conference of the International Speech Communication Association, Brisbane, Australia, September 22-26, 2008},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Keywords = {Deep Learning - Language},
	Pages = {743--746},
	Timestamp = {Thu, 09 Apr 2015 01:00:00 +0200},
	Title = {Advances in phonotactic language recognition},
	Url = {http://www.isca-speech.org/archive/interspeech_2008/i08_0743.html},
	Year = {2008},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://www.isca-speech.org/archive/interspeech_2008/i08_0743.html}}

@article{II:2017aa,
	Author = {Alexander G. Ororbia II and Tomas Mikolov and David Reitter},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/OrorbiaMR17},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {Learning Simpler Language Models with the Delta Recurrent Neural Network Framework},
	Url = {http://arxiv.org/abs/1703.08864},
	Volume = {abs/1703.08864},
	Year = {2017},
	Bdsk-Url-1 = {http://arxiv.org/abs/1703.08864}}

@article{Jernite:2016aa,
	Author = {Yacine Jernite and Edouard Grave and Armand Joulin and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/JerniteGJM16},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {Variable Computation in Recurrent Neural Networks},
	Url = {http://arxiv.org/abs/1611.06188},
	Volume = {abs/1611.06188},
	Year = {2016},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1611.06188}}

@article{Joulin:2016ab,
	Author = {Armand Joulin and Edouard Grave and Piotr Bojanowski and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/JoulinGBM16},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {Bag of Tricks for Efficient Text Classification},
	Url = {http://arxiv.org/abs/1607.01759},
	Volume = {abs/1607.01759},
	Year = {2016},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1607.01759}}

@article{Joulin:2016aa,
	Author = {Armand Joulin and Edouard Grave and Piotr Bojanowski and Matthijs Douze and Herv{\'{e}} J{\'{e}}gou and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/JoulinGBDJM16},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {FastText.zip: Compressing text classification models},
	Url = {http://arxiv.org/abs/1612.03651},
	Volume = {abs/1612.03651},
	Year = {2016},
	Bdsk-Url-1 = {http://arxiv.org/abs/1612.03651}}

@inproceedings{Joulin:2015aa,
	Author = {Armand Joulin and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/conf/nips/JoulinM15},
	Booktitle = {Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - Language},
	Pages = {190--198},
	Timestamp = {Fri, 08 Apr 2016 19:32:52 +0200},
	Title = {Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets},
	Url = {http://papers.nips.cc/paper/5857-inferring-algorithmic-patterns-with-stack-augmented-recurrent-nets},
	Year = {2015},
	Bdsk-Url-1 = {http://papers.nips.cc/paper/5857-inferring-algorithmic-patterns-with-stack-augmented-recurrent-nets}}

@article{Mesnil:2014aa,
	Author = {Gr{\'{e}}goire Mesnil and Tomas Mikolov and Marc'Aurelio Ranzato and Yoshua Bengio},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/MesnilMRB14},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews},
	Url = {http://arxiv.org/abs/1412.5335},
	Volume = {abs/1412.5335},
	Year = {2014},
	Bdsk-Url-1 = {http://arxiv.org/abs/1412.5335}}

@article{Mikolov:2014ab,
	Author = {Tomas Mikolov and Armand Joulin and Sumit Chopra and Micha{\"{e}}l Mathieu and Marc'Aurelio Ranzato},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/MikolovJCMR14},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {Learning Longer Memory in Recurrent Neural Networks},
	Url = {http://arxiv.org/abs/1412.7753},
	Volume = {abs/1412.7753},
	Year = {2014},
	Bdsk-Url-1 = {http://arxiv.org/abs/1412.7753}}

@inproceedings{Mikolov:2013aa,
	Author = {Tomas Mikolov and Wen{-}tau Yih and Geoffrey Zweig},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/conf/naacl/MikolovYZ13},
	Booktitle = {Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 9-14, 2013, Westin Peachtree Plaza Hotel, Atlanta, Georgia, {USA}},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - Language},
	Pages = {746--751},
	Timestamp = {Wed, 05 Apr 2017 12:53:50 +0200},
	Title = {Linguistic Regularities in Continuous Space Word Representations},
	Url = {http://aclweb.org/anthology/N/N13/N13-1090.pdf},
	Year = {2013},
	Bdsk-Url-1 = {http://aclweb.org/anthology/N/N13/N13-1090.pdf}}

@article{Mikolov:2013ad,
	Author = {Tomas Mikolov and Quoc V. Le and Ilya Sutskever},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/MikolovLS13},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {Exploiting Similarities among Languages for Machine Translation},
	Url = {http://arxiv.org/abs/1309.4168},
	Volume = {abs/1309.4168},
	Year = {2013},
	Bdsk-Url-1 = {http://arxiv.org/abs/1309.4168}}

@article{Norouzi:2013aa,
	Author = {Mohammad Norouzi and Tomas Mikolov and Samy Bengio and Yoram Singer and Jonathon Shlens and Andrea Frome and Greg Corrado and Jeffrey Dean},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/NorouziMBSSFCD13},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {Zero-Shot Learning by Convex Combination of Semantic Embeddings},
	Url = {http://arxiv.org/abs/1312.5650},
	Volume = {abs/1312.5650},
	Year = {2013},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1312.5650}}

@inproceedings{Pascanu:2013aa,
	Author = {Razvan Pascanu and Tomas Mikolov and Yoshua Bengio},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/conf/icml/PascanuMB13},
	Booktitle = {Proceedings of the 30th International Conference on Machine Learning, {ICML} 2013, Atlanta, GA, USA, 16-21 June 2013},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - Language},
	Pages = {1310--1318},
	Timestamp = {Wed, 29 Mar 2017 16:45:25 +0200},
	Title = {On the difficulty of training recurrent neural networks},
	Url = {http://jmlr.org/proceedings/papers/v28/pascanu13.html},
	Year = {2013},
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	Bdsk-Url-1 = {http://jmlr.org/proceedings/papers/v28/pascanu13.html}}

@article{Pascanu:2012aa,
	Author = {Razvan Pascanu and Tomas Mikolov and Yoshua Bengio},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1211-5063},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language; Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 01:00:00 +0200},
	Title = {Understanding the exploding gradient problem},
	Url = {http://arxiv.org/abs/1211.5063},
	Volume = {abs/1211.5063},
	Year = {2012},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1211.5063}}

@inproceedings{Zaremba:2016aa,
	Author = {Wojciech Zaremba and Tomas Mikolov and Armand Joulin and Rob Fergus},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/conf/icml/ZarembaMJF16},
	Booktitle = {Proceedings of the 33nd International Conference on Machine Learning, {ICML} 2016, New York City, NY, USA, June 19-24, 2016},
	Date-Added = {2017-07-27 15:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - Language},
	Pages = {421--429},
	Timestamp = {Tue, 12 Jul 2016 21:51:16 +0200},
	Title = {Learning Simple Algorithms from Examples},
	Url = {http://jmlr.org/proceedings/papers/v48/zaremba16.html},
	Year = {2016},
	Bdsk-Url-1 = {http://jmlr.org/proceedings/papers/v48/zaremba16.html}}

@article{Johnson:2017aa,
	Author = {Justin Johnson and Bharath Hariharan and Laurens van der Maaten and Judy Hoffman and Fei{-}Fei Li and C. Lawrence Zitnick and Ross B. Girshick},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/JohnsonHMHLZG17},
	Date-Added = {2017-07-27 14:49:27 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 14:42:58 +0200},
	Title = {Inferring and Executing Programs for Visual Reasoning},
	Url = {http://arxiv.org/abs/1705.03633},
	Volume = {abs/1705.03633},
	Year = {2017},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1705.03633}}

@article{Johnson:2016aa,
	Author = {Justin Johnson and Bharath Hariharan and Laurens van der Maaten and Li Fei{-}Fei and C. Lawrence Zitnick and Ross B. Girshick},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/JohnsonHMFZG16},
	Date-Added = {2017-07-27 14:48:01 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 14:40:40 +0200},
	Title = {{CLEVR:} {A} Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning},
	Url = {http://arxiv.org/abs/1612.06890},
	Volume = {abs/1612.06890},
	Year = {2016},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1612.06890}}

@article{Andreas:2015aa,
	Author = {Jacob Andreas and Marcus Rohrbach and Trevor Darrell and Dan Klein},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/AndreasRDK15},
	Date-Added = {2017-07-27 14:46:32 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 14:40:47 +0200},
	Title = {Deep Compositional Question Answering with Neural Module Networks},
	Url = {http://arxiv.org/abs/1511.02799},
	Volume = {abs/1511.02799},
	Year = {2015},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1511.02799}}

@article{Hermann:2014aa,
	Author = {Karl Moritz Hermann},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/Hermann14},
	Date-Added = {2017-07-27 14:15:17 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 14:42:39 +0200},
	Title = {Distributed Representations for Compositional Semantics},
	Url = {http://arxiv.org/abs/1411.3146},
	Volume = {abs/1411.3146},
	Year = {2014},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1411.3146}}

@inproceedings{Lai:2015aa,
	Acmid = {2886636},
	Author = {Lai, Siwei and Xu, Liheng and Liu, Kang and Zhao, Jun},
	Booktitle = {Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence},
	Date-Added = {2017-07-27 14:12:05 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Isbn = {0-262-51129-0},
	Keywords = {Deep Learning - Language},
	Location = {Austin, Texas},
	Numpages = {7},
	Pages = {2267--2273},
	Publisher = {AAAI Press},
	Series = {AAAI'15},
	Title = {Recurrent Convolutional Neural Networks for Text Classification},
	Url = {http://dl.acm.org/citation.cfm?id=2886521.2886636},
	Year = {2015},
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	Bdsk-Url-1 = {http://dl.acm.org/citation.cfm?id=2886521.2886636}}

@article{Bengio:2003aa,
	Author = {Yoshua Bengio and R{\'e}jean Ducharme and Pascal Vincent and Christian Jauvin},
	Date-Added = {2017-07-27 14:05:01 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {Journal of Machine Learning Research},
	Keywords = {Deep Learning - Language},
	Pages = {1137--1155},
	Title = {A Neural Probabilistic Language Model},
	Volume = {3},
	Year = {2003},
	Bdsk-File-1 = {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}}

@article{Goldberg:2015aa,
	Author = {Yoav Goldberg},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/Goldberg15c},
	Date-Added = {2017-07-27 14:00:40 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Language},
	Timestamp = {Wed, 07 Jun 2017 14:41:26 +0200},
	Title = {A Primer on Neural Network Models for Natural Language Processing},
	Url = {http://arxiv.org/abs/1510.00726},
	Volume = {abs/1510.00726},
	Year = {2015},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1510.00726}}

@article{Oord:2016aa,
	Author = {A{\"{a}}ron van den Oord and Sander Dieleman and Heiga Zen and Karen Simonyan and Oriol Vinyals and Alex Graves and Nal Kalchbrenner and Andrew W. Senior and Koray Kavukcuoglu},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/OordDZSVGKSK16},
	Date-Added = {2017-07-27 13:56:57 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Speech},
	Timestamp = {Wed, 07 Jun 2017 14:42:54 +0200},
	Title = {WaveNet: {A} Generative Model for Raw Audio},
	Url = {http://arxiv.org/abs/1609.03499},
	Volume = {abs/1609.03499},
	Year = {2016},
	Bdsk-File-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAVcGFwZXJzLzE2MDkuMDM0OTkucGRmTxEBvgAAAAABvgACAAAMTWFjaW50b3NoIEhEAAAAAAAAAAAAAAAAAAAAAAAAAEJEAAH/////DjE2MDkuMDM0OTkucGRmAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP////8AAAAAAAAAAAAAAAAAAQADAAAKIGN1AAAAAAAAAAAAAAAAAAZwYXBlcnMAAgBjLzpVc2VyczpoYXJpc3RoZW1pc3RvY2xlb3VzOkRvY3VtZW50czpkZXZlbG9wbWVudC1vbmxpbmU6ZGVlcF9sZWFybmluZ19wYXBlcnM6cGFwZXJzOjE2MDkuMDM0OTkucGRmAAAOAB4ADgAxADYAMAA5AC4AMAAzADQAOQA5AC4AcABkAGYADwAaAAwATQBhAGMAaQBuAHQAbwBzAGgAIABIAEQAEgBhVXNlcnMvaGFyaXN0aGVtaXN0b2NsZW91cy9Eb2N1bWVudHMvZGV2ZWxvcG1lbnQtb25saW5lL2RlZXBfbGVhcm5pbmdfcGFwZXJzL3BhcGVycy8xNjA5LjAzNDk5LnBkZgAAEwABLwAAFQACABr//wAAAAgADQAaACQAPAAAAAAAAAIBAAAAAAAAAAUAAAAAAAAAAAAAAAAAAAH+},
	Bdsk-Url-1 = {http://arxiv.org/abs/1609.03499}}

@incollection{Irsoy:2014aa,
	Author = {\.{I}rsoy, Ozan and Cardie, Claire},
	Booktitle = {Advances in Neural Information Processing Systems 27},
	Date-Added = {2017-07-27 13:52:57 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Editor = {Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger},
	Keywords = {Deep Learning - Language},
	Location = {Montreal, Quebec},
	Pages = {2096--2104},
	Publisher = {Curran Associates, Inc.},
	Title = {Deep Recursive Neural Networks for Compositionality in Language},
	Url = {http://papers.nips.cc/paper/5551-deep-recursive-neural-networks-for-compositionality-in-language.pdf},
	Year = {2014},
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	Bdsk-Url-1 = {http://papers.nips.cc/paper/5551-deep-recursive-neural-networks-for-compositionality-in-language.pdf}}

@article{Goodfellow:2014aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2014arXiv1406.2661G},
	Archiveprefix = {arXiv},
	Author = {{Goodfellow}, I.~J. and {Pouget-Abadie}, J. and {Mirza}, M. and {Xu}, B. and {Warde-Farley}, D. and {Ozair}, S. and {Courville}, A. and {Bengio}, Y.},
	Date-Added = {2017-07-27 12:44:35 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Eprint = {1406.2661},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Theory},
	Month = jun,
	Primaryclass = {stat.ML},
	Title = {{Generative Adversarial Networks}},
	Year = 2014,
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@inproceedings{Karpathy:2014aa,
	Author = {Andrej Karpathy and George Toderici and Sanketh Shetty and Thomas Leung and Rahul Sukthankar and Li Fei-Fei},
	Booktitle = {CVPR},
	Date-Added = {2017-07-26 16:34:02 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Keywords = {Deep Learning - Video},
	Title = {Large-scale Video Classification with Convolutional Neural Networks},
	Year = {2014},
	Bdsk-File-1 = {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}}

@inproceedings{Karpathy:2015aa,
	Author = {Karpathy, Andrej and Fei-Fei, Li},
	Booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	Date-Added = {2017-07-26 16:31:09 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Keywords = {Deep Learning - Caption},
	Month = {June},
	Title = {Deep Visual-Semantic Alignments for Generating Image Descriptions},
	Year = {2015},
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@article{Lara:2013aa,
	Author = {Oscar D. Lara and Miguel A. Labrador},
	Date-Added = {2017-07-26 16:29:44 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {IEEE Communications Surveys and Tutorials},
	Keywords = {Deep Learning - Video},
	Pages = {1192-1209},
	Title = {A Survey on Human Activity Recognition using Wearable Sensors},
	Volume = {15},
	Year = {2013},
	Bdsk-File-1 = {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}}

@article{Girshick:2013aa,
	Author = {Ross B. Girshick and Jeff Donahue and Trevor Darrell and Jitendra Malik},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/GirshickDDM13},
	Date-Added = {2017-07-26 16:28:51 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Image},
	Timestamp = {Wed, 07 Jun 2017 14:43:00 +0200},
	Title = {Rich feature hierarchies for accurate object detection and semantic segmentation},
	Url = {http://arxiv.org/abs/1311.2524},
	Volume = {abs/1311.2524},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1311.2524}}

@article{Xu:2015aa,
	Author = {Kelvin Xu and Jimmy Ba and Ryan Kiros and Kyunghyun Cho and Aaron C. Courville and Ruslan Salakhutdinov and Richard S. Zemel and Yoshua Bengio},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/XuBKCCSZB15},
	Date-Added = {2017-07-26 16:28:10 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Caption},
	Timestamp = {Wed, 07 Jun 2017 14:43:05 +0200},
	Title = {Show, Attend and Tell: Neural Image Caption Generation with Visual Attention},
	Url = {http://arxiv.org/abs/1502.03044},
	Volume = {abs/1502.03044},
	Year = {2015},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1502.03044}}

@article{Zeiler:2013aa,
	Author = {Matthew D. Zeiler and Rob Fergus},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/ZeilerF13},
	Date-Added = {2017-07-26 16:27:27 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:40:45 +0200},
	Title = {Visualizing and Understanding Convolutional Networks},
	Url = {http://arxiv.org/abs/1311.2901},
	Volume = {abs/1311.2901},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1311.2901}}

@article{Ji:2013aa,
	Abstract = {We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. Convolutional neural networks (CNNs) are a type of deep model that can act directly on the raw inputs. However, such models are currently limited to handling 2D inputs. In this paper, we develop a novel 3D CNN model for action recognition. This model extracts features from both the spatial and the temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames. The developed model generates multiple channels of information from the input frames, and the final feature representation combines information from all channels. To further boost the performance, we propose regularizing the outputs with high-level features and combining the predictions of a variety of different models. We apply the developed models to recognize human actions in the real-world environment of airport surveillance videos, and they achieve superior performance in comparison to baseline methods.},
	Author = {S. Ji and W. Xu and M. Yang and K. Yu},
	Date-Added = {2017-07-26 16:26:41 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Doi = {10.1109/TPAMI.2012.59},
	Issn = {0162-8828},
	Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	Keywords = {Deep Learning - Video},
	Month = {Jan},
	Number = {1},
	Pages = {221-231},
	Title = {3D Convolutional Neural Networks for Human Action Recognition},
	Volume = {35},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/TPAMI.2012.59}}

@inproceedings{Pennington:2014aa,
	Author = {Jeffrey Pennington and Richard Socher and Christopher D. Manning},
	Booktitle = {In EMNLP},
	Date-Added = {2017-07-26 16:24:54 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Keywords = {Deep Learning - Word Embeddings},
	Title = {Glove: Global vectors for word representation},
	Year = {2014},
	Bdsk-File-1 = {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},
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@article{Toshev:2013aa,
	Author = {Alexander Toshev and Christian Szegedy},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/ToshevS13},
	Date-Added = {2017-07-26 16:22:54 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Video},
	Timestamp = {Wed, 07 Jun 2017 14:41:29 +0200},
	Title = {DeepPose: Human Pose Estimation via Deep Neural Networks},
	Url = {http://arxiv.org/abs/1312.4659},
	Volume = {abs/1312.4659},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1312.4659}}

@article{Sutskever:2014aa,
	Author = {Ilya Sutskever and Oriol Vinyals and Quoc V. Le},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/SutskeverVL14},
	Date-Added = {2017-07-26 16:22:07 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Word Embeddings},
	Timestamp = {Wed, 07 Jun 2017 14:40:10 +0200},
	Title = {Sequence to Sequence Learning with Neural Networks},
	Url = {http://arxiv.org/abs/1409.3215},
	Volume = {abs/1409.3215},
	Year = {2014},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1409.3215}}

@article{Le:2014aa,
	Author = {Quoc V. Le and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/LeM14},
	Date-Added = {2017-07-26 16:21:18 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Word Embeddings},
	Timestamp = {Wed, 07 Jun 2017 14:42:08 +0200},
	Title = {Distributed Representations of Sentences and Documents},
	Url = {http://arxiv.org/abs/1405.4053},
	Volume = {abs/1405.4053},
	Year = {2014},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1405.4053}}

@article{Mikolov:2013ac,
	Author = {Tomas Mikolov and Ilya Sutskever and Kai Chen and Greg Corrado and Jeffrey Dean},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/MikolovSCCD13},
	Date-Added = {2017-07-26 16:19:53 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Word Embeddings},
	Timestamp = {Wed, 07 Jun 2017 14:40:03 +0200},
	Title = {Distributed Representations of Words and Phrases and their Compositionality},
	Url = {http://arxiv.org/abs/1310.4546},
	Volume = {abs/1310.4546},
	Year = {2013},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1310.4546}}

@article{Vinyals:2015aa,
	Author = {Oriol Vinyals and Alexander Toshev and Samy Bengio and Dumitru Erhan},
	Date-Added = {2017-07-26 16:18:56 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	Keywords = {Deep Learning - Caption},
	Pages = {3156-3164},
	Title = {Show and tell: A neural image caption generator},
	Year = {2015},
	Bdsk-File-1 = {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}}

@inbook{Boureau:2010aa,
	Abstract = {Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter re-sponses, or SIFT descriptors) into richer representations of intermediate complexity. This process can often be bro-ken down into two steps: (1) a coding step, which per-forms a pointwise transformation of the descriptors into a representation better adapted to the task, and (2) a pool-ing step, which summarizes the coded features over larger neighborhoods. Several combinations of coding and pool-ing schemes have been proposed in the literature. The goal of this paper is threefold. We seek to establish the rela-tive importance of each step of mid-level feature extrac-tion through a comprehensive cross evaluation of several types of coding modules (hard and soft vector quantization, sparse coding) and pooling schemes (by taking the aver-age, or the maximum), which obtains state-of-the-art per-formance or better on several recognition benchmarks. We show how to improve the best performing coding scheme by learning a supervised discriminative dictionary for sparse coding. We provide theoretical and empirical insight into the remarkable performance of max pooling. By teasing apart components shared by modern mid-level feature ex-tractors, our approach aims to facilitate the design of better recognition architectures.},
	Author = {Boureau, {Y. Lan} and Francis Bach and Yann LeCun and Jean Ponce},
	Booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010},
	Date-Added = {2017-07-26 16:18:01 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1109/CVPR.2010.5539963},
	Isbn = {9781424469840},
	Keywords = {Deep Learning - Image},
	Pages = {2559--2566},
	Title = {Learning mid-level features for recognition},
	Year = {2010},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/CVPR.2010.5539963}}

@article{Donahue:2013aa,
	Author = {Jeff Donahue and Yangqing Jia and Oriol Vinyals and Judy Hoffman and Ning Zhang and Eric Tzeng and Trevor Darrell},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/DonahueJVHZTD13},
	Date-Added = {2017-07-26 16:08:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Image},
	Timestamp = {Wed, 07 Jun 2017 14:40:11 +0200},
	Title = {DeCAF: {A} Deep Convolutional Activation Feature for Generic Visual Recognition},
	Url = {http://arxiv.org/abs/1310.1531},
	Volume = {abs/1310.1531},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1310.1531}}

@article{Russakovsky:2014aa,
	Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael S. Bernstein and Alexander C. Berg and Fei{-}Fei Li},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/RussakovskyDSKSMHKKBBF14},
	Date-Added = {2017-07-26 16:07:34 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Image},
	Timestamp = {Wed, 07 Jun 2017 14:41:16 +0200},
	Title = {ImageNet Large Scale Visual Recognition Challenge},
	Url = {http://arxiv.org/abs/1409.0575},
	Volume = {abs/1409.0575},
	Year = {2014},
	Bdsk-File-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAdLi4vcmVhZHMvcGFwZXJzLzE0MDkuMDU3NS5wZGZPEQGEAAAAAAGEAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8NMTQwOS4wNTc1LnBkZgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAMAAAogY3UAAAAAAAAAAAAAAAAABnBhcGVycwACAEgvOlVzZXJzOmhhcmlzdGhlbWlzdG9jbGVvdXM6RG9jdW1lbnRzOmxpYnJhcnk6cmVhZHM6cGFwZXJzOjE0MDkuMDU3NS5wZGYADgAcAA0AMQA0ADAAOQAuADAANQA3ADUALgBwAGQAZgAPABoADABNAGEAYwBpAG4AdABvAHMAaAAgAEgARAASAEZVc2Vycy9oYXJpc3RoZW1pc3RvY2xlb3VzL0RvY3VtZW50cy9saWJyYXJ5L3JlYWRzL3BhcGVycy8xNDA5LjA1NzUucGRmABMAAS8AABUAAgAa//8AAAAIAA0AGgAkAEQAAAAAAAACAQAAAAAAAAAFAAAAAAAAAAAAAAAAAAABzA==},
	Bdsk-Url-1 = {http://arxiv.org/abs/1409.0575}}

@inproceedings{Le:2011ab,
	Abstract = {Previous work on action recognition has focused on adapting hand-designed local features, such as SIFT or HOG, from static images to the video domain. In this paper, we propose using unsupervised feature learning as a way to learn features directly from video data. More specifically, we present an extension of the Independent Subspace Analysis algorithm to learn invariant spatio-temporal features from unlabeled video data. We discovered that, despite its simplicity, this method performs surprisingly well when combined with deep learning techniques such as stacking and convolution to learn hierarchical representations. By replacing hand-designed features with our learned features, we achieve classification results superior to all previous published results on the Hollywood2, UCF, KTH and YouTube action recognition datasets. On the challenging Hollywood2 and YouTube action datasets we obtain 53.3% and 75.8% respectively, which are approximately 5% better than the current best published results. Further benefits of this method, such as the ease of training and the efficiency of training and prediction, will also be discussed. You can download our code and learned spatio-temporal features here: http://ai.stanford.edu/~wzou/.},
	Author = {Q. V. Le and W. Y. Zou and S. Y. Yeung and A. Y. Ng},
	Booktitle = {CVPR 2011},
	Date-Added = {2017-07-26 16:06:00 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1109/CVPR.2011.5995496},
	Issn = {1063-6919},
	Keywords = {Deep Learning - Image},
	Month = {June},
	Pages = {3361-3368},
	Title = {Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis},
	Year = {2011},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/CVPR.2011.5995496}}

@article{Farabet:2013aa,
	Abstract = {Scene labeling consists of labeling each pixel in an image with the category of the object it belongs to. We propose a method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel. The method alleviates the need for engineered features, and produces a powerful representation that captures texture, shape, and contextual information. We report results using multiple postprocessing methods to produce the final labeling. Among those, we propose a technique to automatically retrieve, from a pool of segmentation components, an optimal set of components that best explain the scene; these components are arbitrary, for example, they can be taken from a segmentation tree or from any family of oversegmentations. The system yields record accuracies on the SIFT Flow dataset (33 classes) and the Barcelona dataset (170 classes) and near-record accuracy on Stanford background dataset (eight classes), while being an order of magnitude faster than competing approaches, producing a 320×240 image labeling in less than a second, including feature extraction.},
	Author = {C. Farabet and C. Couprie and L. Najman and Y. LeCun},
	Date-Added = {2017-07-26 16:05:21 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1109/TPAMI.2012.231},
	Issn = {0162-8828},
	Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	Keywords = {Deep Learning - Image},
	Month = {Aug},
	Number = {8},
	Pages = {1915-1929},
	Title = {Learning Hierarchical Features for Scene Labeling},
	Volume = {35},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/TPAMI.2012.231}}

@inproceedings{Taigman:2014aa,
	Abstract = {In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.},
	Author = {Y. Taigman and M. Yang and M. Ranzato and L. Wolf},
	Booktitle = {2014 IEEE Conference on Computer Vision and Pattern Recognition},
	Date-Added = {2017-07-26 16:03:20 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1109/CVPR.2014.220},
	Issn = {1063-6919},
	Keywords = {Deep Learning - Image},
	Month = {June},
	Pages = {1701-1708},
	Title = {DeepFace: Closing the Gap to Human-Level Performance in Face Verification},
	Year = {2014},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/CVPR.2014.220}}

@article{Gregor:2015aa,
	Author = {Karol Gregor and Ivo Danihelka and Alex Graves and Daan Wierstra},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/GregorDGW15},
	Date-Added = {2017-07-26 16:02:37 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Image},
	Timestamp = {Wed, 07 Jun 2017 14:40:10 +0200},
	Title = {{DRAW:} {A} Recurrent Neural Network For Image Generation},
	Url = {http://arxiv.org/abs/1502.04623},
	Volume = {abs/1502.04623},
	Year = {2015},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1502.04623}}

@inproceedings{Glorot:2011aa,
	Abstract = {While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-differentiability at zero, creating sparse representations with true zeros which seem remarkably suitable for naturally sparse data. Even though they can take advantage of semi-supervised setups with extra-unlabeled data, deep rectifier networks can reach their best performance without requiring any unsupervised pre-training on purely supervised tasks with large labeled datasets. Hence, these results can be seen as a new milestone in the attempts at understanding the difficulty in training deep but purely supervised neural networks, and closing the performance gap between neural networks learnt with and without unsupervised pre-training. [pdf]},
	Address = {Fort Lauderdale, FL, USA},
	Author = {Xavier Glorot and Antoine Bordes and Yoshua Bengio},
	Booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
	Date-Added = {2017-07-26 16:01:40 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Editor = {Geoffrey Gordon and David Dunson and Miroslav Dud{\'\i}k},
	Keywords = {Deep Learning - Theory},
	Month = {11--13 Apr},
	Pages = {315--323},
	Pdf = {http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf},
	Publisher = {PMLR},
	Series = {Proceedings of Machine Learning Research},
	Title = {Deep Sparse Rectifier Neural Networks},
	Url = {http://proceedings.mlr.press/v15/glorot11a.html},
	Volume = {15},
	Year = {2011},
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	Bdsk-Url-1 = {http://proceedings.mlr.press/v15/glorot11a.html}}

@inproceedings{Dean:2012aa,
	Acmid = {2999271},
	Address = {USA},
	Author = {Dean, Jeffrey and Corrado, Greg S. and Monga, Rajat and Chen, Kai and Devin, Matthieu and Le, Quoc V. and Mao, Mark Z. and Ranzato, Marc'Aurelio and Senior, Andrew and Tucker, Paul and Yang, Ke and Ng, Andrew Y.},
	Booktitle = {Proceedings of the 25th International Conference on Neural Information Processing Systems},
	Date-Added = {2017-07-26 16:01:13 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Keywords = {Deep Learning - Theory},
	Location = {Lake Tahoe, Nevada},
	Numpages = {9},
	Pages = {1223--1231},
	Publisher = {Curran Associates Inc.},
	Series = {NIPS'12},
	Title = {Large Scale Distributed Deep Networks},
	Url = {http://dl.acm.org/citation.cfm?id=2999134.2999271},
	Year = {2012},
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	Bdsk-Url-1 = {http://dl.acm.org/citation.cfm?id=2999134.2999271}}

@incollection{Krizhevsky:2012aa,
	Author = {Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E},
	Booktitle = {Advances in Neural Information Processing Systems 25},
	Date-Added = {2017-07-26 16:00:15 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},
	Keywords = {Deep Learning - Image},
	Pages = {1097--1105},
	Publisher = {Curran Associates, Inc.},
	Title = {ImageNet Classification with Deep Convolutional Neural Networks},
	Url = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf},
	Year = {2012},
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	Bdsk-Url-1 = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}}

@article{Goodfellow:2013aa,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2013arXiv1302.4389G},
	Archiveprefix = {arXiv},
	Author = {{Goodfellow}, I.~J. and {Warde-Farley}, D. and {Mirza}, M. and {Courville}, A. and {Bengio}, Y.},
	Date-Added = {2017-07-26 15:59:24 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Eprint = {1302.4389},
	Journal = {ArXiv e-prints},
	Keywords = {Deep Learning - Theory},
	Month = feb,
	Primaryclass = {stat.ML},
	Title = {{Maxout Networks}},
	Year = 2013,
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@article{Sermanet:2013aa,
	Author = {Pierre Sermanet and David Eigen and Xiang Zhang and Micha{\"{e}}l Mathieu and Rob Fergus and Yann LeCun},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/SermanetEZMFL13},
	Date-Added = {2017-07-26 15:58:00 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:42:22 +0200},
	Title = {OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks},
	Url = {http://arxiv.org/abs/1312.6229},
	Volume = {abs/1312.6229},
	Year = {2013},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1312.6229}}

@inproceedings{Turian:2010aa,
	Acmid = {1858721},
	Address = {Stroudsburg, PA, USA},
	Author = {Turian, Joseph and Ratinov, Lev and Bengio, Yoshua},
	Booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
	Date-Added = {2017-07-26 15:57:11 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Keywords = {Deep Learning - Word Embeddings},
	Location = {Uppsala, Sweden},
	Numpages = {11},
	Pages = {384--394},
	Publisher = {Association for Computational Linguistics},
	Series = {ACL '10},
	Title = {Word Representations: A Simple and General Method for Semi-supervised Learning},
	Url = {http://dl.acm.org/citation.cfm?id=1858681.1858721},
	Year = {2010},
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	Bdsk-Url-1 = {http://dl.acm.org/citation.cfm?id=1858681.1858721}}

@article{Shelhamer:2016aa,
	Author = {Evan Shelhamer and Jonathan Long and Trevor Darrell},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/ShelhamerLD16},
	Date-Added = {2017-07-26 15:56:23 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:41:18 +0200},
	Title = {Fully Convolutional Networks for Semantic Segmentation},
	Url = {http://arxiv.org/abs/1605.06211},
	Volume = {abs/1605.06211},
	Year = {2016},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1605.06211}}

@article{Mikolov:2013ab,
	Author = {Tomas Mikolov and Kai Chen and Greg Corrado and Jeffrey Dean},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1301-3781},
	Date-Added = {2017-07-26 13:04:57 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Word Embeddings},
	Timestamp = {Wed, 07 Jun 2017 14:42:25 +0200},
	Title = {Efficient Estimation of Word Representations in Vector Space},
	Url = {http://arxiv.org/abs/1301.3781},
	Volume = {abs/1301.3781},
	Year = {2013},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1301.3781}}

@article{Weston:2015aa,
	Author = {Jason Weston and Antoine Bordes and Sumit Chopra and Tomas Mikolov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/WestonBCM15},
	Date-Added = {2017-07-26 12:31:47 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Machine Translation},
	Timestamp = {Wed, 07 Jun 2017 14:42:32 +0200},
	Title = {Towards AI-Complete Question Answering: {A} Set of Prerequisite Toy Tasks},
	Url = {http://arxiv.org/abs/1502.05698},
	Volume = {abs/1502.05698},
	Year = {2015},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1502.05698}}

@article{Simonyan:2014aa,
	Author = {Karen Simonyan and Andrew Zisserman},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/SimonyanZ14a},
	Date-Added = {2017-07-26 12:15:35 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:41:51 +0200},
	Title = {Very Deep Convolutional Networks for Large-Scale Image Recognition},
	Url = {http://arxiv.org/abs/1409.1556},
	Volume = {abs/1409.1556},
	Year = {2014},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1409.1556}}

@article{Bahdanau:2014aa,
	Author = {Dzmitry Bahdanau and Kyunghyun Cho and Yoshua Bengio},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/BahdanauCB14},
	Date-Added = {2017-07-26 12:14:41 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Machine Translation, Deep Learning - General Intelligence},
	Timestamp = {Wed, 07 Jun 2017 14:40:19 +0200},
	Title = {Neural Machine Translation by Jointly Learning to Align and Translate},
	Url = {http://arxiv.org/abs/1409.0473},
	Volume = {abs/1409.0473},
	Year = {2014},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1409.0473}}

@article{Girshick:2015aa,
	Author = {Ross B. Girshick},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/Girshick15},
	Date-Added = {2017-07-26 10:27:10 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:40:36 +0200},
	Title = {Fast {R-CNN}},
	Url = {http://arxiv.org/abs/1504.08083},
	Volume = {abs/1504.08083},
	Year = {2015},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1504.08083}}

@inproceedings{Szegedy:2015aa,
	Abstract = {We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.},
	Author = {C. Szegedy and Wei Liu and Yangqing Jia and P. Sermanet and S. Reed and D. Anguelov and D. Erhan and V. Vanhoucke and A. Rabinovich},
	Booktitle = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	Date-Added = {2017-07-26 10:26:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {10.1109/CVPR.2015.7298594},
	Issn = {1063-6919},
	Keywords = {Deep Learning - Theory},
	Month = {June},
	Pages = {1-9},
	Title = {Going deeper with convolutions},
	Year = {2015},
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	Bdsk-Url-1 = {http://dx.doi.org/10.1109/CVPR.2015.7298594}}

@article{He:2015aa,
	Author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/HeZRS15},
	Date-Added = {2017-07-26 10:25:43 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:41:17 +0200},
	Title = {Deep Residual Learning for Image Recognition},
	Url = {http://arxiv.org/abs/1512.03385},
	Volume = {abs/1512.03385},
	Year = {2015},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1512.03385}}

@article{Cho:2014aa,
	Author = {Kyunghyun Cho and Bart van Merrienboer and {\c{C}}aglar G{\"{u}}l{\c{c}}ehre and Fethi Bougares and Holger Schwenk and Yoshua Bengio},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/ChoMGBSB14},
	Date-Added = {2017-07-26 10:23:47 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Machine Translation},
	Timestamp = {Wed, 07 Jun 2017 14:43:08 +0200},
	Title = {Learning Phrase Representations using {RNN} Encoder-Decoder for Statistical Machine Translation},
	Url = {http://arxiv.org/abs/1406.1078},
	Volume = {abs/1406.1078},
	Year = {2014},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1406.1078}}

@article{Kalchbrenner:2014aa,
	Author = {Nal Kalchbrenner and Edward Grefenstette and Phil Blunsom},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KalchbrennerGB14},
	Date-Added = {2017-07-26 10:22:42 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Machine Translation},
	Timestamp = {Wed, 07 Jun 2017 14:42:24 +0200},
	Title = {A Convolutional Neural Network for Modelling Sentences},
	Url = {http://arxiv.org/abs/1404.2188},
	Volume = {abs/1404.2188},
	Year = {2014},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1404.2188}}

@article{Kim:2014aa,
	Author = {Yoon Kim},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/Kim14f},
	Date-Added = {2017-07-26 10:21:24 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Machine Translation},
	Timestamp = {Wed, 07 Jun 2017 14:40:07 +0200},
	Title = {Convolutional Neural Networks for Sentence Classification},
	Url = {http://arxiv.org/abs/1408.5882},
	Volume = {abs/1408.5882},
	Year = {2014},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1408.5882}}

@inproceedings{Manning:2014aa,
	Abstract = {https://stanfordnlp.github.io/CoreNLP/
},
	Author = {Manning, Christopher D. and Surdeanu, Mihai and Bauer, John and Finkel, Jenny and Bethard, Steven J. and McClosky, David},
	Booktitle = {Association for Computational Linguistics (ACL) System Demonstrations},
	Date-Added = {2017-07-26 10:18:46 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Keywords = {Deep Learning - Machine Translation},
	Pages = {55--60},
	Title = {The {Stanford} {CoreNLP} Natural Language Processing Toolkit},
	Url = {http://www.aclweb.org/anthology/P/P14/P14-5010},
	Year = {2014},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://www.aclweb.org/anthology/P/P14/P14-5010}}

@inproceedings{Socher:2013aa,
	Address = {Stroudsburg, PA},
	Author = {Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D. and Ng, Andrew Y. and Potts, Christopher},
	Booktitle = {Proceedings of the 2013 Conference on {E}mpirical {M}ethods in {N}atural {L}anguage {P}rocessing},
	Date-Added = {2017-07-26 10:15:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Keywords = {Deep Learning - Machine Translation},
	Location = {Seattle, WA},
	Month = {October},
	Pages = {1631--1642},
	Publisher = {Association for Computational Linguistics},
	Title = {Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank},
	Year = {2013},
	Bdsk-File-1 = {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}}

@article{He:2014aa,
	Author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/HeZR014},
	Date-Added = {2017-07-26 10:10:42 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:41:13 +0200},
	Title = {Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition},
	Url = {http://arxiv.org/abs/1406.4729},
	Volume = {abs/1406.4729},
	Year = {2014},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1406.4729}}

@article{Collobert:2011aa,
	Author = {Ronan Collobert and Jason Weston and L{\'{e}}on Bottou and Michael Karlen and Koray Kavukcuoglu and Pavel P. Kuksa},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1103-0398},
	Date-Added = {2017-07-26 10:04:58 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Machine Translation},
	Timestamp = {Wed, 07 Jun 2017 14:43:14 +0200},
	Title = {Natural Language Processing (almost) from Scratch},
	Url = {http://arxiv.org/abs/1103.0398},
	Volume = {abs/1103.0398},
	Year = {2011},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1103.0398}}

@inproceedings{Graves:2013aa,
	Author = {A. Graves and A. r. Mohamed and G. Hinton},
	Booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
	Date-Added = {2017-07-26 10:00:42 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {10.1109/ICASSP.2013.6638947},
	Issn = {1520-6149},
	Keywords = {Deep Learning - Speech},
	Month = {May},
	Pages = {6645-6649},
	Title = {Speech recognition with deep recurrent neural networks},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/ICASSP.2013.6638947}}

@article{Hinton:2012ab,
	Author = {Geoffrey E. Hinton and Nitish Srivastava and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1207-0580},
	Date-Added = {2017-07-26 09:54:51 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:40:12 +0200},
	Title = {Improving neural networks by preventing co-adaptation of feature detectors},
	Url = {http://arxiv.org/abs/1207.0580},
	Volume = {abs/1207.0580},
	Year = {2012},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1207.0580}}

@inproceedings{Wan:2013aa,
	Abstract = {We introduce DropConnect, a generalization of DropOut, for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recoginition benchmarks can be obtained by aggregating multiple DropConnect-trained models.},
	Address = {Atlanta, Georgia, USA},
	Author = {Li Wan and Matthew Zeiler and Sixin Zhang and Yann Le Cun and Rob Fergus},
	Booktitle = {Proceedings of the 30th International Conference on Machine Learning},
	Date-Added = {2017-07-26 09:53:56 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Editor = {Sanjoy Dasgupta and David McAllester},
	Keywords = {Deep Learning - Theory},
	Month = {17--19 Jun},
	Number = {3},
	Pages = {1058--1066},
	Pdf = {http://proceedings.mlr.press/v28/wan13.pdf},
	Publisher = {PMLR},
	Series = {Proceedings of Machine Learning Research},
	Title = {Regularization of Neural Networks using DropConnect},
	Url = {http://proceedings.mlr.press/v28/wan13.html},
	Volume = {28},
	Year = {2013},
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	Bdsk-Url-1 = {http://proceedings.mlr.press/v28/wan13.html}}

@article{Kingma:2014aa,
	Author = {Diederik P. Kingma and Jimmy Ba},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KingmaB14},
	Date-Added = {2017-07-26 09:51:21 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:40:52 +0200},
	Title = {Adam: {A} Method for Stochastic Optimization},
	Url = {http://arxiv.org/abs/1412.6980},
	Volume = {abs/1412.6980},
	Year = {2014},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1412.6980}}

@article{Srivastava:2014aa,
	Author = {Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov},
	Date-Added = {2017-07-26 09:50:24 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {Journal of Machine Learning Research},
	Keywords = {Deep Learning - Theory},
	Pages = {1929-1958},
	Title = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
	Url = {http://jmlr.org/papers/v15/srivastava14a.html},
	Volume = {15},
	Year = {2014},
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	Bdsk-Url-1 = {http://jmlr.org/papers/v15/srivastava14a.html}}

@article{He:2015ab,
	Author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/HeZR015},
	Date-Added = {2017-07-26 09:48:23 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory},
	Timestamp = {Wed, 07 Jun 2017 14:41:19 +0200},
	Title = {Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification},
	Url = {http://arxiv.org/abs/1502.01852},
	Volume = {abs/1502.01852},
	Year = {2015},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1502.01852}}

@article{Nguyen:2014aa,
	Author = {Anh Mai Nguyen and Jason Yosinski and Jeff Clune},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/NguyenYC14},
	Date-Added = {2017-07-26 09:47:00 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory; Deep Learning - Image},
	Timestamp = {Wed, 07 Jun 2017 14:42:29 +0200},
	Title = {Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images},
	Url = {http://arxiv.org/abs/1412.1897},
	Volume = {abs/1412.1897},
	Year = {2014},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1412.1897}}

@article{Ioffe:2015aa,
	Author = {Sergey Ioffe and Christian Szegedy},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/IoffeS15},
	Date-Added = {2017-07-26 09:45:56 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Theory, Deep Learning - Speech_and_Video},
	Timestamp = {Wed, 07 Jun 2017 14:40:49 +0200},
	Title = {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
	Url = {http://arxiv.org/abs/1502.03167},
	Volume = {abs/1502.03167},
	Year = {2015},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1502.03167}}

@article{Hinton:2012ac,
	Abstract = {Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.},
	Author = {G. Hinton and L. Deng and D. Yu and G. E. Dahl and A. r. Mohamed and N. Jaitly and A. Senior and V. Vanhoucke and P. Nguyen and T. N. Sainath and B. Kingsbury},
	Date-Added = {2017-07-26 09:42:33 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {10.1109/MSP.2012.2205597},
	Issn = {1053-5888},
	Journal = {IEEE Signal Processing Magazine},
	Keywords = {Deep Learning - Speech},
	Month = {Nov},
	Number = {6},
	Pages = {82-97},
	Title = {Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups},
	Volume = {29},
	Year = {2012},
	Bdsk-File-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAeLi4vcmVhZHMvcGFwZXJzL1NQTV9ETk5fMTIucGRmTxEBigAAAAABigACAAAMTWFjaW50b3NoIEhEAAAAAAAAAAAAAAAAAAAAAAAAAEJEAAH/////DlNQTV9ETk5fMTIucGRmAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP////8AAAAAAAAAAAAAAAAAAQADAAAKIGN1AAAAAAAAAAAAAAAAAAZwYXBlcnMAAgBJLzpVc2VyczpoYXJpc3RoZW1pc3RvY2xlb3VzOkRvY3VtZW50czpsaWJyYXJ5OnJlYWRzOnBhcGVyczpTUE1fRE5OXzEyLnBkZgAADgAeAA4AUwBQAE0AXwBEAE4ATgBfADEAMgAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIAR1VzZXJzL2hhcmlzdGhlbWlzdG9jbGVvdXMvRG9jdW1lbnRzL2xpYnJhcnkvcmVhZHMvcGFwZXJzL1NQTV9ETk5fMTIucGRmAAATAAEvAAAVAAIAGv//AAAACAANABoAJABFAAAAAAAAAgEAAAAAAAAABQAAAAAAAAAAAAAAAAAAAdM=},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/MSP.2012.2205597}}

@article{Dahl:2012aa,
	Abstract = {We propose a novel context-dependent (CD) model for large-vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output. The deep belief network pre-training algorithm is a robust and often helpful way to initialize deep neural networks generatively that can aid in optimization and reduce generalization error. We illustrate the key components of our model, describe the procedure for applying CD-DNN-HMMs to LVSR, and analyze the effects of various modeling choices on performance. Experiments on a challenging business search dataset demonstrate that CD-DNN-HMMs can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs, with an absolute sentence accuracy improvement of 5.8% and 9.2% (or relative error reduction of 16.0% and 23.2%) over the CD-GMM-HMMs trained using the minimum phone error rate (MPE) and maximum-likelihood (ML) criteria, respectively.},
	Author = {G. E. Dahl and D. Yu and L. Deng and A. Acero},
	Date-Added = {2017-07-26 09:38:45 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {10.1109/TASL.2011.2134090},
	Issn = {1558-7916},
	Journal = {IEEE Transactions on Audio, Speech, and Language Processing},
	Keywords = {Deep Learning - Speech},
	Month = {Jan},
	Number = {1},
	Pages = {30-42},
	Title = {Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition},
	Volume = {20},
	Year = {2012},
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	Bdsk-Url-1 = {http://dx.doi.org/10.1109/TASL.2011.2134090}}

@article{Shahriari:2016aa,
	Abstract = {Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.},
	Author = {B. Shahriari and K. Swersky and Z. Wang and R. P. Adams and N. de Freitas},
	Date-Added = {2017-07-26 09:23:33 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Doi = {10.1109/JPROC.2015.2494218},
	Issn = {0018-9219},
	Journal = {Proceedings of the IEEE},
	Keywords = {Deep Learning - Unsupervised},
	Month = {Jan},
	Number = {1},
	Pages = {148-175},
	Title = {Taking the Human Out of the Loop: A Review of Bayesian Optimization},
	Volume = {104},
	Year = {2016},
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	Bdsk-Url-1 = {http://dx.doi.org/10.1109/JPROC.2015.2494218}}

@inproceedings{Glorot:2010aa,
	Abstract = {Whereas before 2006 it appears that deep multi-layer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future.  We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1.  Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence.},
	Address = {Chia Laguna Resort, Sardinia, Italy},
	Author = {Xavier Glorot and Yoshua Bengio},
	Booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
	Date-Added = {2017-07-26 09:22:32 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Editor = {Yee Whye Teh and Mike Titterington},
	Keywords = {Deep Learning - Theory},
	Month = {13--15 May},
	Pages = {249--256},
	Pdf = {http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf},
	Publisher = {PMLR},
	Series = {Proceedings of Machine Learning Research},
	Title = {Understanding the difficulty of training deep feedforward neural networks},
	Url = {http://proceedings.mlr.press/v9/glorot10a.html},
	Volume = {9},
	Year = {2010},
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	Bdsk-Url-1 = {http://proceedings.mlr.press/v9/glorot10a.html}}

@article{Erhan:2010aa,
	Acmid = {1756025},
	Author = {Erhan, Dumitru and Bengio, Yoshua and Courville, Aaron and Manzagol, Pierre-Antoine and Vincent, Pascal and Bengio, Samy},
	Date-Added = {2017-07-26 09:21:41 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Issn = {1532-4435},
	Issue_Date = {3/1/2010},
	Journal = {J. Mach. Learn. Res.},
	Keywords = {Deep Learning - Theory; Deep Learning - Unsupervised},
	Month = mar,
	Numpages = {36},
	Pages = {625--660},
	Publisher = {JMLR.org},
	Title = {Why Does Unsupervised Pre-training Help Deep Learning?},
	Url = {http://dl.acm.org/citation.cfm?id=1756006.1756025},
	Volume = {11},
	Year = {2010},
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	Bdsk-Url-1 = {http://dl.acm.org/citation.cfm?id=1756006.1756025}}

@incollection{Yosinski:2014aa,
	Author = {Jason Yosinski and Jeff Clune and Yoshua Bengio and Hod Lipson},
	Booktitle = {Advances in Neural Information Processing Systems 27},
	Date-Added = {2017-07-26 09:15:29 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Editor = {Z. Ghahramani and M. Welling and C. Cortes and N.d. Lawrence and K.q. Weinberger},
	Keywords = {Deep Learning - Theory},
	Pages = {3320--3328},
	Publisher = {Curran Associates, Inc.},
	Title = {How transferable are features in deep neural networks?},
	Url = {http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-neural-networks.pdf},
	Year = {2014},
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	Bdsk-Url-1 = {http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-neural-networks.pdf}}

@article{Schmidhuber:2015ab,
	Abstract = {Abstract In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning &amp; evolutionary computation, and indirect search for short programs encoding deep and large networks. },
	Author = {J{\"u}rgen Schmidhuber},
	Date-Added = {2017-07-26 08:11:48 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {https://doi.org/10.1016/j.neunet.2014.09.003},
	Issn = {0893-6080},
	Journal = {Neural Networks},
	Keywords = {Deep Learning - Review Papers; Deep Learning - Reinforcement Learning},
	Pages = {85 - 117},
	Title = {Deep learning in neural networks: An overview},
	Url = {http://www.sciencedirect.com/science/article/pii/S0893608014002135},
	Volume = {61},
	Year = {2015},
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	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0893608014002135},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.neunet.2014.09.003}}

@article{Bengio:2012aa,
	Author = {Yoshua Bengio and Aaron C. Courville and Pascal Vincent},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1206-5538},
	Date-Added = {2017-07-26 08:09:33 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Review Papers},
	Timestamp = {Wed, 07 Jun 2017 14:41:40 +0200},
	Title = {Unsupervised Feature Learning and Deep Learning: {A} Review and New Perspectives},
	Url = {http://arxiv.org/abs/1206.5538},
	Volume = {abs/1206.5538},
	Year = {2012},
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	Bdsk-Url-1 = {http://arxiv.org/abs/1206.5538}}

@article{Lenz:2013aa,
	Author = {Ian Lenz and Honglak Lee and Ashutosh Saxena},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1301-3592},
	Date-Added = {2017-07-26 07:35:25 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Robotics},
	Timestamp = {Wed, 07 Jun 2017 14:41:41 +0200},
	Title = {Deep Learning for Detecting Robotic Grasps},
	Url = {http://arxiv.org/abs/1301.3592},
	Volume = {abs/1301.3592},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1301.3592}}

@article{Mnih:2013aa,
	Author = {Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Alex Graves and Ioannis Antonoglou and Daan Wierstra and Martin A. Riedmiller},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/MnihKSGAWR13},
	Date-Added = {2017-07-26 07:31:53 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Games; Deep Learning - Reinforcement Learning},
	Timestamp = {Wed, 07 Jun 2017 14:43:06 +0200},
	Title = {Playing Atari with Deep Reinforcement Learning},
	Url = {http://arxiv.org/abs/1312.5602},
	Volume = {abs/1312.5602},
	Year = {2013},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1312.5602}}

@inproceedings{Rifai:2011aa,
	Author = {Salah Rifai and Pascal Vincent and Xavier Muller and Xavier Glorot and Yoshua Bengio},
	Booktitle = {Proceedings of the Twenty-eight International Conference on Machine Learning (ICML'11},
	Date-Added = {2017-07-26 07:30:33 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Keywords = {Deep Learning - Unsupervised},
	Title = {Contracting auto-encoders: Explicit invariance during feature extraction},
	Year = {2011},
	Bdsk-File-1 = {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}}

@inproceedings{Coates:2011aa,
	Abstract = {A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made on benchmark datasets like NORB and CIFAR-10 by employing increasingly complex unsupervised learning algorithms and deep models. In this paper, however, we show that several simple factors, such as the number of hidden nodes in the model, may be more important to achieving high performance than the learning algorithm or the depth of the model. Specifically, we will apply several off-the-shelf feature learning algorithms (sparse auto-encoders, sparse RBMs, K-means clustering, and Gaussian mixtures) to CIFAR-10, NORB, and STL datasets using only single-layer networks. We then present a detailed analysis of the effect of changes in the model setup: the receptive field size, number of hidden nodes (features), the step-size (``stride'') between extracted features, and the effect of whitening. Our results show that large numbers of hidden nodes and dense feature extraction are critical to achieving high performance - so critical, in fact, that when these parameters are pushed to their limits, we achieve state-of-the-art performance on both CIFAR-10 and NORB using only a single layer of features. More surprisingly, our best performance is based on K-means clustering, which is extremely fast, has no hyper-parameters to tune beyond the model structure itself, and is very easy to implement. Despite the simplicity of our system, we achieve accuracy beyond all previously published results on the CIFAR-10 and NORB datasets (79.6% and 97.2% respectively).  [pdf]},
	Address = {Fort Lauderdale, FL, USA},
	Author = {Adam Coates and Andrew Ng and Honglak Lee},
	Booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
	Date-Added = {2017-07-26 07:29:01 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Editor = {Geoffrey Gordon and David Dunson and Miroslav Dud{\'\i}k},
	Keywords = {Deep Learning - Unsupervised},
	Month = {11--13 Apr},
	Pages = {215--223},
	Pdf = {http://proceedings.mlr.press/v15/coates11a/coates11a.pdf},
	Publisher = {PMLR},
	Series = {Proceedings of Machine Learning Research},
	Title = {An Analysis of Single-Layer Networks in Unsupervised Feature Learning},
	Url = {http://proceedings.mlr.press/v15/coates11a.html},
	Volume = {15},
	Year = {2011},
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	Bdsk-Url-1 = {http://proceedings.mlr.press/v15/coates11a.html}}

@article{Jia:2014aa,
	Author = {Yangqing Jia and Evan Shelhamer and Jeff Donahue and Sergey Karayev and Jonathan Long and Ross B. Girshick and Sergio Guadarrama and Trevor Darrell},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Software},
	Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
	Url = {http://arxiv.org/abs/1408.5093},
	Volume = {abs/1408.5093},
	Year = {2014},
	Bdsk-File-1 = {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}}

@article{Bastien:2012aa,
	Author = {Fr{\'{e}}d{\'{e}}ric Bastien and Pascal Lamblin and Razvan Pascanu and James Bergstra and Ian J. Goodfellow and Arnaud Bergeron and Nicolas Bouchard and David Warde{-}Farley and Yoshua Bengio},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Software},
	Title = {Theano: new features and speed improvements},
	Url = {http://arxiv.org/abs/1211.5590},
	Volume = {abs/1211.5590},
	Year = {2012},
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@article{Vedaldi:2014aa,
	Author = {Andrea Vedaldi and Karel Lenc},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Software},
	Title = {MatConvNet - Convolutional Neural Networks for {MATLAB}},
	Url = {http://arxiv.org/abs/1412.4564},
	Volume = {abs/1412.4564},
	Year = {2014},
	Bdsk-File-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxAdLi4vcmVhZHMvcGFwZXJzLzE0MTIuNDU2NC5wZGZPEQGEAAAAAAGEAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8NMTQxMi40NTY0LnBkZgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAAAAAAAAAAAAABAAMAAAogY3UAAAAAAAAAAAAAAAAABnBhcGVycwACAEgvOlVzZXJzOmhhcmlzdGhlbWlzdG9jbGVvdXM6RG9jdW1lbnRzOmxpYnJhcnk6cmVhZHM6cGFwZXJzOjE0MTIuNDU2NC5wZGYADgAcAA0AMQA0ADEAMgAuADQANQA2ADQALgBwAGQAZgAPABoADABNAGEAYwBpAG4AdABvAHMAaAAgAEgARAASAEZVc2Vycy9oYXJpc3RoZW1pc3RvY2xlb3VzL0RvY3VtZW50cy9saWJyYXJ5L3JlYWRzL3BhcGVycy8xNDEyLjQ1NjQucGRmABMAAS8AABUAAgAa//8AAAAIAA0AGgAkAEQAAAAAAAACAQAAAAAAAAAFAAAAAAAAAAAAAAAAAAABzA==}}

@article{Abadi:2016aa,
	Author = {Mart{\'{\i}}n Abadi and Ashish Agarwal and Paul Barham and Eugene Brevdo and Zhifeng Chen and Craig Citro and Gregory S. Corrado and Andy Davis and Jeffrey Dean and Matthieu Devin and Sanjay Ghemawat and Ian J. Goodfellow and Andrew Harp and Geoffrey Irving and Michael Isard and Yangqing Jia and Rafal J{\'{o}}zefowicz and Lukasz Kaiser and Manjunath Kudlur and Josh Levenberg and Dan Man{\'{e}} and Rajat Monga and Sherry Moore and Derek Gordon Murray and Chris Olah and Mike Schuster and Jonathon Shlens and Benoit Steiner and Ilya Sutskever and Kunal Talwar and Paul A. Tucker and Vincent Vanhoucke and Vijay Vasudevan and Fernanda B. Vi{\'{e}}gas and Oriol Vinyals and Pete Warden and Martin Wattenberg and Martin Wicke and Yuan Yu and Xiaoqiang Zheng},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Software},
	Title = {TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems},
	Url = {http://arxiv.org/abs/1603.04467},
	Volume = {abs/1603.04467},
	Year = {2016},
	Bdsk-File-1 = {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}}

@article{Vincent:2010aa,
	Author = {Vincent, Pascal and Larochelle, Hugo and Lajoie, Isabelle and Bengio, Yoshua and Manzagol, Pierre-Antoine},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {The Journal of Machine Learning Research},
	Keywords = {Deep Learning - Unsupervised},
	Pages = {3371--3408},
	Title = {Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion},
	Url = {http://www.jmlr.org/papers/volume11/vincent10a/vincent10a.pdf},
	Volume = {11},
	Year = {2010},
	Bdsk-File-1 = {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}}

@inbook{Hinton:2012aa,
	Abstract = {Restricted Boltzmann machines (RBMs) have been used as generative models of many different types of data. RBMs are usually trained using the contrastive divergence learning procedure. This requires a certain amount of practical experience to decide how to set the values of numerical meta-parameters. Over the last few years, the machine learning group at the University of Toronto has acquired considerable expertise at training RBMs and this guide is an attempt to share this expertise with other machine learning researchers.},
	Author = {Hinton, Geoffrey E.},
	Booktitle = {Neural Networks: Tricks of the Trade: Second Edition},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Doi = {10.1007/978-3-642-35289-8_32},
	Editor = {Montavon, Gr{\'e}goire and Orr, Genevi{\`e}ve B. and M{\"u}ller, Klaus-Robert},
	Isbn = {978-3-642-35289-8},
	Keywords = {Deep Learning - Unsupervised},
	Pages = {599--619},
	Publisher = {Springer Berlin Heidelberg},
	Title = {A Practical Guide to Training Restricted Boltzmann Machines},
	Url = {https://doi.org/10.1007/978-3-642-35289-8_32},
	Year = {2012},
	Bdsk-File-1 = {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}}

@article{Le:2011aa,
	Author = {Quoc V. Le and Rajat Monga and Matthieu Devin and Greg Corrado and Kai Chen and Marc'Aurelio Ranzato and Jeffrey Dean and Andrew Y. Ng},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - Unsupervised},
	Title = {Building high-level features using large scale unsupervised learning},
	Url = {https://arxiv.org/abs/1112.6209},
	Year = {2011},
	Bdsk-File-1 = {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}}

@article{Mnih:2015aa,
	Author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A. and Veness, Joel and Bellemare, Marc G. and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K. and Ostrovski, Georg and et al.},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1038/nature14236},
	Issn = {1476-4687},
	Journal = {Nature},
	Keywords = {Deep Learning - Games; Deep Learning - Reinforcement Learning},
	Number = {7540},
	Pages = {529--533},
	Publisher = {Springer Nature},
	Title = {Human-level control through deep reinforcement learning},
	Url = {http://dx.doi.org/10.1038/nature14236},
	Volume = {518},
	Year = {2015},
	Bdsk-File-1 = {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}}

@article{Wang:2013aa,
	Author = {Wang, Heng and Schmid, Cordelia},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:49 +0000},
	Doi = {10.1109/iccv.2013.441},
	Isbn = {978-1-4799-2840-8},
	Journal = {2013 IEEE International Conference on Computer Vision},
	Keywords = {Deep Learning - Video},
	Month = {Dec},
	Publisher = {IEEE},
	Title = {Action Recognition with Improved Trajectories},
	Url = {http://dx.doi.org/10.1109/ICCV.2013.441},
	Year = {2013},
	Bdsk-File-1 = {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}}

@article{Bergstra:2012aa,
	Author = {James Bergstra and Yoshua Bengio},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {Journal of Machine Learning Research},
	Keywords = {Deep Learning - Theory},
	Pages = {281--305},
	Title = {Random Search for Hyper-Parameter Optimization},
	Volume = {13},
	Year = {2012},
	Bdsk-File-1 = {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}}

@article{Hinton:2014aa,
	Abstract = {A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.},
	Author = {Geoffrey Hinton and Oriol Vinyals and Jeff Dean},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Keywords = {Deep Learning - Theory},
	Title = {Distilling the Knowledge in a Neural Network},
	Url = {https://arxiv.org/abs/1503.02531},
	Year = {2014},
	Bdsk-File-1 = {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}}

@article{LeCun:2015aa,
	Author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
	Date-Added = {2017-07-25 22:28:55 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {10.1038/nature14539},
	Issn = {1476-4687},
	Journal = {Nature},
	Keywords = {Deep Learning - Review Papers},
	Month = {May},
	Number = {7553},
	Pages = {436--444},
	Publisher = {Springer Nature},
	Title = {Deep learning},
	Url = {http://dx.doi.org/10.1038/nature14539},
	Volume = {521},
	Year = {2015},
	Bdsk-File-1 = {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}}

@article{Lazaridou:2016aa,
	Author = {Angeliki Lazaridou and Alexander Peysakhovich and Marco Baroni},
	Date-Added = {2017-07-23 17:04:29 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1612.07182},
	Keywords = {Deep Learning - General Intelligence, Deep Learning - Language},
	Month = {12},
	Title = {Multi-Agent Cooperation and the Emergence of (Natural) Language},
	Url = {https://arxiv.org/abs/1612.07182},
	Year = {2016},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {https://arxiv.org/abs/1612.07182}}

@article{Baroni:2017ad,
	Author = {Marco Baroni and Armand Joulin and Allan Jabri and Germ{\`a}n Kruszewski and Angeliki Lazaridou and Klemen Simonic and Tomas Mikolov},
	Date-Added = {2017-07-23 17:04:29 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1701.08954},
	Keywords = {Deep Learning - General Intelligence},
	Month = {01},
	Title = {CommAI: Evaluating the first steps towards a useful general AI},
	Url = {https://arxiv.org/abs/1701.08954},
	Year = {2017},
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	Bdsk-Url-1 = {https://arxiv.org/abs/1701.08954}}

@article{Boleda:2017aa,
	Author = {Gemma Boleda and Sebastian Pad{\'o} and Nghia The Pham and Marco Baroni},
	Date-Added = {2017-07-23 17:04:28 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1702.01815},
	Keywords = {Deep Learning - Language},
	Month = {02},
	Title = {Living a discrete life in a continuous world: Reference with distributed representations},
	Url = {https://arxiv.org/abs/1702.01815},
	Year = {2017},
	Bdsk-Url-1 = {https://arxiv.org/abs/1702.01815}}

@article{Rojas-Carulla:2017aa,
	Author = {Mateo Rojas-Carulla and Marco Baroni and David Lopez-Paz},
	Date-Added = {2017-07-23 17:04:26 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1702.07306},
	Keywords = {Deep Learning - Language},
	Month = {02},
	Title = {Causal Discovery Using Proxy Variables},
	Url = {https://arxiv.org/abs/1702.07306},
	Year = {2017},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {https://arxiv.org/abs/1702.07306}}

@article{Herbelot:2017aa,
	Author = {Aurelie Herbelot and Marco Baroni},
	Date-Added = {2017-07-23 17:04:18 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Eprint = {1707.06556},
	Journal = {EMNLP 2017},
	Keywords = {Deep Learning - General Intelligence, Deep Learning - Language},
	Month = {07},
	Title = {High-risk learning: acquiring new word vectors from tiny data},
	Url = {https://arxiv.org/abs/1707.06556},
	Year = {2017},
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	Bdsk-Url-1 = {https://arxiv.org/abs/1707.06556}}

@article{Mikolov:2015aa,
	Author = {Tomas Mikolov and Armand Joulin and Marco Baroni},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/MikolovJB15},
	Date-Added = {2017-07-23 16:52:08 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - General Intelligence},
	Timestamp = {Wed, 07 Jun 2017 14:41:50 +0200},
	Title = {A Roadmap towards Machine Intelligence},
	Url = {http://arxiv.org/abs/1511.08130},
	Volume = {abs/1511.08130},
	Year = {2015},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1511.08130}}

@book{Hutter:2005ab,
	Abstract = {This book presents sequential decision theory from a
                  novel algorithmic information theory perspective. While the former
                  theory is suited for active agents in known environments, the
                  latter is suited for passive prediction of unknown environments.
                  The book introduces these two well-known but very different ideas
                  and removes the limitations by unifying them to one parameter-free
                  theory of an optimal reinforcement learning agent interacting with
                  an arbitrary unknown world. Most if not all AI problems can easily
                  be formulated within this theory, which reduces the conceptual
                  problems to pure computational ones. Considered problem classes
                  include sequence prediction, strategic games, function
                  minimization, reinforcement and supervised learning. Formal
                  definitions of intelligence order relations, the horizon problem
                  and relations to other approaches to AI are discussed. One
                  intention of this book is to excite a broader AI audience about
                  abstract algorithmic information theory concepts, and conversely
                  to inform theorists about exciting applications to AI.},
	Address = {Berlin},
	Author = {Marcus Hutter},
	Date-Added = {2017-07-23 16:43:28 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1007/b138233},
	Isbn = {3-540-22139-5},
	Isbn-Online = {978-3-540-26877-2},
	Keywords = {Deep Learning - General Intelligence; Deep Learning - Reinforcement Learning},
	Pages = {300 pages},
	Publisher = {Springer},
	Title = {Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability},
	Url = {http://www.hutter1.net/ai/uaibook.htm},
	Year = {2005},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://www.hutter1.net/ai/uaibook.htm},
	Bdsk-Url-2 = {http://dx.doi.org/10.1007/b138233}}

@article{Rosa:2016aa,
	Author = {Marek Rosa and Jan Feyereisl},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/RosaFC16},
	Date-Added = {2017-07-23 16:02:58 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {CoRR},
	Keywords = {Deep Learning - General Intelligence},
	Timestamp = {Wed, 07 Jun 2017 14:41:51 +0200},
	Title = {A Framework for Searching for General Artificial Intelligence},
	Url = {http://arxiv.org/abs/1611.00685},
	Volume = {abs/1611.00685},
	Year = {2016},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://arxiv.org/abs/1611.00685}}

@article{Hutton:2009aa,
	Author = {Hutton, Chloe and Draganski, Bogdan and Ashburner, John and Weiskopf, Nikolaus},
	Date-Added = {2017-07-11 08:41:36 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1016/j.neuroimage.2009.06.043},
	Issn = {1053-8119},
	Journal = {NeuroImage},
	Keywords = {Brain},
	Number = {2},
	Pages = {371--380},
	Publisher = {Elsevier BV},
	Title = {A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging},
	Url = {http://dx.doi.org/10.1016/j.neuroimage.2009.06.043},
	Volume = {48},
	Year = {2009},
	Bdsk-File-1 = {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}}

@article{Tzourio-Mazoyer:2002aa,
	Abstract = {An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.},
	Author = {Tzourio-Mazoyer, N and Landeau, B and Papathanassiou, D and Crivello, F and Etard, O and Delcroix, N and Mazoyer, B and Joliot, M},
	Date-Added = {2017-07-11 08:41:36 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1006/nimg.2001.0978},
	Journal = {Neuroimage},
	Journal-Full = {NeuroImage},
	Keywords = {Brain},
	Mesh = {Brain; Brain Mapping; Computer Graphics; Dominance, Cerebral; Humans; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Reference Values},
	Month = {Jan},
	Number = {1},
	Pages = {273-89},
	Pmid = {11771995},
	Pst = {ppublish},
	Title = {Automated anatomical labeling of activations in {SPM} using a macroscopic anatomical parcellation of the {MNI MRI} single-subject brain},
	Volume = {15},
	Year = {2002},
	Bdsk-File-1 = {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}}

@article{Sowell:2006aa,
	Author = {Sowell, E. R. and Peterson, B. S. and Kan, E. and Woods, R. P. and Yoshii, J. and Bansal, R. and Xu, D. and Zhu, H. and Thompson, P. M. and Toga, A. W.},
	Date-Added = {2017-07-11 08:41:36 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1093/cercor/bhl066},
	Issn = {1460-2199},
	Journal = {Cerebral Cortex},
	Keywords = {Brain},
	Month = {Aug},
	Number = {7},
	Pages = {1550--1560},
	Publisher = {Oxford University Press (OUP)},
	Title = {Sex Differences in Cortical Thickness Mapped in 176 Healthy Individuals between 7 and 87 Years of Age},
	Url = {http://dx.doi.org/10.1093/cercor/bhl066},
	Volume = {17},
	Year = {2006},
	Bdsk-File-1 = {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}}

@article{Brown:2009aa,
	Author = {Brown, Steven and Laird, Angela R. and Pfordresher, Peter Q. and Thelen, Sarah M. and Turkeltaub, Peter and Liotti, Mario},
	Date-Added = {2017-07-11 08:41:36 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1016/j.bandc.2008.12.006},
	Issn = {0278-2626},
	Journal = {Brain and Cognition},
	Keywords = {Brain},
	Month = {Jun},
	Number = {1},
	Pages = {31--41},
	Publisher = {Elsevier BV},
	Title = {The somatotopy of speech: Phonation and articulation in the human motor cortex},
	Url = {http://dx.doi.org/10.1016/j.bandc.2008.12.006},
	Volume = {70},
	Year = {2009},
	Bdsk-File-1 = {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}}

@misc{Angelopoulou:2018aa,
	Author = {Georgia Angelopoulou and Dimitrios Kasselimis and Maria Varkanitsa and Panagiotis Fotiadis and Charalambos Themistocleous and Dimitrios Tsolakopoulos and Christally Grillou and Foteini Christidi and Efstratios Karavasilis and George Argiropoulos and George Velonakis and Sofia Karanassou and Zoi Nikitopoulou and Petros Roussos and Dionysios Goutsos and Nikolaos Kelekis and Ioannis Evdokimidis and Constantin Potagas},
	Date-Added = {2017-07-11 08:41:36 +0000},
	Date-Modified = {2018-01-24 16:52:46 +0000},
	Keywords = {Brain},
	Title = {Effects of cortical thickness on pause duration in neurotypical adults' speech: Evidence for the role of the left middle temporal gyrus in lexical retrieval},
	Year = {submitted},
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@article{Mack:2015aa,
	Author = {Jennifer E. Mack and Sarah D. Chandler and Aya Meltzer-Asscher amd Emily Rogalski and Sandra Weintraub and Marsel Mesulam and Cynthia K. Thompson},
	Date-Added = {2017-07-11 08:41:36 +0000},
	Date-Modified = {2018-01-24 16:52:46 +0000},
	Doi = {http://dx.doi.org/10.1016/j.neuropsychologia.2015.08.019},
	Journal = {Neuropsychologia},
	Keywords = {Brain},
	Pages = {211--222},
	Title = {What do pauses in narrative production reveal about the nature of word retrieval deficits in PPA?},
	Volume = {77},
	Year = {2015},
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@article{Fischl:2000aa,
	Author = {Fischl, B. and Dale, A. M.},
	Date-Added = {2017-07-11 08:41:36 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1073/pnas.200033797},
	Issn = {1091-6490},
	Journal = {Proceedings of the National Academy of Sciences},
	Keywords = {Brain},
	Month = {Sep},
	Number = {20},
	Pages = {11050--11055},
	Publisher = {Proceedings of the National Academy of Sciences},
	Title = {Measuring the thickness of the human cerebral cortex from magnetic resonance images},
	Url = {http://dx.doi.org/10.1073/pnas.200033797},
	Volume = {97},
	Year = {2000},
	Bdsk-File-1 = {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}}

@article{Thambisetty:2010aa,
	Author = {Thambisetty, Madhav and Wan, Jing and Carass, Aaron and An, Yang and Prince, Jerry L. and Resnick, Susan M.},
	Date-Added = {2017-07-11 08:41:36 +0000},
	Date-Modified = {2018-01-24 16:52:46 +0000},
	Doi = {10.1016/j.neuroimage.2010.04.258},
	Issn = {1053-8119},
	Journal = {NeuroImage},
	Keywords = {Brain},
	Month = {Oct},
	Number = {4},
	Pages = {1215--1223},
	Publisher = {Elsevier BV},
	Title = {Longitudinal changes in cortical thickness associated with normal aging},
	Url = {http://dx.doi.org/10.1016/j.neuroimage.2010.04.258},
	Volume = {52},
	Year = {2010},
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@article{Christiansen:2008aa,
	Author = {Christiansen, Morten H. and Chater, Nick},
	Date-Added = {2017-07-06 14:58:41 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1017/S0140525X08004998},
	Journal = {Behavioral and Brain Sciences},
	Keywords = {Brain},
	Number = {5},
	Pages = {489--509},
	Publisher = {Cambridge University Press},
	Title = {Language as shaped by the brain},
	Volume = {31},
	Year = {2008},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1017/S0140525X08004998}}

@article{Wildgruber:2006aa,
	Abstract = {During acoustic communication in humans, information about a speaker's emotional state is predominantly conveyed by modulation of the tone of voice (emotional or affective prosody). Based on lesion data, a right hemisphere superiority for cerebral processing of emotional prosody has been assumed. However, the available clinical studies do not yet provide a coherent picture with respect to interhemispheric lateralization effects of prosody recognition and intrahemispheric localization of the respective brain regions. To further delineate the cerebral network engaged in the perception of emotional tone, a series of experiments was carried out based upon functional magnetic resonance imaging (fMRI). The findings obtained from these investigations allow for the separation of three successive processing stages during recognition of emotional prosody: (1) extraction of suprasegmental acoustic information predominantly subserved by right-sided primary and higher order acoustic regions; (2) representation of meaningful suprasegmental acoustic sequences within posterior aspects of the right superior temporal sulcus; (3) explicit evaluation of emotional prosody at the level of the bilateral inferior frontal cortex. Moreover, implicit processing of affective intonation seems to be bound to subcortical regions mediating automatic induction of specific emotional reactions such as activation of the amygdala in response to fearful stimuli. As concerns lower level processing of the underlying suprasegmental acoustic cues, linguistic and emotional prosody seem to share the same right hemisphere neural resources. Explicit judgment of linguistic aspects of speech prosody, however, appears to be linked to left-sided language areas whereas bilateral orbitofrontal cortex has been found involved in explicit evaluation of emotional prosody. These differences in hemispheric lateralization effects might explain that specific impairments in nonverbal emotional communication subsequent to focal brain lesions are relatively rare clinical observations as compared to the more frequent aphasic disorders.},
	Author = {D. Wildgruber and H. Ackermann and B. Kreifelts and T. Ethofer},
	Date-Added = {2017-07-06 14:26:59 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {http://dx.doi.org/10.1016/S0079-6123(06)56013-3},
	Issn = {0079-6123},
	Journal = {Progress in Brain Research},
	Keywords = {Brain; Deep Learning - Language; Deep Learning - General Intelligence},
	Note = {Understanding Emotions},
	Pages = {249 - 268},
	Title = {Cerebral processing of linguistic and emotional prosody: fMRI studies},
	Url = {http://www.sciencedirect.com/science/article/pii/S0079612306560133},
	Volume = {156},
	Year = {2006},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0079612306560133},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/S0079-6123(06)56013-3}}

@article{Fedorenko:2015aa,
	Author = {Evelina Fedorenko and Po-Jang Hsieh and Zuzanna Balewski},
	Date-Added = {2017-07-06 11:39:28 +0000},
	Date-Modified = {2018-01-24 16:52:46 +0000},
	Doi = {10.1080/01690965.2013.861917},
	Eprint = {http://dx.doi.org/10.1080/01690965.2013.861917},
	Journal = {Language, Cognition and Neuroscience},
	Keywords = {Brain},
	Number = {1-2},
	Pages = {120-148},
	Title = {A possible functional localiser for identifying brain regions sensitive to sentence-level prosody},
	Url = {http://dx.doi.org/10.1080/01690965.2013.861917},
	Volume = {30},
	Year = {2015},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1080/01690965.2013.861917}}

@article{Mulder:2015aa,
	Author = {Wim De Mulder and Steven Bethard and Marie-Francine Moens},
	Date-Added = {2017-06-27 13:05:45 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Doi = {http://dx.doi.org/10.1016/j.csl.2014.09.005},
	Journal = {Computer Speech and Language},
	Keywords = {Deep Learning - Language; Deep Learning - Review Papers},
	Pages = {61--98},
	Title = {A survey on the application of recurrent neural networks to statistical language modeling},
	Volume = {30},
	Year = {2015},
	Bdsk-File-1 = {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}}

@inbook{Wellman:1998aa,
	Address = {Hoboken, NJ, US},
	Author = {Henry M. Wellman and Susan A. Gelman},
	Booktitle = {Handbook of child psychology: Cognition, perception, and language},
	Date-Added = {2017-06-25 15:31:24 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Editor = {William Damon and Richard M. Lerner and Deanna Kuhn and Robert S. Siegler},
	Keywords = {Deep Learning - Language},
	Pages = {523-573},
	Publisher = {John Wiley \& Sons Inc},
	Title = {Knowledge Acquisition in Foundational Domains},
	Volume = {2},
	Year = {1998},
	Bdsk-File-1 = {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}}

@article{Le:2015aa,
	Author = {Quoc V. Le and Navdeep Jaitly and Geoffrey E. Hinton},
	Date-Added = {2017-06-25 13:28:30 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {arXiv:1504.00941v2},
	Keywords = {Deep Learning - Language; Deep Learning - Theory},
	Title = {A Simple Way to Initialize Recurrent Networks of Rectified Linear Units},
	Year = {2015},
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@article{Manning:2015aa,
	Author = {Manning, Christopher},
	Date-Added = {2017-06-25 13:28:30 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {Association for Computational Linguistics},
	Keywords = {Deep Learning - Language},
	Title = {Last Words. {C}omputational Linguistics and Deep Learning},
	Year = {2015},
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@article{McClelland:2010aa,
	Author = {McClelland, James L. and Botvinick, Matthew M. and Noelle, David C. and Plaut, David C. and Rogers, Timothy T. and Seidenberg, Mark S. and Smith, Linda B.},
	Date-Added = {2017-06-25 13:28:08 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1016/j.tics.2010.06.002},
	Issn = {1364-6613},
	Journal = {Trends in Cognitive Sciences},
	Keywords = {Deep Learning - Language},
	Number = {8},
	Pages = {348--356},
	Publisher = {Elsevier BV},
	Title = {Letting structure emerge: connectionist and dynamical systems approaches to cognition},
	Url = {http://dx.doi.org/10.1016/j.tics.2010.06.002},
	Volume = {14},
	Year = {2010},
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@article{Griffiths:2010aa,
	Author = {Griffiths, Thomas L. and Chater, Nick and Kemp, Charles and Perfors, Amy and Tenenbaum, Joshua B.},
	Date-Added = {2017-06-25 13:28:08 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Doi = {10.1016/j.tics.2010.05.004},
	Issn = {1364-6613},
	Journal = {Trends in Cognitive Sciences},
	Keywords = {Deep Learning - Language},
	Number = {8},
	Pages = {357--364},
	Publisher = {Elsevier BV},
	Title = {Probabilistic models of cognition: exploring representations and inductive biases},
	Url = {http://dx.doi.org/10.1016/j.tics.2010.05.004},
	Volume = {14},
	Year = {2010},
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@article{Szepesvari:2018aa,
	Author = {Csaba Szepesv{\'a}ri},
	Date-Added = {2017-06-25 13:28:08 +0000},
	Date-Modified = {2018-01-24 16:52:48 +0000},
	Journal = {Synthesis Lectures on Artificial Intelligence and Machine Learning},
	Keywords = {Deep Learning - Language; Deep Learning - Reinforcement Learning},
	Title = {Algorithms for Reinforcement Learning},
	Year = {Draft},
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@article{Lake:2016aa,
	Abstract = {Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.},
	Author = {Brenden M. Lake and Tomer D. Ullman and Joshua B. Tenenbaum and Samuel J. Gershman},
	Date-Added = {2017-06-15 19:36:07 +0000},
	Date-Modified = {2018-01-24 16:52:47 +0000},
	Journal = {Behavioral and Brain Sciences},
	Keywords = {Deep Learning - General Intelligence},
	Pages = {1-101},
	Rating = {5},
	Read = {1},
	Title = {Building Machines That Learn and Think Like People},
	Volume = {24},
	Year = {2016},
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