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1
FieldTypeIDAuthorsYearTitlePublishmentPDFFrom Doc. (ID)Added dateRead start(date)Read end(date)Key words
2
machine learning1
Ilya Sutskever, James Martens, Geoffrey Hinton
2011Generating Text with Recurrent Neural Networks[ICML 2011]6/12/2017
3
machine learning2
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, Christian Jauvin
2003A Neural Probabilistic Language Models
Journal of Machine Learning Research
6/12/2017
4
machine learning3
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, Jeff Dean
2013Distributed representations of words and phrases and their compositionality
[NIPS 2013] Advances in Neural Information Processing Systems 26
6/12/2017
5
artificial intelligence
4Alan M. Turing1950Computing Machinery and IntelligenceMind 49: 433-4606/12/2017
imitation game, Turing test
6
machine learning5
Marcus Liwicki, Alex Graves, Horst Bunke, J¨urgen Schmidhuber
2007A Novel Approach to On-Line Handwriting Recognition Based on Bidirectional Long Short-Term Memory Networks
[ICDAR 2007] Proceedings of the 9th International Conference on Document Analysis and Recognition
6/12/2017
RNN, LSTM(Long Short Term Memory), pattern recognition
7
machine learning6
David Rumelhart, Geoffrey Hinton, Ronald Williams
1986Learning representations by backpropagating errorsNature6/17/2017backpropagtion
8
72010From Frequency to Meaning: Vector Space Models of Semantics
Journal of Artificial Intelligence Research
6/18/2017
9
machine learning8
Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
2013Efficient Estimation of Word Representations in Vector SpaceICLR Workshopword2vec6/18/2017
skip-gram model
10
artificial intelligence
9
Peratham Wiriyathammabhum, Douglas Summers-Stay, Cornelia Fermüller, Yiannis Aloimonos
2017Computer Vision and Natural Language Processing: Recent Approaches in Multimedia and Robotics[CSUR] ACM Computing Surveyskkmin7/17/20177/13/2017
survey, computer vision, natural language processing
11
10
McCulloch, W. S., Pitts, W.
1943A logical calculus of ideas immanent in nervous activityBulletin of Mathematical BiophysicsDeep Learning7/19/2017perceptron
12
11Hebb, D. O.1949The Organization of BehaviorWiley, New York7/19/2017
13
machine learning12
Hinton, G. E., Osindero, S., and Teh, Y.
2006A fast learning algorithm for deep belief netsNeural ComputationDeep Learning7/19/2017
14
machine learning13
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H.
2006Greedy layer-wise training of deep networksNIPS 2006Deep Learning7/19/2017
15
machine learning14
Ranzato, M., Poultney, C., Chopra, S., and LeCun, Y.
2006Efficient learning of sparse representations with an energy-based modelNIPS 2006Deep Learning7/19/2017
16
15
Hinton, G. E. and Shallice, T.
1991Lesioning an attractor network: investigations of acquired dyslexiaPsychological review7/19/2017
a kind of neural networks used for machine learning used to understand brain function
17
16Jordan, M. I.1998Learning in Graphical ModelsKluwer7/19/2017
graphical model
18
17
James Martens, Ilya Sutskever
2011Learning Recurrent Neural Networks with Hessian-Free Optimization ICML 2011NNML course7/25/2017
RNN, Echo-State-Network
19
18
Herbert Jaeger, Harald Haas
2004Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless CommunicationScience177/25/2017
20
machine learning19
Yoshua Bengio, Aaron Courville, Pascal Vincent
2014Representation Learning: A Review and New Perspectivesarxiv
https://stats.stackexchange.com/questions/95480/comparing-different-deep-learning-models
7/26/2017
representation learning, review
21
20
Dayan, P., Hinton, G. E. E., Neal, R. M. M., & Zemel, R. S. S.
1995The Helmholtz machineNeural Computation딥러닝 장병탁7/27/2017
22
21G. Cybenko1989Approximation by Superpositions of a Sigmoidal Function
Mathematics of Control, Signals, and Systems
딥러닝 장병탁7/27/2017
Prove that one hidden layer is enough.
23
22Hornik, K.1991Approximation capabilities of multilayer feedforward networksNeural Networks딥러닝 장병탁7/27/2017
Prove that one hidden layer is enough.
24
sequence model23
Ilya Sutskever, Oriol Vinyals , Quoc V. Le
2014Sequence to Sequence Learning with Neural NetworksNIPS 20148/27/2017
25
sequence model24
S. Hochreiter, J. Schmidhuber
1997Long short-term memoryNeural Computation8/27/2017
26
dataset25
Yuncheng Li, Yale Song, Liangliang Cao, Joel Tetreault, Larry Goldberg, Alejandro Jaimes, Jiebo Luo
2016TGIF: A New Dataset and Benchmark on Animated GIF DescriptionCVPR 20168/31/2017
{(GIF, description)} dataset
27
language model26
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Raquel Urtasun, Antonio Torralba, Sanja Fidler
2015Skip-Thought VectorsNIPS 20158/31/2017
sentence2vector
28
language model27
Quoc V. Le, Tomas Mikolov
2014Distributed Representations of Sentences and DocumentsICML 20148/31/2017
29
28
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield,Phil Blunsom, Koray Kavukcuoglu, Demis Hassabis
2016Hybrid computing using a neural network with dynamic external memoryNature9/1/2017
30
29
Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio
2014Identifying and attacking the saddle point problem in high-dimensional non-convex optimizationNIPS 20149/18/2017google search
31
30
Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio
2014Identifying and attacking the saddle point problem in high-dimensional non-convex optimizationarXiv:1406.25729/18/2017
32
dropout31
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R.
2014Dropout: A Simple Way to Prevent Neural Networks from Overfitting JMLR9/28/2017
33
dropout32
Pierre Baldi, Peter J. Sadowski
2013Understanding DropoutNIPS
34
batch normalization
33
Sergey Ioffe, Christian Szegedy
2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftICML
https://arxiv.org/abs/1502.03167
35
dropout34Kyunghyun Cho
2013Understanding Dropout: Training Multi-Layer Perceptrons with Auxiliary Independent Stochastic NeuronsarXiv
36
measure AI35
Marco Baroni, Armand Joulin, Allan Jabri, Germàn Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas Mikolov
2017CommAI: Evaluating the first steps towards a useful general AIarXivTomas Mikolov
37
language model36
Alexander G Ororbia II, Tomas Mikolov, David Reitter
2017Learning Simpler Language Models with the Differential State FrameworkNeural ComputationTomas Mikolov
38
language model37
Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov
2016Bag of tricks for efficient text classificationarXivTomas Mikolov
39
language model38
Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov
2016Enriching word vectors with subword informationarXivTomas Mikolov
40
measure AI39
Tomas Mikolov, Armand Joulin, Marco Baroni
2015A roadmap towards machine intelligencearXivTomas Mikolov
41
measure AI40
Kushal Kafle, Christopher Kanan
2017An Analysis of Visual Question Answering AlgorithmsarXivjinwha kim
42
NLP, machine learning
41
David M. Blei, Andrew Y. Ng, Michael I. Jordan
2001Latent Dirichlet AllocationNIPSsearch engine
43
NLP, machine learning
42
David M. Blei, Andrew Y. Ng, Michael I. Jordan
2003Latent Dirichlet AllocationJMLRsearch engine
44
optimization43
James Bergstra, Yoshua Bengio
2012Random search for hyper-parameter optimizationJMLR
45
activation function44
Adam Coates, Andrew Y. Ng
2011Selecting receptive fields in deep networksNIPS 2011
46
45Ilya Sutskever2013Training recurrent neural networksThesis
47
46
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman
2017Building Machines That Learn and Think Like PeopleBehavioral and Brain Sciences
48
reinforcement learning
47
Richard Bellman
1957A Markovian Decision Process
Journal of Mathematics and Mechanics. 6
https://en.wikipedia.org/wiki/Markov_decision_process
49
48Stickel, M. E.1988A prolog technology theorem prover: Implementation by an extended prolog compiler
Journal of Automated Reasoning. 4 (4): 353–380
50
NLP dataset49
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang
2016SQuAD: 100,000+ Questions for Machine Comprehension of TextEMNLP 2/23/20182/23/2018
51
50
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, Antoine Bordes
2017Supervised Learning of Universal Sentence Representations from Natural Language Inference DataEMNLP 2/23/2018
52
GAN, text generation
51
William Fedus, Ian J. Goodfellow, Andrew M. Dai
2018MaskGAN: Better Text Generation via Filling in the ______arXiv2/23/2018
53
QA dataset52
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov
2015Towards AI Complete Question Answering: A Set of Prerequisite Toy TasksarXiv2/24/2018
54
representation learning
53
Jeffrey Pennington, Richard Socher, Christopher D. Manning
2014GloVe: Global Vectors for Word RepresentationEMNLP
https://nlp.stanford.edu/pubs/glove.pdf
2/24/2018
word representation
55
text dataset54
Samuel R. Bowman, Gabor Angeli, Christopher Potts, Christopher D. Manning
2015A large annotated corpus for learning natural language inferenceEMNLP
https://nlp.stanford.edu/pubs/snli_paper.pdf
2/24/2018
56
initialization55
Xavier Glorot, Yoshua Bengio
2010Understanding the difficulty of training deep feedforward neural networksAISTATS
http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
http://cs231n.github.io/neural-networks-2/#init
2/27/2018
Xavier initialization
57
initialization56
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet ClassificationICCV
https://arxiv.org/abs/1502.01852
http://cs231n.github.io/neural-networks-2/#init
2/27/2018
ReLU, weight initialization
58
dropout57
Stefan Wager, Sida I. Wang, Percy Liang
2013Dropout Training as Adaptive RegularizationNIPS
http://papers.nips.cc/paper/4882-dropout-training-as-adaptive-regularization.pdf
http://cs231n.github.io/neural-networks-2/#init
2/27/2018
59
582001Part-of-Speech Tagging with Recurrent Neural NetworksIJCNN
http://www.dlsi.ua.es/~japerez/pub/pdf/ijcnn2001.pdf
60
592017Deep Neural Networks in Computational NeurosciencebioRxiv
61
60
Haohan Wang, Bhiksha Raj
2017On the Origin of Deep LearningarXiv3/9/2018
62
61
Yoshua Bengio, Aaron C. Courville, Pascal Vincent
2013Representation Learning: A Review and New Perspectives
IEEE Trans. Pattern Anal. Mach. Intell. 35(8)
3/10/2018
63
62
Guillaume Alain, Yoshua Bengio
2014What Regularized Auto-Encoders Learn from the Data-Generating Distribution
Journal of Machine Learning Research 15(1)
3/10/2018
64
63
Robert Gens, Pedro M. Domingos
2012Discriminative learning of sum-product networksNIPS
https://papers.nips.cc/paper/4516-discriminative-learning-of-sum-product-networks.pdf
3/12/2018
65
64
Hoifung Poon and Pedro Domingos
2011Sum-Product Networks - A New Deep ArchitectureUAI
https://dslpitt.org/uai/papers/11/p337-poon.pdf
3/12/2018
66
65James Martens2010Deep Learning via Hessian-free OptimizationICML
http://www.cs.toronto.edu/~jmartens/docs/Deep_HessianFree.pdf
3/13/2018
67
66
James Martens, Ilya Sutskever
2011Learning Recurrent Neural Networks with Hessian-Free Optimization ICML
http://www.icml-2011.org/papers/532_icmlpaper.pdf
3/13/2018
68
67
Martin Krzywinski, Naomi Altman
2013Points of significance: Importance of being uncertainNature Methods 10, 809–8103/14/20183/10/2018
69
68
Martin Krzywinski, Naomi Altman
2013Points of significance: Significance, P values and t-testsNature Methods 10, 1041–10423/14/20183/9/2018
70
69
Sang-Woo Lee, Yu-Jung Heo, Byoung-Tak Zhang
2018Answerer in Questioner's Mind for Goal-Oriented Visual DialogueaxXiv
https://arxiv.org/pdf/1802.03881.pdf
3/14/2018
71
702012Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups3/14/2018
72
cognitive science71
Andrew D. Wilson, Sabrina Golonka
2013Embodied cognition is not what you think it isFront. Psychol., 12
https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00058/full
3/15/2018
73
cognitive science72
Howard Eichenbaum
2008MemoryScholarpedia
http://www.scholarpedia.org/article/Memory
74
cognitive science73이정모2010‘체화된 인지(Embodied Cognition)’ 접근과 학문간 융합철학사상, 38, 27-6
http://www.dbpia.co.kr/Journal/ArticleDetail/NODE02123010
75
NLP, CV74
Damien Teney, Anton van den Hengel
2016Zero-Shot Visual Question AnsweringarXiv
https://arxiv.org/pdf/1611.05546.pdf
안우영3/18/2018
76
75
Wagenmakers, E.-J., Lee, M. D., Lodewyckx, T., & Iverson, G.
2008Bayesian versus frequentist inference
Bayesian evaluation of informative hypotheses (pp. 181-207)
http://www.ejwagenmakers.com/2008/BayesFreqBook.pdf
3/18/2018
77
76
Rangel, A., Camerer, C., & Montague, P. R.
2008A framework for studying the neurobiology of value-based decision making
Nature reviews neuroscience, 9(7), 545
안우영3/18/2018
78
77Niv, Y.2009Reinforcement learning in the brain
Journal of Mathematical Psychology, 53(3), 139-154
안우영3/18/2018
79
78
Heathcote, A., Brown, S. D. & Wagenmakers, E.-J.
2015An introduction to good practices in cognitive modeling
An Introduction to Model-based Cognitive Neuroscience, pp. 25-48. Springer
http://www.springer.com/cda/content/document/cda_downloaddocument/9781493922352-c1.pdf?SGWID=0-0-45-1507168-p177055772
안우영3/18/2018
80
79
Stephen Grossberg
2017Towards solving the hard problem of consciousness: The varieties of
brain resonances and the conscious experiences that they support
Neural Networks 87 (2017) 38–95
https://www.sciencedirect.com/science/article/pii/S0893608016301800
장병탁3/19/2018
81
NLP80
Julien Tissier, Christopher Gravier, Amaury Habrard
2017Dict2vec : Learning Word Embeddings using Lexical DictionariesEMNLP
http://aclweb.org/anthology/D17-1024
3/19/2018
82
book81
John von Neumann
1958The computer and the brainYale University Press
https://dl.acm.org/citation.cfm?id=578873
3/26/2018
83
paper - journal82
Stephen J. Ceci, Wendy M. Williams
1997Schooling, Intelligence, and Income
American Psychologist 52(10):1051-1058
https://www.bc.edu/content/dam/files/schools/cas_sites/psych/pdf/critique_income_.pdf
3/26/2018
84
paper - journal83
Neisser, Ulric,Boodoo, Gwyneth,Bouchard Jr., Thomas J.,Boykin, A. Wade,Brody, Nathan,Ceci, Stephen J.,Halpern, Diane F.,Loehlin, John C.,Perloff, Robert,Sternberg, Robert J.,Urbina, Susana
1996Intelligence: Knowns and Unknowns
American Psychologist, Vol 51(2), 77-101
http://psych.colorado.edu/~carey/pdfFiles/IQ_Neisser2.pdf
3/26/2018
85
paper - conference84
Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, Robert Fergus
2010Deconvolutional networksCVPR
http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf
3/27/2018
86
slide85
Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, Robert Fergus
Deconvolutional Networks
https://cs.nyu.edu/~fergus/drafts/utexas2.pdf
3/27/2018
87
slide86한보형Deconvolutions in Convolutional Neural Networks
http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf
3/27/2018
88
87
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, Andrew Y. Ng
2011Multimodal deep learningICML
http://ai.stanford.edu/~ang/papers/icml11-MultimodalDeepLearning.pdf
3/30/2018
89
optimization88
James Bergstra, Yoshua Bengio
2012Random Search for Hyper-Parameter OptimizationJMLR
http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
4/5/2018
90
89
Kurt Hornik, Maxwell B. Stinchcombe, Halbert White
1989Multilayer Feedforward Networks are Universal Approximators Neural Networks 2(5): 359-366
https://pdfs.semanticscholar.org/f22f/6972e66bdd2e769fa64b0df0a13063c0c101.pdf
4/6/2018
91
90
George Cybenko
1989Approximation by superpositions of a sigmoidal functionMCSS 2(4): 303-314
https://link.springer.com/article/10.1007/BF02551274
4/6/2018
92
The Power of Depth for Feedforward Neural Networks4/7/2018
93
Universal function approximation by deep neural nets with bounded width and relu activations4/7/2018
94
Understanding Deep Neural Networks with Rectified Linear Units4/7/2018
95
Lawrence W. Barsalou
2010Grounded cognition: past, present, and future
Topics in Cognitive Science, 2(4): 716-724
http://matt.colorado.edu/teaching/highcog/readings/b8.pdf
4/8/2018
96
97
98
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