Yoshua Bengio
Yoshua Bengio
Professor, University of Montreal (Computer Sc. & Op. Res.), Mila, CIFAR, CRM, IVADO, REPARTI, GRSNC
Подтвержден адрес электронной почты в домене mila.quebec - Главная страница
НазваниеПроцитированоГод
Gradient-based learning applied to document recognition
Y LeCun, L Bottou, Y Bengio, P Haffner
Proceedings of the IEEE 86 (11), 2278-2324, 1998
213961998
Deep learning
Y LeCun, Y Bengio, G Hinton
nature 521 (7553), 436, 2015
191682015
Generative adversarial nets
I Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, ...
Advances in neural information processing systems, 2672-2680, 2014
123102014
Deep learning
I Goodfellow, Y Bengio, A Courville
MIT press, 2016
104142016
Neural machine translation by jointly learning to align and translate
D Bahdanau, K Cho, Y Bengio
arXiv preprint arXiv:1409.0473, 2014
87512014
Learning deep architectures for AI
Y Bengio
Foundations and trends® in Machine Learning 2 (1), 1-127, 2009
72032009
Understanding the difficulty of training deep feedforward neural networks
X Glorot, Y Bengio
Proceedings of the thirteenth international conference on artificial …, 2010
66092010
Learning phrase representations using RNN encoder-decoder for statistical machine translation
K Cho, B Van Merriënboer, C Gulcehre, D Bahdanau, F Bougares, ...
arXiv preprint arXiv:1406.1078, 2014
65182014
Representation learning: A review and new perspectives
Y Bengio, A Courville, P Vincent
IEEE transactions on pattern analysis and machine intelligence 35 (8), 1798-1828, 2013
54502013
A Neural probabilistic language model
Y Bengio, R Ducharme, P Vincent
Journal of Machine Learning Research 3, 1137-1155, 2003
51722003
Greedy layer-wise training of deep networks
Y Bengio, P Lamblin, D Popovici, H Larochelle
Advances in neural information processing systems, 153-160, 2007
40512007
Learning long-term dependencies with gradient descent is difficult
Y Bengio, P Simard, P Frasconi
IEEE transactions on neural networks 5 (2), 157-166, 1994
40441994
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
P Vincent, H Larochelle, I Lajoie, Y Bengio, PA Manzagol
Journal of machine learning research 11 (Dec), 3371-3408, 2010
38952010
Show, attend and tell: Neural image caption generation with visual attention
K Xu, J Ba, R Kiros, K Cho, A Courville, R Salakhudinov, R Zemel, ...
International conference on machine learning, 2048-2057, 2015
38182015
Deep sparse rectifier neural networks
X Glorot, A Bordes, Y Bengio
Proceedings of the fourteenth international conference on artificial …, 2011
36902011
Extracting and composing robust features with denoising autoencoders
P Vincent, H Larochelle, Y Bengio, PA Manzagol
Proceedings of the 25th international conference on Machine learning, 1096-1103, 2008
36052008
Empirical evaluation of gated recurrent neural networks on sequence modeling
J Chung, C Gulcehre, KH Cho, Y Bengio
arXiv preprint arXiv:1412.3555, 2014
33392014
How transferable are features in deep neural networks?
J Yosinski, J Clune, Y Bengio, H Lipson
Advances in neural information processing systems, 3320-3328, 2014
30822014
Random search for hyper-parameter optimization
J Bergstra, Y Bengio
Journal of Machine Learning Research 13 (Feb), 281-305, 2012
29672012
Convolutional networks for images, speech, and time series
Y LeCun, Y Bengio
The handbook of brain theory and neural networks 3361 (10), 1995, 1995
25301995
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