| Deep learning Y LeCun, Y Bengio, G Hinton nature 521 (7553), 436-444, 2015 | 76654 | 2015 |
| Generative adversarial nets I Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, ... Advances in neural information processing systems 27, 2014 | 76536* | 2014 |
| Gradient-based learning applied to document recognition Y LeCun, L Bottou, Y Bengio, P Haffner Proceedings of the IEEE 86 (11), 2278-2324, 1998 | 64202 | 1998 |
| Deep learning I Goodfellow, Y Bengio, A Courville MIT press, 2016 | 62503 | 2016 |
| Neural machine translation by jointly learning to align and translate D Bahdanau, K Cho, Y Bengio arXiv preprint arXiv:1409.0473, 2014 | 34018 | 2014 |
| 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 | 28840 | 2014 |
| Understanding the difficulty of training deep feedforward neural networks X Glorot, Y Bengio Proceedings of the thirteenth international conference on artificial …, 2010 | 23976 | 2010 |
| Graph attention networks P Velickovic, G Cucurull, A Casanova, A Romero, P Lio, Y Bengio stat 1050 (20), 10-48550, 2017 | 19917* | 2017 |
| Empirical evaluation of gated recurrent neural networks on sequence modeling J Chung, C Gulcehre, KH Cho, Y Bengio arXiv preprint arXiv:1412.3555, 2014 | 15692 | 2014 |
| 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 | 15384 | 2013 |
| Learning deep architectures for AI Y Bengio Foundations and trends® in Machine Learning 2 (1), 1-127, 2009 | 12307 | 2009 |
| 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 | 12160 | 1994 |
| 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 | 12129 | 2015 |
| Deep sparse rectifier neural networks X Glorot, A Bordes, Y Bengio Proceedings of the fourteenth international conference on artificial …, 2011 | 11940 | 2011 |
| Random search for hyper-parameter optimization. J Bergstra, Y Bengio Journal of machine learning research 13 (2), 2012 | 11816 | 2012 |
| A Neural probabilistic language model Y Bengio, R Ducharme, P Vincent Journal of Machine Learning Research 3, 1137-1155, 2003 | 11227 | 2003 |
| How transferable are features in deep neural networks? J Yosinski, J Clune, Y Bengio, H Lipson Advances in neural information processing systems 27, 2014 | 10325 | 2014 |
| 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 | 8881 | 2008 |
| 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, L Bottou Journal of machine learning research 11 (12), 2010 | 8864 | 2010 |
| On the properties of neural machine translation: Encoder-decoder approaches K Cho, B Van Merriënboer, D Bahdanau, Y Bengio arXiv preprint arXiv:1409.1259, 2014 | 8572 | 2014 |
| Convolutional networks for images, speech, and time series Y LeCun, Y Bengio The handbook of brain theory and neural networks 3361 (10), 1995, 1995 | 7796 | 1995 |
| On the difficulty of training recurrent neural networks R Pascanu, T Mikolov, Y Bengio International conference on machine learning, 1310-1318, 2013 | 7152 | 2013 |
| Greedy layer-wise training of deep networks Y Bengio, P Lamblin, D Popovici, H Larochelle Advances in neural information processing systems 19, 2006 | 7039 | 2006 |
| Curriculum learning Y Bengio, J Louradour, R Collobert, J Weston Proceedings of the 26th annual international conference on machine learning …, 2009 | 5954 | 2009 |
| Algorithms for hyper-parameter optimization J Bergstra, R Bardenet, Y Bengio, B Kégl Advances in neural information processing systems 24, 2011 | 5945 | 2011 |
| Binaryconnect: Training deep neural networks with binary weights during propagations M Courbariaux, Y Bengio, JP David Advances in neural information processing systems 28, 2015 | 3533 | 2015 |
| Why does unsupervised pre-training help deep learning? D Erhan, A Courville, Y Bengio, P Vincent Proceedings of the thirteenth international conference on artificial …, 2010 | 3525 | 2010 |
| Brain tumor segmentation with deep neural networks M Havaei, A Davy, D Warde-Farley, A Biard, A Courville, Y Bengio, C Pal, ... Medical image analysis 35, 18-31, 2017 | 3456 | 2017 |
| Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1 M Courbariaux, I Hubara, D Soudry, R El-Yaniv, Y Bengio arXiv preprint arXiv:1602.02830, 2016 | 3456 | 2016 |
| Attention-based models for speech recognition JK Chorowski, D Bahdanau, D Serdyuk, K Cho, Y Bengio Advances in neural information processing systems 28, 2015 | 3136 | 2015 |
| Maxout networks I Goodfellow, D Warde-Farley, M Mirza, A Courville, Y Bengio International conference on machine learning, 1319-1327, 2013 | 3106 | 2013 |
| Practical recommendations for gradient-based training of deep architectures Y Bengio Neural networks: Tricks of the trade: Second edition, 437-478, 2012 | 2985 | 2012 |
| Word representations: a simple and general method for semi-supervised learning J Turian, L Ratinov, Y Bengio Proceedings of the 48th annual meeting of the association for computational …, 2010 | 2928 | 2010 |
| Estimating or propagating gradients through stochastic neurons for conditional computation Y Bengio, N Léonard, A Courville arXiv preprint arXiv:1308.3432, 2013 | 2892 | 2013 |
| Learning deep representations by mutual information estimation and maximization RD Hjelm, A Fedorov, S Lavoie-Marchildon, K Grewal, P Bachman, ... arXiv preprint arXiv:1808.06670, 2018 | 2778 | 2018 |
| On the number of linear regions of deep neural networks GF Montufar, R Pascanu, K Cho, Y Bengio Advances in neural information processing systems 27, 2014 | 2723 | 2014 |
| Gradient flow in recurrent nets: the difficulty of learning long-term dependencies S Hochreiter, Y Bengio, P Frasconi, J Schmidhuber A field guide to dynamical recurrent neural networks. IEEE Press, 2001 | 2622 | 2001 |
| A structured self-attentive sentence embedding Z Lin, M Feng, CN Santos, M Yu, B Xiang, B Zhou, Y Bengio arXiv preprint arXiv:1703.03130, 2017 | 2589 | 2017 |
| Binarized neural networks I Hubara, M Courbariaux, D Soudry, R El-Yaniv, Y Bengio Advances in neural information processing systems 29, 2016 | 2455 | 2016 |
| Semi-supervised learning by entropy minimization Y Grandvalet, Y Bengio Advances in neural information processing systems 17, 2004 | 2379 | 2004 |
Aaron CourvilleProfessor, DIRO, Université de Montréal, Mila, Cifar CAI chairVerified email at umontreal.ca
