| Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in neural information processing systems 25, 2012 | 10234 | 2012 |
| Domain-adversarial training of neural networks Y Ganin, E Ustinova, H Ajakan, P Germain, H Larochelle, F Laviolette, ... Journal of machine learning research 17 (59), 1-35, 2016 | 9857 | 2016 |
| 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 | 9346 | 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 | 9225 | 2010 |
| Greedy layer-wise training of deep networks Y Bengio, P Lamblin, D Popovici, H Larochelle, U Montreal Advances in neural information processing systems 19, 153, 2007 | 7197 | 2007 |
| Optimization as a model for few-shot learning S Ravi, H Larochelle International conference on learning representations, 2016 | 3777 | 2016 |
| 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 | 3657 | 2017 |
| Deep learning with coherent nanophotonic circuits Y Shen, NC Harris, S Skirlo, M Prabhu, T Baehr-Jones, M Hochberg, ... Nature photonics 11 (7), 441-446, 2017 | 2941 | 2017 |
| Autoencoding beyond pixels using a learned similarity metric ABL Larsen, SK Sønderby, H Larochelle, O Winther International conference on machine learning, 1558-1566, 2016 | 2645 | 2016 |
| Meta-learning for semi-supervised few-shot classification M Ren, E Triantafillou, S Ravi, J Snell, K Swersky, JB Tenenbaum, ... arXiv preprint arXiv:1803.00676, 2018 | 1619 | 2018 |
| Efficient learning of deep boltzmann machines R Salakhutdinov, H Larochelle International Conference on Artificial Intelligence and Statistics, 2010 | 1522* | 2010 |
| Exploring strategies for training deep neural networks. H Larochelle, Y Bengio, J Louradour, P Lamblin Journal of machine learning research 10 (1), 2009 | 1451 | 2009 |
| An empirical evaluation of deep architectures on problems with many factors of variation H Larochelle, D Erhan, A Courville, J Bergstra, Y Bengio Proceedings of the 24th international conference on Machine learning, 473-480, 2007 | 1426 | 2007 |
| Describing videos by exploiting temporal structure L Yao, A Torabi, K Cho, N Ballas, C Pal, H Larochelle, A Courville Proceedings of the IEEE international conference on computer vision, 4507-4515, 2015 | 1335 | 2015 |
| Proceedings of the 32nd international conference on neural information processing systems S Bengio, HM Wallach, H Larochelle, K Grauman, N Cesa-Bianchi Curran Associates Inc., 2018 | 1294 | 2018 |
| Machine behaviour I Rahwan, M Cebrian, N Obradovich, J Bongard, JF Bonnefon, C Breazeal, ... Nature 568 (7753), 477-486, 2019 | 1118 | 2019 |
| Classification using discriminative restricted boltzmann machines H Larochelle, Y Bengio Proceedings of the 25th international conference on Machine learning, 536-543, 2008 | 1040 | 2008 |
| Made: Masked autoencoder for distribution estimation M Germain, K Gregor, I Murray, H Larochelle International conference on machine learning, 881-889, 2015 | 973 | 2015 |
| Meta-dataset: A dataset of datasets for learning to learn from few examples E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ... arXiv preprint arXiv:1903.03096, 2019 | 704 | 2019 |
| The Neural Autoregressive Distribution Estimator H Larochelle, I Murray AISTATS, 2011 | 701 | 2011 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
