| Greedy layer-wise training of deep networks Y Bengio, P Lamblin, D Popovici, H Larochelle Advances in neural information processing systems 19, 2006 | 6753 | 2006 |
| Theano: A CPU and GPU math compiler in Python J Bergstra, O Breuleux, F Bastien, P Lamblin, R Pascanu, G Desjardins, ... Proc. 9th python in science conf 1, 3-10, 2010 | 2815* | 2010 |
| Theano: new features and speed improvements F Bastien, P Lamblin, R Pascanu, J Bergstra, I Goodfellow, A Bergeron, ... arXiv preprint arXiv:1211.5590, 2012 | 1691 | 2012 |
| Exploring strategies for training deep neural networks. H Larochelle, Y Bengio, J Louradour, P Lamblin Journal of machine learning research 10 (1), 2009 | 1323 | 2009 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 1098* | 2016 |
| 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 | 534 | 2019 |
| Emonets: Multimodal deep learning approaches for emotion recognition in video SE Kahou, X Bouthillier, P Lamblin, C Gulcehre, V Michalski, K Konda, ... Journal on Multimodal User Interfaces 10, 99-111, 2016 | 464 | 2016 |
| Combining modality specific deep neural networks for emotion recognition in video SE Kahou, C Pal, X Bouthillier, P Froumenty, Ç Gülçehre, R Memisevic, ... Proceedings of the 15th ACM on International conference on multimodal …, 2013 | 404 | 2013 |
| Pylearn2: a machine learning research library IJ Goodfellow, D Warde-Farley, P Lamblin, V Dumoulin, M Mirza, ... arXiv preprint arXiv:1308.4214, 2013 | 367 | 2013 |
| Theano: Deep learning on GPUs with python J Bergstra, F Bastien, O Breuleux, P Lamblin, R Pascanu, O Delalleau, ... NIPS 2011, BigLearning Workshop, Granada, Spain 3, 2011 | 348 | 2011 |
| Advances in neural information processing systems Y Bengio (No Title) 19, 153, 2007 | 328 | 2007 |
| Quadratic polynomials learn better image features J Bergstra, G Desjardins, P Lamblin, Y Bengio Technical report, 1337, 2009 | 88 | 2009 |
| Automatic differentiation in ML: Where we are and where we should be going B Van Merriënboer, O Breuleux, A Bergeron, P Lamblin Advances in neural information processing systems 31, 2018 | 81 | 2018 |
| Important gains from supervised fine-tuning of deep architectures on large labeled sets P Lamblin, Y Bengio NIPS* 2010 Deep Learning and Unsupervised Feature Learning Workshop, 1-8, 2010 | 29 | 2010 |
| Learning the 2-D topology of images N Le Roux, Y Bengio, P Lamblin, M Joliveau, B Kégl Advances in Neural Information Processing Systems, 841-848, 2008 | 25* | 2008 |
| PLUR: A unifying, graph-based view of program learning, understanding, and repair Z Chen, VJ Hellendoorn, P Lamblin, P Maniatis, PA Manzagol, D Tarlow, ... Advances in Neural Information Processing Systems 34, 23089-23101, 2021 | 23 | 2021 |
| Oríon-asynchronous distributed hyperparameter optimization X Bouthillier, C Tsirigotis, F Corneau-Tremblay, P Delaunay, ... October, 2019 | 9 | 2019 |
| Deep learning on GPUs with Theano J Bergstra, F Bastien, J Turian, R Pascanu, O Delalleau, O Breuleux, ... The Learning Workshop, 2010 | 7 | 2010 |
| Oríon: Experiment version control for efficient hyperparameter optimization C Tsirigotis, X Bouthillier, F Corneau-Tremblay, P Henderson, R Askari, ... | 4 | 2018 |
| Image classification with complex cell neural networks J Bergstra, Y Bengio, P Lamblin, G Desjardins, J Louradour Front. Neurosci. Computational and Systems Neuroscience, 2010 | 1 | 2010 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
