| Generative adversarial networks IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, ... arXiv preprint arXiv:1406.2661, 2014 | 27507 | 2014 |
| Maxout networks I Goodfellow, D Warde-Farley, M Mirza, A Courville, Y Bengio International conference on machine learning, 1319-1327, 2013 | 2316 | 2013 |
| The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function D Warde-Farley, SL Donaldson, O Comes, K Zuberi, R Badrawi, P Chao, ... Nucleic acids research 38 (suppl_2), W214-W220, 2010 | 2134 | 2010 |
| Theano: a CPU and GPU math expression compiler J Bergstra, O Breuleux, F Bastien, P Lamblin, R Pascanu, G Desjardins, ... Proceedings of the Python for scientific computing conference (SciPy) 4 (3), 1-7, 2010 | 1839 | 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 | 1742 | 2017 |
| Theano: new features and speed improvements F Bastien, P Lamblin, R Pascanu, J Bergstra, I Goodfellow, A Bergeron, ... arXiv preprint arXiv:1211.5590, 2012 | 1521 | 2012 |
| GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function S Mostafavi, D Ray, D Warde-Farley, C Grouios, Q Morris Genome biology 9 (1), 1-15, 2008 | 764 | 2008 |
| Dynamic modularity in protein interaction networks predicts breast cancer outcome IW Taylor, R Linding, D Warde-Farley, Y Liu, C Pesquita, D Faria, S Bull, ... Nature biotechnology 27 (2), 199-204, 2009 | 746 | 2009 |
| Theano: A CPU and GPU Math Compiler in Python J Bergstra, O Breuleux, F Bastien, P Lamblin, R Pascanu, G Desjardins, ... SciPy 2010, 0 | 709* | |
| 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 | 608 | 2016 |
| Pylearn2: a machine learning research library IJ Goodfellow, D Warde-Farley, P Lamblin, V Dumoulin, M Mirza, ... arXiv preprint arXiv:1308.4214, 2013 | 332 | 2013 |
| 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 (2), 99-111, 2016 | 317 | 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 | 288 | 2013 |
| Theano: Deep Learning on GPUs with Python J Bergstra, F Bastien, O Breuleux, P Lamblin, R Pascanu, O Delalleau, ... "Big Learning" Workshop, NIPS 2011, 2011 | 286 | 2011 |
| A critical assessment of Mus musculus gene function prediction using integrated genomic evidence L Peña-Castillo, M Tasan, CL Myers, H Lee, T Joshi, C Zhang, Y Guan, ... Genome biology 9 (1), 1-19, 2008 | 276 | 2008 |
| Unsupervised and transfer learning challenge: a deep learning approach GMY Dauphin, X Glorot, S Rifai, Y Bengio, I Goodfellow, E Lavoie, ... Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 97-110, 2012 | 209 | 2012 |
| Blocks and fuel: Frameworks for deep learning B Van Merriënboer, D Bahdanau, V Dumoulin, D Serdyuk, ... arXiv preprint arXiv:1506.00619, 2015 | 172 | 2015 |
| Variational approaches for auto-encoding generative adversarial networks M Rosca, B Lakshminarayanan, D Warde-Farley, S Mohamed arXiv preprint arXiv:1706.04987, 2017 | 171 | 2017 |
| Improving generative adversarial networks with denoising feature matching D Warde-Farley, Y Bengio | 122 | 2016 |
| 11 adversarial perturbations of deep neural networks D Warde-Farley, I Goodfellow Perturbations, Optimization, and Statistics 311, 2016 | 114 | 2016 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARAdresse e-mail validée de umontreal.ca
