| Predicting parameters in deep learning M Denil, B Shakibi, L Dinh, MA Ranzato, N De Freitas Advances in neural information processing systems, 2148-2156, 2013 | 629 | 2013 |
| Density estimation using real nvp L Dinh, J Sohl-Dickstein, S Bengio arXiv preprint arXiv:1605.08803, 2016 | 467 | 2016 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688, 2016 | 458 | 2016 |
| A recurrent latent variable model for sequential data J Chung, K Kastner, L Dinh, K Goel, AC Courville, Y Bengio Advances in neural information processing systems, 2980-2988, 2015 | 422 | 2015 |
| Nice: Non-linear independent components estimation L Dinh, D Krueger, Y Bengio arXiv preprint arXiv:1410.8516, 2014 | 313 | 2014 |
| Recurrent batch normalization T Cooijmans, N Ballas, C Laurent, Ç Gülçehre, A Courville arXiv preprint arXiv:1603.09025, 2016 | 251 | 2016 |
| Sharp minima can generalize for deep nets L Dinh, R Pascanu, S Bengio, Y Bengio Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 176 | 2017 |
| Batch normalized recurrent neural networks C Laurent, G Pereyra, P Brakel, Y Zhang, Y Bengio 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 136 | 2016 |
| Theano: A Python framework for fast computation of mathematical expressions TTD Team, R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, ... arXiv preprint arXiv:1605.02688, 2016 | 111 | 2016 |
| Techniques for learning binary stochastic feedforward neural networks T Raiko, M Berglund, G Alain, L Dinh arXiv preprint arXiv:1406.2989, 2014 | 73 | 2014 |
| The tenth annual MLSP competition: schizophrenia classification challenge RF Silva, E Castro, CN Gupta, M Cetin, M Arbabshirani, VK Potluru, ... 2014 IEEE International Workshop on Machine Learning for Signal Processing …, 2014 | 33 | 2014 |
| VideoFlow: A flow-based generative model for video M Kumar, M Babaeizadeh, D Erhan, C Finn, S Levine, L Dinh, D Kingma arXiv preprint arXiv:1903.01434, 2019 | 19 | 2019 |
| Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints, abs/1605.02688 R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... URL http://arxiv. org/abs/1605.02688, 2016 | 19 | 2016 |
| Harm de Vries, David Warde-Farley, Dustin J R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, and …, 2016 | 18 | 2016 |
| Deep independence network analysis of structural brain imaging: application to schizophrenia E Castro, RD Hjelm, SM Plis, L Dinh, JA Turner, VD Calhoun IEEE transactions on medical imaging 35 (7), 1729-1740, 2016 | 17 | 2016 |
| Learning awareness models B Amos, L Dinh, S Cabi, T Rothörl, SG Colmenarejo, A Muldal, T Erez, ... arXiv preprint arXiv:1804.06318, 2018 | 12 | 2018 |
| Fast approximate natural gradient descent in a kronecker factored eigenbasis T George, C Laurent, X Bouthillier, N Ballas, P Vincent Advances in Neural Information Processing Systems, 9550-9560, 2018 | 9 | 2018 |
| Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688 (May 2016) R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... URL http://arxiv. org/abs/1605.02688, 2016 | 7 | 2016 |
| Learnable explicit density for continuous latent space and variational inference CW Huang, A Touati, L Dinh, M Drozdzal, M Havaei, L Charlin, ... arXiv preprint arXiv:1710.02248, 2017 | 5 | 2017 |
| Discrete Flows: Invertible Generative Models of Discrete Data D Tran, K Vafa, KK Agrawal, L Dinh, B Poole arXiv preprint arXiv:1905.10347, 2019 | 3 | 2019 |
Yoshua BengioProfessor, University of Montreal (Computer Sc. & Op. Res.), Mila, CIFAR, CRM, IVADO, REPARTI, GRSNCVerified email at mila.quebec
