| On the difficulty of training recurrent neural networks R Pascanu, T Mikolov, Y Bengio International conference on machine learning, 1310-1318, 2013 | 2533 | 2013 |
| 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), 2010 | 1660 | 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 | 1356 | 2012 |
| Overcoming catastrophic forgetting in neural networks J Kirkpatrick, R Pascanu, N Rabinowitz, J Veness, G Desjardins, AA Rusu, ... Proceedings of the national academy of sciences 114 (13), 3521-3526, 2017 | 821 | 2017 |
| Identifying and attacking the saddle point problem in high-dimensional non-convex optimization YN Dauphin, R Pascanu, C Gulcehre, K Cho, S Ganguli, Y Bengio Advances in neural information processing systems, 2933-2941, 2014 | 817 | 2014 |
| 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 | 615 | 2010 |
| How to construct deep recurrent neural networks R Pascanu, C Gulcehre, K Cho, Y Bengio arXiv preprint arXiv:1312.6026, 2013 | 569 | 2013 |
| On the number of linear regions of deep neural networks GF Montufar, R Pascanu, K Cho, Y Bengio Advances in neural information processing systems, 2924-2932, 2014 | 550 | 2014 |
| A simple neural network module for relational reasoning A Santoro, D Raposo, DG Barrett, M Malinowski, R Pascanu, P Battaglia, ... Advances in neural information processing systems, 4967-4976, 2017 | 534 | 2017 |
| Progressive neural networks AA Rusu, NC Rabinowitz, G Desjardins, H Soyer, J Kirkpatrick, ... arXiv preprint arXiv:1606.04671, 2016 | 510 | 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 | 470 | 2016 |
| Relational inductive biases, deep learning, and graph networks PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 410 | 2018 |
| Advances in optimizing recurrent networks Y Bengio, N Boulanger-Lewandowski, R Pascanu 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 377 | 2013 |
| Interaction networks for learning about objects, relations and physics P Battaglia, R Pascanu, M Lai, DJ Rezende Advances in neural information processing systems, 4502-4510, 2016 | 365 | 2016 |
| Learning to navigate in complex environments P Mirowski, R Pascanu, F Viola, H Soyer, AJ Ballard, A Banino, M Denil, ... arXiv preprint arXiv:1611.03673, 2016 | 346 | 2016 |
| Pylearn2: a machine learning research library IJ Goodfellow, D Warde-Farley, P Lamblin, V Dumoulin, M Mirza, ... arXiv preprint arXiv:1308.4214, 2013 | 286 | 2013 |
| Understanding the exploding gradient problem R Pascanu, T Mikolov, Y Bengio CoRR, abs/1211.5063 2, 2012 | 279 | 2012 |
| 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, 1-48, 2011 | 231 | 2011 |
| 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 | 229 | 2013 |
| Sim-to-real robot learning from pixels with progressive nets AA Rusu, M Vecerik, T Rothörl, N Heess, R Pascanu, R Hadsell arXiv preprint arXiv:1610.04286, 2016 | 224 | 2016 |
Yoshua BengioProfessor, University of Montreal (Computer Sc. & Op. Res.), Mila, CIFAR, CRM, IVADO, REPARTI, GRSNCVerified email at mila.quebec
