| 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 | 11403 | 2017 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 6995 | 2023 |
| Progressive neural networks AA Rusu, NC Rabinowitz, G Desjardins, H Soyer, J Kirkpatrick, ... arXiv preprint arXiv:1606.04671, 2016 | 3894 | 2016 |
| 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 | 2046 | 2010 |
| Understanding disentangling in -VAE CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ... arXiv preprint arXiv:1804.03599, 2018 | 1518 | 2018 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| 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 | 1023 | 2016 |
| Policy distillation AA Rusu, SG Colmenarejo, C Gulcehre, G Desjardins, J Kirkpatrick, ... arXiv preprint arXiv:1511.06295, 2015 | 1005 | 2015 |
| Theano: A CPU and GPU Math Compiler in Python. J Bergstra, O Breuleux, F Bastien, P Lamblin, R Pascanu, G Desjardins, ... SciPy 4, 1-7, 2010 | 902 | 2010 |
| 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 | 471 | 2013 |
| Theano: Deep learning on gpus with python J Bergstra, F Bastien, O Breuleux, P Lamblin, R Pascanu, O Delalleau, ... | 378 | 2011 |
| Unsupervised and transfer learning challenge: a deep learning approach G Mesnil, Y Dauphin, X Glorot, S Rifai, Y Bengio, I Goodfellow, E Lavoie, ... Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 97-110, 2012 | 315 | 2012 |
| Natural neural networks G Desjardins, K Simonyan, R Pascanu Advances in neural information processing systems 28, 2015 | 242 | 2015 |
| 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 | 210 | 2016 |
| Griffin: Mixing gated linear recurrences with local attention for efficient language models S De, SL Smith, A Fernando, A Botev, G Cristian-Muraru, A Gu, R Haroun, ... arXiv preprint arXiv:2402.19427, 2024 | 186 | 2024 |
| Tempered Markov chain Monte Carlo for training of restricted Boltzmann machines G Desjardins, A Courville, Y Bengio, P Vincent, O Delalleau Proceedings of the thirteenth international conference on artificial …, 2010 | 159 | 2010 |
| Disentangling factors of variation via generative entangling G Desjardins, A Courville, Y Bengio arXiv preprint arXiv:1210.5474, 2012 | 133 | 2012 |
| Parallel tempering for training of restricted Boltzmann machines G Desjardins, A Courville, Y Bengio, P Vincent, O Delalleau Proceedings of the thirteenth international conference on artificial …, 2010 | 123 | 2010 |
| Progressive neural networks. arXiv 2016 AA Rusu, NC Rabinowitz, G Desjardins, H Soyer, J Kirkpatrick, ... arXiv preprint arXiv:1606.04671, 2016 | 122 | 2016 |
| Reward is enough for convex mdps T Zahavy, B O'Donoghue, G Desjardins, S Singh Advances in Neural Information Processing Systems 34, 25746-25759, 2021 | 111 | 2021 |
