| A convolutional neural network approach to brain tumor segmentation M Havaei, F Dutil, C Pal, H Larochelle, PM Jodoin Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016 | 128 | 2016 |
| A deep and autoregressive approach for topic modeling of multimodal data Y Zheng, YJ Zhang, H Larochelle IEEE transactions on pattern analysis and machine intelligence 38 (6), 1056-1069, 2015 | 74 | 2015 |
| A deep and tractable density estimator B Uria, I Murray, H Larochelle International Conference on Machine Learning, 467-475, 2014 | 187 | 2014 |
| A density estimation perspective on learning from pairwise human preferences V Dumoulin, DD Johnson, PS Castro, H Larochelle, Y Dauphin arXiv preprint arXiv:2311.14115, 2023 | 3 | 2023 |
| A meta-learning perspective on cold-start recommendations for items M Vartak, A Thiagarajan, C Miranda, J Bratman, H Larochelle Advances in neural information processing systems 30, 2017 | 258 | 2017 |
| A neural autoregressive approach to attention-based recognition Y Zheng, RS Zemel, YJ Zhang, H Larochelle International Journal of Computer Vision 113, 67-79, 2015 | 38 | 2015 |
| A neural autoregressive topic model H Larochelle, S Lauly Advances in Neural Information Processing Systems 25, 2012 | 293 | 2012 |
| A RAD approach to deep mixture models L Dinh, J Sohl-Dickstein, H Larochelle, R Pascanu arXiv preprint arXiv:1903.07714, 2019 | 41 | 2019 |
| A supervised neural autoregressive topic model for simultaneous image classification and annotation Y Zheng, YJ Zhang, H Larochelle arXiv preprint arXiv:1305.5306, 2013 | 3 | 2013 |
| A unified few-shot classification benchmark to compare transfer and meta learning approaches V Dumoulin, N Houlsby, U Evci, X Zhai, R Goroshin, S Gelly, H Larochelle Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 17 | 2021 |
| A universal representation transformer layer for few-shot image classification L Liu, W Hamilton, G Long, J Jiang, H Larochelle arXiv preprint arXiv:2006.11702, 2020 | 139 | 2020 |
| Acquiring and predicting multidimensional diffusion (MUDI) data: an open challenge M Pizzolato, M Palombo, E Bonet-Carne, CMW Tax, F Grussu, A Ianus, ... Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019 …, 2020 | 23 | 2020 |
| Advances in neural information processing systems L Prokhorenkova, G Gusev, A Vorobev, AV Dorogush, A Gulin, S Bengio, ... NeurIPS 2018, 6638-6648, 2018 | 56 | 2018 |
| Advances in neural information processing systems Y Bengio (No Title) 19, 153, 2007 | 239 | 2007 |
| Advances in neural information processing systems J Snoek, H Larochelle, RP Adams, F Pereira, CJC Burges, L Bottou, ... Practical Bayesian optimization of machine learning algorithms, 2951-9, 2012 | 212 | 2012 |
| Agnostic Bayesian learning of ensembles A Lacoste, M Marchand, F Laviolette, H Larochelle International Conference on Machine Learning, 611-619, 2014 | 51 | 2014 |
| Algorithme des k plus proches voisins O Delalleau, H Larochelle Janvier, 2007 | 1 | 2007 |
| Algorithmic improvements for deep reinforcement learning applied to interactive fiction V Jain, W Fedus, H Larochelle, D Precup, MG Bellemare Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4328-4336, 2020 | 34 | 2020 |
| An autoencoder approach to learning bilingual word representations S Chandar AP, S Lauly, H Larochelle, M Khapra, B Ravindran, VC Raykar, ... Advances in neural information processing systems 27, 2014 | 394 | 2014 |
| An effective anti-aliasing approach for residual networks C Vasconcelos, H Larochelle, V Dumoulin, NL Roux, R Goroshin arXiv preprint arXiv:2011.10675, 2020 | 28 | 2020 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca