| Tadam: Task dependent adaptive metric for improved few-shot learning B Oreshkin, P Rodríguez López, A Lacoste Advances in neural information processing systems 31, 2018 | 1483 | 2018 |
| Tackling climate change with machine learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... ACM Computing Surveys (CSUR) 55 (2), 1-96, 2022 | 1011 | 2022 |
| Quantifying the carbon emissions of machine learning A Lacoste, A Luccioni, V Schmidt, T Dandres arXiv preprint arXiv:1910.09700, 2019 | 656 | 2019 |
| Neural autoregressive flows CW Huang, D Krueger, A Lacoste, A Courville International conference on machine learning, 2078-2087, 2018 | 511 | 2018 |
| Learning heuristics for the tsp by policy gradient M Deudon, P Cournut, A Lacoste, Y Adulyasak, LM Rousseau Integration of Constraint Programming, Artificial Intelligence, and …, 2018 | 392 | 2018 |
| Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data O Manas, A Lacoste, X Giró-i-Nieto, D Vazquez, P Rodriguez Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 223 | 2021 |
| Embedding propagation: Smoother manifold for few-shot classification P Rodríguez, I Laradji, A Drouin, A Lacoste Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 218 | 2020 |
| Coarse-to-fine question answering for long documents E Choi, D Hewlett, J Uszkoreit, I Polosukhin, A Lacoste, J Berant Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017 | 204* | 2017 |
| PAC-Bayesian theory meets Bayesian inference P Germain, F Bach, A Lacoste, S Lacoste-Julien Advances in Neural Information Processing Systems 29, 2016 | 198 | 2016 |
| Bayesian hypernetworks D Krueger, CW Huang, R Islam, R Turner, A Lacoste, A Courville arXiv preprint arXiv:1710.04759, 2017 | 166 | 2017 |
| Wikireading: A novel large-scale language understanding task over wikipedia D Hewlett, A Lacoste, L Jones, I Polosukhin, A Fandrianto, J Han, ... arXiv preprint arXiv:1608.03542, 2016 | 163 | 2016 |
| Differentiable causal discovery from interventional data P Brouillard, S Lachapelle, A Lacoste, S Lacoste-Julien, A Drouin Advances in Neural Information Processing Systems 33, 21865-21877, 2020 | 160 | 2020 |
| Online fast adaptation and knowledge accumulation (osaka): a new approach to continual learning M Caccia, P Rodriguez, O Ostapenko, F Normandin, M Lin, ... Advances in Neural Information Processing Systems 33, 16532-16545, 2020 | 141* | 2020 |
| Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ICA S Lachapelle, P Rodriguez, Y Sharma, KE Everett, R Le Priol, A Lacoste, ... Conference on Causal Learning and Reasoning, 428-484, 2022 | 110 | 2022 |
| A supervised classification algorithm for note onset detection A Lacoste, D Eck EURASIP Journal on Advances in Signal Processing 2007, 1-13, 2006 | 97 | 2006 |
| Improving explorability in variational inference with annealed variational objectives CW Huang, S Tan, A Lacoste, AC Courville Advances in neural information processing systems 31, 2018 | 63 | 2018 |
| MHC-NP: predicting peptides naturally processed by the MHC S Giguère, A Drouin, A Lacoste, M Marchand, J Corbeil, F Laviolette Journal of immunological methods 400, 30-36, 2013 | 59 | 2013 |
| Beyond trivial counterfactual explanations with diverse valuable explanations P Rodriguez, M Caccia, A Lacoste, L Zamparo, I Laradji, L Charlin, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 53 | 2021 |
| Agnostic Bayesian learning of ensembles A Lacoste, M Marchand, F Laviolette, H Larochelle International Conference on Machine Learning, 611-619, 2014 | 51 | 2014 |
| Tackling climate change with machine learning (2019) D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... arXiv preprint arxiv:1906.05433, 2019 | 47* | 2019 |
Pau RodríguezApple Machine Learning Research, ELLIS member在 apple.com 的电子邮件经过验证
