| Tadam: Task dependent adaptive metric for improved few-shot learning B Oreshkin, PR López, A Lacoste Advances in Neural Information Processing Systems, 721-731, 2018 | 354 | 2018 |
| Neural autoregressive flows CW Huang, D Krueger, A Lacoste, A Courville arXiv preprint arXiv:1804.00779, 2018 | 174 | 2018 |
| Tackling climate change with machine learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... arXiv preprint arXiv:1906.05433, 2019 | 137 | 2019 |
| 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 | 131* | 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 | 112 | 2016 |
| A supervised classification algorithm for note onset detection A Lacoste, D Eck EURASIP Journal on Advances in Signal Processing 2007, 1-13, 2006 | 81 | 2006 |
| PAC-Bayesian theory meets Bayesian inference P Germain, F Bach, A Lacoste, S Lacoste-Julien Advances in Neural Information Processing Systems 29, 1884-1892, 2016 | 79 | 2016 |
| Bayesian hypernetworks D Krueger, CW Huang, R Islam, R Turner, A Lacoste, A Courville arXiv preprint arXiv:1710.04759, 2017 | 73 | 2017 |
| Learning heuristics for the tsp by policy gradient M Deudon, P Cournut, A Lacoste, Y Adulyasak, LM Rousseau International conference on the integration of constraint programming …, 2018 | 69 | 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 | 36 | 2013 |
| Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets A Lacoste, F Laviolette, M Marchand Journal of Machine Learning Research - Proceedings Track, 665-675, 2012 | 33 | 2012 |
| Agnostic Bayesian learning of ensembles A Lacoste, M Marchand, F Laviolette, H Larochelle International Conference on Machine Learning, 611-619, 2014 | 25 | 2014 |
| A PAC-Bayes sample-compression approach to kernel methods P Germain, A Lacoste, F Laviolette, M Marchand, S Shanian ICML, 2011 | 25 | 2011 |
| Quantifying the carbon emissions of machine learning A Lacoste, A Luccioni, V Schmidt, T Dandres arXiv preprint arXiv:1910.09700, 2019 | 24 | 2019 |
| Sequential model-based ensemble optimization A Lacoste, H Larochelle, F Laviolette, M Marchand arXiv preprint arXiv:1402.0796, 2014 | 24 | 2014 |
| Deep prior A Lacoste, T Boquet, N Rostamzadeh, B Oreshkin, W Chung, D Krueger arXiv preprint arXiv:1712.05016, 2017 | 23* | 2017 |
| Onset detection with artificial neural networks for MIREX 2005 A Lacoste, D Eck Extended abstract of the 1 st Annual Music Information Retrieval Evaluation …, 2005 | 23 | 2005 |
| Predicting genre labels for artist using FreeDB. J Bergstra, A Lacoste, D Eck ISMIR, 85-88, 2006 | 20 | 2006 |
| Accurate supervised and semi-supervised machine reading for long documents D Hewlett, L Jones, A Lacoste, I Gur Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 19 | 2017 |
| Improving explorability in variational inference with annealed variational objectives CW Huang, S Tan, A Lacoste, AC Courville Advances in Neural Information Processing Systems 31, 9701-9711, 2018 | 18 | 2018 |
Aaron CourvilleUniversité de MontréalVerified email at umontreal.ca
