Interpretable genotype-to-phenotype classifiers with performance guarantees A Drouin, G Letarte, F Raymond, M Marchand, J Corbeil, F Laviolette Scientific reports 9 (1), 1-13, 2019 | 119 | 2019 |
Importance of Self-Attention for Sentiment Analysis G Letarte, F Paradis, P Giguère, F Laviolette Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and …, 2018 | 95 | 2018 |
Dichotomize and generalize: Pac-bayesian binary activated deep neural networks G Letarte, P Germain, B Guedj, F Laviolette Advances in Neural Information Processing Systems, 6872-6882, 2019 | 60 | 2019 |
Large scale modeling of antimicrobial resistance with interpretable classifiers A Drouin, F Raymond, GL St-Pierre, M Marchand, J Corbeil, F Laviolette arXiv preprint arXiv:1612.01030, 2016 | 11 | 2016 |
Pseudo-bayesian learning with kernel fourier transform as prior G Letarte, E Morvant, P Germain The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 7 | 2019 |
Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations L Fortier-Dubois, G Letarte, B Leblanc, F Laviolette, P Germain arXiv preprint arXiv:2110.15137, 2021 | 2 | 2021 |
Revisite des" random Fourier features" basée sur l'apprentissage PAC-Bayésien via des points d'intérêts L Gautheron, P Germain, A Habrard, G Letarte, E Morvant, M Sebban, ... CAp 2019-Conférence sur l'Apprentissage automatique, 2019 | 2 | 2019 |
PAC-Bayesian representation learning G Letarte | | 2023 |