| Domain-adversarial training of neural networks Y Ganin, E Ustinova, H Ajakan, P Germain, H Larochelle, F Laviolette, ... The Journal of Machine Learning Research 17 (1), 2096-2030, 2016 | 2207 | 2016 |
| Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species KR Bradnam, JN Fass, A Alexandrov, P Baranay, M Bechner, I Birol, ... GigaScience 2 (1), 2047-217X-2-10, 2013 | 597 | 2013 |
| Ray: simultaneous assembly of reads from a mix of high-throughput sequencing technologies S Boisvert, F Laviolette, J Corbeil Journal of computational biology 17 (11), 1519-1533, 2010 | 500 | 2010 |
| Ray Meta: scalable de novo metagenome assembly and profiling S Boisvert, F Raymond, É Godzaridis, F Laviolette, J Corbeil Genome biology 13 (12), 1-13, 2012 | 486 | 2012 |
| Domain-adversarial neural networks H Ajakan, P Germain, H Larochelle, F Laviolette, M Marchand arXiv preprint arXiv:1412.4446, 2014 | 175 | 2014 |
| PAC-Bayesian learning of linear classifiers P Germain, A Lacasse, F Laviolette, M Marchand Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 165 | 2009 |
| Deep learning for electromyographic hand gesture signal classification using transfer learning U Côté-Allard, CL Fall, A Drouin, A Campeau-Lecours, C Gosselin, ... IEEE Transactions on Neural Systems and Rehabilitation Engineering 27 (4 …, 2019 | 116 | 2019 |
| Bisimulation and cocongruence for probabilistic systems V Danos, J Desharnais, F Laviolette, P Panangaden Information and Computation 204 (4), 503-523, 2006 | 100 | 2006 |
| Approximate analysis of probabilistic processes: Logic, simulation and games J Desharnais, F Laviolette, M Tracol 2008 Fifth International Conference on Quantitative Evaluation of Systems …, 2008 | 88 | 2008 |
| PAC-Bayes bounds for the risk of the majority vote and the variance of the Gibbs classifier A Lacasse, F Laviolette, M Marchand, P Germain, N Usunier Advances in Neural information processing systems, 769-776, 2007 | 83 | 2007 |
| Risk bounds for the majority vote: From a PAC-Bayesian analysis to a learning algorithm P Germain, A Lacasse, F Laviolette, M Marchand, JF Roy arXiv preprint arXiv:1503.08329, 2015 | 81 | 2015 |
| A PAC-Bayesian approach for domain adaptation with specialization to linear classifiers P Germain, A Habrard, F Laviolette, E Morvant International conference on machine learning, 738-746, 2013 | 77 | 2013 |
| PAC-Bayesian inequalities for martingales Y Seldin, F Laviolette, N Cesa-Bianchi, J Shawe-Taylor, P Auer IEEE Transactions on Information Theory 58 (12), 7086-7093, 2012 | 77 | 2012 |
| Tighter PAC-Bayes bounds through distribution-dependent priors G Lever, F Laviolette, J Shawe-Taylor Theoretical Computer Science 473, 4-28, 2013 | 72 | 2013 |
| Transfer learning for sEMG hand gestures recognition using convolutional neural networks U Cote-Allard, CL Fall, A Campeau-Lecours, C Gosselin, F Laviolette, ... 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2017 | 59 | 2017 |
| From PAC-Bayes bounds to quadratic programs for majority votes JF Roy, F Laviolette, M Marchand ICML, 2011 | 55 | 2011 |
| A convolutional neural network for robotic arm guidance using sEMG based frequency-features UC Allard, F Nougarou, CL Fall, P Giguère, C Gosselin, F Laviolette, ... 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 52 | 2016 |
| On cop-win graphs G Hahn, F Laviolette, N Sauer, RE Woodrow Discrete Mathematics 258 (1-3), 27-41, 2002 | 49 | 2002 |
| Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons A Drouin, S Giguère, M Déraspe, M Marchand, M Tyers, VG Loo, ... BMC genomics 17 (1), 1-15, 2016 | 48 | 2016 |
| HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels S Boisvert, M Marchand, F Laviolette, J Corbeil Retrovirology 5 (1), 110, 2008 | 46 | 2008 |
Mario Marchandprofesseur d'informatique, Université LavalVerified email at ift.ulaval.ca
