| High-quality plane wave compounding using convolutional neural networks M Gasse, F Millioz, E Roux, D Garcia, H Liebgott, D Friboulet IEEE transactions on ultrasonics, ferroelectrics, and frequency control 64 …, 2017 | 56 | 2017 |
| Exact combinatorial optimization with graph convolutional neural networks M Gasse, D Chételat, N Ferroni, L Charlin, A Lodi Advances in Neural Information Processing Systems, 15580-15592, 2019 | 44 | 2019 |
| A hybrid algorithm for Bayesian network structure learning with application to multi-label learning M Gasse, A Aussem, H Elghazel Expert Systems with Applications 41 (15), 6755-6772, 2014 | 37 | 2014 |
| An experimental comparison of hybrid algorithms for Bayesian network structure learning M Gasse, A Aussem, H Elghazel Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012 | 26 | 2012 |
| The SCIP Optimization Suite 7.0 G Gamrath, D Anderson, K Bestuzheva, WK Chen, L Eifler, M Gasse, ... | 16 | 2020 |
| On the optimality of multi-label classification under subset zero-one loss for distributions satisfying the composition property M Gasse, A Aussem, H Elghazel | 14 | 2015 |
| F-measure maximization in multi-label classification with conditionally independent label subsets M Gasse, A Aussem Joint European Conference on Machine Learning and Knowledge Discovery in …, 2016 | 5 | 2016 |
| On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming B Müller, G Muñoz, M Gasse, A Gleixner, A Lodi, F Serrano International Conference on Integer Programming and Combinatorial …, 2020 | 3 | 2020 |
| Accelerating plane wave imaging through deep learning-based reconstruction: An experimental study M Gasse, F Millioz, E Roux, H Liebgott, D Friboulet 2017 IEEE International Ultrasonics Symposium (IUS), 1-1, 2017 | 3 | 2017 |
| Probabilistic Graphical Model Structure Learning: Application to Multi-Label Classification M Gasse | 2 | 2017 |
| Hybrid models for learning to branch P Gupta, M Gasse, E Khalil, P Mudigonda, A Lodi, Y Bengio Advances in Neural Information Processing Systems 33, 2020 | 1 | 2020 |
| Analysis of risk factors of hip fracture with causal Bayesian networks A Aussem, P Caillet, S Klemm, M Gasse, AM Schott, M Ducher | 1 | 2014 |
| Optimal sensor locations for polymer injection molding process D Garcia, R Le Goff, M Gasse, A Aussem Key Engineering Materials 611, 1724-1733, 2014 | 1 | 2014 |
| Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers A Prouvost, J Dumouchelle, L Scavuzzo, M Gasse, D Chételat, A Lodi arXiv preprint arXiv:2011.06069, 2020 | | 2020 |
| On the Effectiveness of Two-Step Learning for Latent-Variable Models C Subakan, M Gasse, L Charlin 2020 IEEE 30th International Workshop on Machine Learning for Signal …, 2020 | | 2020 |
| Exact Combinatorial Optimization with Graph Convolutional Neural Networks M Gasse, D Chetelat, N Ferroni, L Charlin, A Lodi Advances in Neural Information Processing Systems 32, 15580-15592, 2019 | | 2019 |
| On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss D Lecoeuche, A Aussem, M Gasse Procedia computer science 144, 71-80, 2018 | | 2018 |
| Apprentissage de Structure de Modèles Graphiques Probabilistes: Application à la Classification Multi-Label M Gasse Université Lyon 1, 2017 | | 2017 |
| Identifying the irreducible disjoint factors of a multivariate probability distribution M Gasse, A Aussem Conference on Probabilistic Graphical Models, 183-194, 2016 | | 2016 |
| Supplement: Hybrid Models for Learning to Branch P Gupta, M Gasse, EB Khalil, MP Kumar, A Lodi, Y Bengio | | |
Ronan Le GoffIPCVerified email at ct-ipc.com
Ronan Le GoffIPCVerified email at ct-ipc.com