Maxime GASSE
Title
Cited by
Cited by
Year
Exact combinatorial optimization with graph convolutional neural networks
M Gasse, D Chételat, N Ferroni, L Charlin, A Lodi
arXiv preprint arXiv:1906.01629, 2019
782019
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
652017
The scip optimization suite 7.0
G Gamrath, D Anderson, K Bestuzheva, WK Chen, L Eifler, M Gasse, ...
492020
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
432014
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
282012
On the optimality of multi-label classification under subset zero-one loss for distributions satisfying the composition property
M Gasse, A Aussem, H Elghazel
International Conference on Machine Learning, 2531-2539, 2015
152015
Hybrid models for learning to branch
P Gupta, M Gasse, EB Khalil, MP Kumar, A Lodi, Y Bengio
arXiv preprint arXiv:2006.15212, 2020
92020
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
52020
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
52016
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
32020
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 Ultrasonic Symposium (IUS), 2017
32017
Probabilistic Graphical Model Structure Learning: Application to Multi-Label Classification
M Gasse
Université de Lyon, 2017
32017
A deep learning framework for spatiotemporal ultrasound localization microscopy
L Milecki, J Porée, H Belgharbi, C Bourquin, R Damseh, ...
IEEE Transactions on Medical Imaging 40 (5), 1428-1437, 2021
12021
Analysis of risk factors of hip fracture with causal Bayesian networks
A Aussem, P Caillet, S Klemm, M Gasse, AM Schott, M Ducher
International Work-Conference on Bioinformatics and Biomedical Engineering …, 2014
12014
Optimal sensor locations for polymer injection molding process
D Garcia, R Le Goff, M Gasse, A Aussem
Key Engineering Materials 611, 1724-1733, 2014
12014
Ecole: A Library for Learning Inside MILP Solvers
A Prouvost, J Dumouchelle, M Gasse, D Chételat, A Lodi
arXiv preprint arXiv:2104.02828, 2021
2021
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
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
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Articles 1–20