Maxime GASSE
Maxime GASSE
Postdoctoral Research Scientist, Polytechnique Montréal
Verified email at polymtl.ca - Homepage
TitleCited byYear
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
292014
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
262017
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
212012
On the optimality of multi-label classification under subset zero-one loss for distributions satisfying the composition property
M Gasse, A Aussem, H Elghazel
122015
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
M Gasse, D Chételat, N Ferroni, L Charlin, A Lodi
arXiv preprint arXiv:1906.01629, 2019
72019
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
42016
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
12017
Probabilistic Graphical Model Structure Learning: Application to Multi-Label Classification
M Gasse
12017
Analysis of risk factors of hip fracture with causal Bayesian networks
A Aussem, P Caillet, S Klemm, M Gasse, AM Schott, M Ducher
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
On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming
B Müller, G Muñoz, M Gasse, A Gleixner, A Lodi, F Serrano
arXiv preprint arXiv:1912.00356, 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
Exact Combinatorial Optimization with Graph Convolutional Neural Networks Supplementary Materials
M Gasse, D Chételat, N Ferroni, L Charlin, HEC Mila, A Lodi
Algorithmes de factorisation d’une loi de probabilité jointe en facteurs independents et minimaux
M Gasse, A Aussem
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Articles 1–16