Maxime Gasse
Orcid: 0000-0001-6982-062X
According to our database1,
Maxime Gasse
authored at least 23 papers
between 2012 and 2024.
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Bibliography
2024
CoRR, 2024
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy.
CoRR, 2024
2022
Math. Program., 2022
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights.
CoRR, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
2021
IEEE Trans. Medical Imaging, 2021
CoRR, 2021
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights.
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021
2020
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers.
CoRR, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020
2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
2018
On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss.
Proceedings of the INNS Conference on Big Data and Deep Learning 2018, 2018
2017
Probabilistic Graphical Model Structure Learning: Application to Multi-Label Classification. (Apprentissage de Structure de Modèles Graphiques Probabilistes: Application à la Classification Multi-Label).
PhD thesis, 2017
2016
F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016
Identifying the irreducible disjoint factors of a multivariate probability distribution.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016
2015
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property.
Proceedings of the 32nd International Conference on Machine Learning, 2015
2014
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning.
Expert Syst. Appl., 2014
Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering, 2014
2012
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012