Quentin Cappart | DeepAI

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∙ 01/05/2023

Training a Deep Q-Learning Agent Inside a Generic Constraint Programming Solver

Constraint programming is known for being an efficient approach for solv...

7 Tom Marty, et al. ∙

∙ 03/04/2022

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

Combinatorial optimization is a well-established area in operations rese...

12 Maxime Gasse, et al. ∙

∙ 12/19/2021

On Causal Inference for Data-free Structured Pruning

Neural networks (NNs) are making a large impact both on research and ind...

23 Martin Ferianc, et al. ∙

∙ 02/18/2021

Combinatorial optimization and reasoning with graph neural networks

Combinatorial optimization is a well-established area in operations rese...

9 Quentin Cappart, et al. ∙

∙ 02/18/2021

SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning

The design of efficient and generic algorithms for solving combinatorial...

62 Félix Chalumeau, et al. ∙

∙ 06/12/2020

Learning TSP Requires Rethinking Generalization

End-to-end training of neural network solvers for combinatorial problems...

36 Chaitanya K. Joshi, et al. ∙

∙ 06/02/2020

Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

Combinatorial optimization has found applications in numerous fields, fr...

0 Quentin Cappart, et al. ∙

∙ 09/28/2019

How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman Problem

Combinatorial optimization is the field devoted to the study and practic...

0 Antoine François, et al. ∙

∙ 09/10/2018

Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

Finding tight bounds on the optimal solution is a critical element of pr...

0 Quentin Cappart, et al. ∙

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