57 citations · 72 across the 10 of their papers we have counts for
5 papers · 1 filter
Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
Yann Claes, Pierre Geurts, Vân Anh Huynh-Thu
Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes into developing techniques…
Parallelizing Autoregressive Generation with Variational State Space Models
Gaspard Lambrechts, Yann Claes, Pierre Geurts +1
Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although bo…
Distillation from heterogeneous unlabeled collections
Jean-Michel Begon, Pierre Geurts
Compressing deep networks is essential to expand their range of applications to constrained settings. The need for compression however often arises long after the model was trained…
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning
Pascal Leroy, Damien Ernst, Pierre Geurts +3
This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings. All algorithms are bas…
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
Matthia Sabatelli, Gilles Louppe, Pierre Geurts +1
This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms. The aim of these algorithms is to jointly…