18 citations · 41 across the 5 of their papers we have counts for
6 papers
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
Mathieu Seurin, Florian Strub, Philippe Preux +1
Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize. Intrinsic motivation guidances have thus been developed toward alle…
A Machine of Few Words -- Interactive Speaker Recognition with Reinforcement Learning
Mathieu Seurin, Florian Strub, Philippe Preux +1
Speaker recognition is a well known and studied task in the speech processing domain. It has many applications, either for security or speaker adaptation of personal devices. In th…
HIGhER : Improving instruction following with Hindsight Generation for Experience Replay
Geoffrey Cideron, Mathieu Seurin, Florian Strub +1
Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. While these characterizations may…
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action
Mathieu Seurin, Philippe Preux, Olivier Pietquin
The use of Reinforcement Learning (RL) is still restricted to simulation or to enhance human-operated systems through recommendations. Real-world environments (e.g. industrial robo…
Visual Reasoning with Multi-hop Feature Modulation
Florian Strub, Mathieu Seurin, Ethan Perez +5
Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue…
Unsupervised state representation learning with robotic priors: a robustness benchmark
Timothée Lesort, Mathieu Seurin, Xinrui Li +2
Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simp…