1 code implementation • 30 Sep 2022 • Mathieu Rita, Corentin Tallec, Paul Michel, Jean-bastien Grill, Olivier Pietquin, Emmanuel Dupoux, Florian Strub
Lewis signaling games are a class of simple communication games for simulating the emergence of language.
no code implementations • 16 Jun 2022 • Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments.
1 code implementation • ICCV 2021 • Adrià Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Ross Hemsley, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Patraucean, Florent Altché, Michal Valko, Jean-bastien Grill, Aäron van den Oord, Andrew Zisserman
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data.
8 code implementations • NeurIPS 2020 • Jean-bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Remi Munos, Michal Valko
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
3 code implementations • 20 Oct 2020 • Pierre H. Richemond, Jean-bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, Andrew Brock, Samuel Smith, Soham De, Razvan Pascanu, Bilal Piot, Michal Valko
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation.
3 code implementations • ICML 2020 • Jean-bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
The combination of Monte-Carlo tree search (MCTS) with deep reinforcement learning has led to significant advances in artificial intelligence.
31 code implementations • 13 Jun 2020 • Jean-bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
Ranked #2 on Self-Supervised Person Re-Identification on SYSU-30k
no code implementations • ICML 2020 • Daniel Guo, Bernardo Avila Pires, Bilal Piot, Jean-bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar
These latent embeddings are themselves trained to be predictive of the aforementioned representations.
1 code implementation • NeurIPS 2019 • Jean-bastien Grill, Omar Darwiche Domingues, Pierre Menard, Remi Munos, Michal Valko
We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the SmoothCruiser.
1 code implementation • 20 Feb 2019 • Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo Avila Pires, Jean-bastien Grill, Florent Altché, Rémi Munos
As humans we are driven by a strong desire for seeking novelty in our world.
no code implementations • NeurIPS 2018 • Jean-bastien Grill, Michal Valko, Rémi Munos
Given $W$, our goal is to return an $\epsilon$-approximation of its maximum using the smallest possible number of function evaluations, the sample complexity of the algorithm.
no code implementations • NeurIPS 2016 • Jean-bastien Grill, Michal Valko, Remi Munos
We study the sampling-based planning problem in Markov decision processes (MDPs) that we can access only through a generative model, usually referred to as Monte-Carlo planning.
no code implementations • NeurIPS 2015 • Jean-bastien Grill, Michal Valko, Remi Munos
We study the problem of black-box optimization of a function $f$ of any dimension, given function evaluations perturbed by noise.