3.4k citations · 3.7k across the 8 of their papers we have counts for
8 papers
On the role of population heterogeneity in emergent communication
Mathieu Rita, Florian Strub, Jean-Bastien Grill +2
Populations have often been perceived as a structuring component for language to emerge and evolve: the larger the population, the more structured the language. While this observat…
Broaden Your Views for Self-Supervised Video Learning
Adrià Recasens, Pauline Luc, Jean-Baptiste Alayrac +11
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by…
BYOL works even without batch statistics
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché +8
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation. From an augmented view of an image, BYOL trains an online network to predict a tar…
Monte-Carlo Tree Search as Regularized Policy Optimization
Jean-Bastien Grill, Florent Altché, Yunhao Tang +4
The combination of Monte-Carlo tree search (MCTS) with deep reinforcement learning has led to significant advances in artificial intelligence. However, AlphaZero, the current state…
Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill, Florian Strub, Florent Altché +11
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target…
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Daniel Guo, Bernardo Avila Pires, Bilal Piot +4
Learning a good representation is an essential component for deep reinforcement learning (RL). Representation learning is especially important in multitask and partially observable…