3.4k citations · 3.5k across the 6 of their papers we have counts for
7 papers
Self-conditioned Embedding Diffusion for Text Generation
Robin Strudel, Corentin Tallec, Florent Altché +8
Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can si…
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
Daniel Jarrett, Corentin Tallec, Florent Altché +3
Consider the problem of exploration in sparse-reward or reward-free environments, such as in Montezuma's Revenge. In the curiosity-driven paradigm, the agent is rewarded for how mu…
BYOL-Explore: Exploration by Bootstrapped Prediction
Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pîslar +11
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments. BYOL-Explore learns a world representation, t…
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…