3.4k citations · 3.7k across the 14 of their papers we have counts for
15 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…
Emergent Communication: Generalization and Overfitting in Lewis Games
Mathieu Rita, Corentin Tallec, Paul Michel +4
Lewis signaling games are a class of simple communication games for simulating the emergence of language. In these games, two agents must agree on a communication protocol in order…
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…
Shaking the foundations: delusions in sequence models for interaction and control
Pedro A. Ortega, Markus Kunesch, Grégoire Delétang +16
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domai…
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…
Large-Scale Representation Learning on Graphs via Bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar +5
Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs. However, to achieve state-of-the-…
Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint
Léonard Blier, Corentin Tallec, Yann Ollivier
In reinforcement learning, temporal difference-based algorithms can be sample-inefficient: for instance, with sparse rewards, no learning occurs until a reward is observed. This ca…
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…
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…
Making Deep Q-learning methods robust to time discretization
Corentin Tallec, Léonard Blier, Yann Ollivier
Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust to hyperparameterization, implementation details, or small environment changes (Henderson et al. 2017,…
Mixed batches and symmetric discriminators for GAN training
Thomas Lucas, Corentin Tallec, Jakob Verbeek +1
Generative adversarial networks (GANs) are pow- erful generative models based on providing feed- back to a generative network via a discriminator network. However, the discriminato…
Can recurrent neural networks warp time?
Corentin Tallec, Yann Ollivier
Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use ad hoc gating mechanisms. Empirically these models have been found to impr…
Unbiasing Truncated Backpropagation Through Time
Corentin Tallec, Yann Ollivier
Truncated Backpropagation Through Time (truncated BPTT) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of Backp…
Unbiased Online Recurrent Optimization
Corentin Tallec, Yann Ollivier
The novel Unbiased Online Recurrent Optimization (UORO) algorithm allows for online learning of general recurrent computational graphs such as recurrent network models. It works in…