| Bootstrap your own latent-a new approach to self-supervised learning JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ... Advances in neural information processing systems 33, 21271-21284, 2020 | 1636 | 2020 |
| Can recurrent neural networks warp time? C Tallec, Y Ollivier arXiv preprint arXiv:1804.11188, 2018 | 92 | 2018 |
| Unbiased online recurrent optimization C Tallec, Y Ollivier arXiv preprint arXiv:1702.05043, 2017 | 66 | 2017 |
| Unbiasing truncated backpropagation through time C Tallec, Y Ollivier arXiv preprint arXiv:1705.08209, 2017 | 63 | 2017 |
| Bootstrapped representation learning on graphs S Thakoor, C Tallec, MG Azar, R Munos, P Veličković, M Valko ICLR 2021 Workshop on Geometrical and Topological Representation Learning, 2021 | 43 | 2021 |
| Creating artificial human genomes using generative neural networks B Yelmen, A Decelle, L Ongaro, D Marnetto, C Tallec, F Montinaro, ... PLoS genetics 17 (2), e1009303, 2021 | 43* | 2021 |
| Training recurrent networks online without backtracking Y Ollivier, C Tallec, G Charpiat arXiv preprint arXiv:1507.07680, 2015 | 41 | 2015 |
| Making deep q-learning methods robust to time discretization C Tallec, L Blier, Y Ollivier International Conference on Machine Learning, 6096-6104, 2019 | 40 | 2019 |
| Broaden your views for self-supervised video learning A Recasens, P Luc, JB Alayrac, L Wang, F Strub, C Tallec, M Malinowski, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 37 | 2021 |
| Mixed batches and symmetric discriminators for GAN training T Lucas, C Tallec, Y Ollivier, J Verbeek International Conference on Machine Learning, 2844-2853, 2018 | 31 | 2018 |
| BYOL works even without batch statistics PH Richemond, JB Grill, F Altché, C Tallec, F Strub, A Brock, S Smith, ... arXiv preprint arXiv:2010.10241, 2020 | 27 | 2020 |
| Large-scale representation learning on graphs via bootstrapping S Thakoor, C Tallec, MG Azar, M Azabou, EL Dyer, R Munos, P Veličković, ... arXiv preprint arXiv:2102.06514, 2021 | 12 | 2021 |
| Learning successor states and goal-dependent values: A mathematical viewpoint L Blier, C Tallec, Y Ollivier arXiv preprint arXiv:2101.07123, 2021 | 7 | 2021 |
| Shaking the foundations: delusions in sequence models for interaction and control PA Ortega, M Kunesch, G Delétang, T Genewein, J Grau-Moya, J Veness, ... arXiv preprint arXiv:2110.10819, 2021 | 6 | 2021 |
| Emergent communication at scale R Chaabouni, F Strub, F Altché, E Tarassov, C Tallec, E Davoodi, ... International Conference on Learning Representations, 2021 | 3 | 2021 |
| BYOL-Explore: Exploration by Bootstrapped Prediction ZD Guo, S Thakoor, M Pîslar, BA Pires, F Altché, C Tallec, A Saade, ... arXiv preprint arXiv:2206.08332, 2022 | | 2022 |
| Self-supervised representation learning using bootstrapped latent representations JBFL Grill, F Strub, F Altché, C Tallec, P Richemond, BA Pires, Z Guo, ... US Patent App. 17/338,777, 2021 | | 2021 |
| Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning OD Domingues, C Tallec, R Munos, M Valko ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021 | | 2021 |
| Recurrent Neural Networks and Reinforcement Learning: Dynamic Approaches C Tallec Université Paris-Saclay, 2019 | | 2019 |
| Recurrent Neural Networks and Reinforcement Learning: Dynamic Approaches.(Réseaux Récurrents et Apprentissage par Renforcement: Approches Dynamiques) C Tallec | | 2019 |
