| 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 | 4923 | 2020 |
| Data distributional properties drive emergent in-context learning in transformers S Chan, A Santoro, A Lampinen, J Wang, A Singh, P Richemond, ... Advances in Neural Information Processing Systems 35, 18878-18891, 2022 | 80 | 2022 |
| 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 | 74 | 2020 |
| Continuous diffusion for categorical data S Dieleman, L Sartran, A Roshannai, N Savinov, Y Ganin, PH Richemond, ... arXiv preprint arXiv:2211.15089, 2022 | 32 | 2022 |
| Data distributional properties drive emergent few-shot learning in transformers SCY Chan, A Santoro, AK Lampinen, JX Wang, A Singh, PH Richemond, ... arXiv preprint arXiv:2205.05055, 2022 | 25 | 2022 |
| On Wasserstein reinforcement learning and the Fokker-Planck equation PH Richemond, B Maginnis arXiv preprint arXiv:1712.07185, 2017 | 23 | 2017 |
| Understanding self-predictive learning for reinforcement learning Y Tang, ZD Guo, PH Richemond, BA Pires, Y Chandak, R Munos, ... International Conference on Machine Learning, 33632-33656, 2023 | 11 | 2023 |
| Zipfian environments for reinforcement learning SCY Chan, AK Lampinen, PH Richemond, F Hill Conference on Lifelong Learning Agents, 406-429, 2022 | 11 | 2022 |
| Categorical sdes with simplex diffusion PH Richemond, S Dieleman, A Doucet arXiv preprint arXiv:2210.14784, 2022 | 7 | 2022 |
| SemPPL: Predicting pseudo-labels for better contrastive representations M Bo¹njak, PH Richemond, N Tomasev, F Strub, JC Walker, F Hill, ... arXiv preprint arXiv:2301.05158, 2023 | 6 | 2023 |
| Memory-efficient episodic control reinforcement learning with dynamic online k-means A Agostinelli, K Arulkumaran, M Sarrico, P Richemond, AA Bharath arXiv preprint arXiv:1911.09560, 2019 | 5 | 2019 |
| Sample-efficient reinforcement learning with maximum entropy mellowmax episodic control M Sarrico, K Arulkumaran, A Agostinelli, P Richemond, AA Bharath arXiv preprint arXiv:1911.09615, 2019 | 4 | 2019 |
| A short variational proof of equivalence between policy gradients and soft q learning PH Richemond, B Maginnis arXiv preprint arXiv:1712.08650, 2017 | 4 | 2017 |
| Biologically inspired architectures for sample-efficient deep reinforcement learning PH Richemond, A Kolbeinsson, Y Guo arXiv preprint arXiv:1911.11285, 2019 | 3 | 2019 |
| Combining learning rate decay and weight decay with complexity gradient descent-Part I PH Richemond, Y Guo arXiv preprint arXiv:1902.02881, 2019 | 3 | 2019 |
| Efficiently applying attention to sequential data with the Recurrent Discounted Attention unit B Maginnis, PH Richemond arXiv preprint arXiv:1705.08480, 2017 | 3 | 2017 |
| The edge of orthogonality: a simple view of what makes BYOL tick PH Richemond, A Tam, Y Tang, F Strub, B Piot, F Hill International Conference on Machine Learning, 29063-29081, 2023 | 1 | 2023 |
| k. kavukcuoglu, R JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ... Munos, and M. Valko,“Bootstrap your own latent-a new approach to self …, 2020 | | 2020 |
| How many weights are enough: can tensor factorization learn efficient policies? PH Richemond, A Kolbeinsson, Y Guo | | 2019 |
| Static Activation Function Normalization PH Richemond, Y Guo arXiv preprint arXiv:1905.01369, 2019 | | 2019 |
