| Progress & Compress: A scalable framework for continual learning J Schwarz, J Luketina, WM Czarnecki, A Grabska-Barwinska, YW Teh, ... arXiv preprint arXiv:1805.06370, 2018 | 593 | 2018 |
| Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, TP Lillicrap, G Wayne arXiv preprint arXiv:1811.11682, 2018 | 477 | 2018 |
| The narrativeqa reading comprehension challenge T Kočiský, J Schwarz, P Blunsom, C Dyer, KM Hermann, G Melis, ... Transactions of the Association for Computational Linguistics 6, 317-328, 2018 | 453 | 2018 |
| Neural processes M Garnelo, J Schwarz, D Rosenbaum, F Viola, DJ Rezende, SM Eslami, ... arXiv preprint arXiv:1807.01622, 2018 | 404 | 2018 |
| Attentive neural processes H Kim, A Mnih, J Schwarz, M Garnelo, A Eslami, D Rosenbaum, O Vinyals, ... arXiv preprint arXiv:1901.05761, 2019 | 291 | 2019 |
| A Recurrent Variational Autoencoder for Human Motion Synthesis I Habibie, D Holden, J Schwarz, J Yearsley, T Komura BMVC 7, 1, 2017 | 126 | 2017 |
| Functional regularisation for continual learning with gaussian processes MK Titsias, J Schwarz, AGG Matthews, R Pascanu, YW Teh arXiv preprint arXiv:1901.11356, 2019 | 119 | 2019 |
| Information asymmetry in KL-regularized RL A Galashov, SM Jayakumar, L Hasenclever, D Tirumala, J Schwarz, ... arXiv preprint arXiv:1905.01240, 2019 | 84 | 2019 |
| Multiplicative interactions and where to find them SM Jayakumar, WM Czarnecki, J Menick, J Schwarz, J Rae, S Osindero, ... | 79 | 2020 |
| Empirical Evaluation of Neural Process Objectives TA Le, H Kim, M Garnelo, D Rosenbaum, J Schwarz, YW Teh Third workshop on Bayesian Deep Learning (NeurIPS 2018), 2018 | 33 | 2018 |
| Behavior priors for efficient reinforcement learning D Tirumala, A Galashov, H Noh, L Hasenclever, R Pascanu, J Schwarz, ... The Journal of Machine Learning Research 23 (1), 9989-10056, 2022 | 22 | 2022 |
| Meta-learning surrogate models for sequential decision making A Galashov, J Schwarz, H Kim, M Garnelo, D Saxton, P Kohli, SM Eslami, ... arXiv preprint arXiv:1903.11907, 2019 | 21 | 2019 |
| Powerpropagation: A sparsity inducing weight reparameterisation J Schwarz, S Jayakumar, R Pascanu, PE Latham, Y Teh Advances in neural information processing systems 34, 28889-28903, 2021 | 18 | 2021 |
| Data-Driven Character Animation Synthesis I Habibie, T Komura, J Schwarz, D Holden Handbook of Human Motion, 2016 | 4* | 2016 |
| Meta-learning sparse compression networks JR Schwarz, YW Teh arXiv preprint arXiv:2205.08957, 2022 | 3 | 2022 |
| Learning 3D Global Human Motion Estimation from Unpaired, Disjoint Datasets. J Habekost, T Shiratori, Y Ye, T Komura, M Shi, K Aberman, A Aristidou, ... BMVC, 2020 | 3 | 2020 |
| Neural networks for scalable continual learning in domains with sequentially learned tasks J Schwarz, R Pascanu, RT Hadsell, W Czarnecki, YW Teh, J Luketina US Patent App. 17/048,023, 2021 | 1 | 2021 |
| Efficient Meta-Learning via Error-based Context Pruning for Implicit Neural Representations J Tack, S Kim, S Yu, J Lee, J Shin, JR Schwarz arXiv preprint arXiv:2302.00617, 2023 | | 2023 |
| Modality-Agnostic Variational Compression of Implicit Neural Representations JR Schwarz, J Tack, YW Teh, J Lee, J Shin arXiv preprint arXiv:2301.09479, 2023 | | 2023 |
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
