| Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, TP Lillicrap, G Wayne arXiv preprint arXiv:1811.11682, 2018 | 1148 | 2018 |
| 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 | 964 | 2018 |
| The narrativeqa reading comprehension challenge T Kočiskż, J Schwarz, P Blunsom, C Dyer, KM Hermann, G Melis, ... Transactions of the Association for Computational Linguistics 6, 317-328, 2018 | 754 | 2018 |
| Neural processes M Garnelo, J Schwarz, D Rosenbaum, F Viola, DJ Rezende, SM Eslami, ... arXiv preprint arXiv:1807.01622, 2018 | 652 | 2018 |
| Attentive neural processes H Kim, A Mnih, J Schwarz, M Garnelo, A Eslami, D Rosenbaum, O Vinyals, ... arXiv preprint arXiv:1901.05761, 2019 | 477 | 2019 |
| A Recurrent Variational Autoencoder for Human Motion Synthesis I Habibie, D Holden, J Schwarz, J Yearsley, T Komura BMVC 7, 1, 2017 | 200* | 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 | 181 | 2019 |
| Multiplicative interactions and where to find them SM Jayakumar, WM Czarnecki, J Menick, J Schwarz, J Rae, S Osindero, ... International conference on learning representations, 2020 | 136 | 2020 |
| Information asymmetry in KL-regularized RL A Galashov, SM Jayakumar, L Hasenclever, D Tirumala, J Schwarz, ... arXiv preprint arXiv:1905.01240, 2019 | 109 | 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 | 58 | 2021 |
| 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 | 49 | 2018 |
| Behavior priors for efficient reinforcement learning D Tirumala, A Galashov, H Noh, L Hasenclever, R Pascanu, J Schwarz, ... Journal of Machine Learning Research 23 (221), 1-68, 2022 | 37 | 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 | 34 | 2019 |
| Spatial functa: Scaling functa to imagenet classification and generation M Bauer, E Dupont, A Brock, D Rosenbaum, JR Schwarz, H Kim arXiv preprint arXiv:2302.03130, 2023 | 26 | 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 | 20 | 2023 |
| Meta-learning sparse compression networks JR Schwarz, YW Teh arXiv preprint arXiv:2205.08957, 2022 | 20 | 2022 |
| Empowering biomedical discovery with ai agents S Gao, A Fang, Y Huang, V Giunchiglia, A Noori, JR Schwarz, Y Ektefaie, ... Cell 187 (22), 6125-6151, 2024 | 14 | 2024 |
| Online adaptation of language models with a memory of amortized contexts J Tack, J Kim, E Mitchell, J Shin, YW Teh, JR Schwarz arXiv preprint arXiv:2403.04317, 2024 | 13 | 2024 |
| C3: High-performance and low-complexity neural compression from a single image or video H Kim, M Bauer, L Theis, JR Schwarz, E Dupont Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 13 | 2024 |
| Learning large-scale neural fields via context pruned meta-learning J Tack, S Kim, S Yu, J Lee, J Shin, JR Schwarz Advances in Neural Information Processing Systems 36, 2024 | 8* | 2024 |
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
