| Disentangling by factorising H Kim, A Mnih Proceedings of the 35rd International Conference on Machine Learning 80 …, 2018 | 1815 | 2018 |
| Attentive neural processes H Kim, A Mnih, J Schwarz, M Garnelo, A Eslami, D Rosenbaum, O Vinyals, ... International Conference on Learning Representations (ICLR), 2019 | 498 | 2019 |
| Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects AR Kosiorek, H Kim, I Posner, YW Teh Advances in Neural Information Processing Systems 31, 8615--8625, 2018 | 282 | 2018 |
| From data to functa: Your data point is a function and you can treat it like one E Dupont, H Kim, SM Eslami, D Rezende, D Rosenbaum arXiv preprint arXiv:2201.12204, 2022 | 204 | 2022 |
| The lipschitz constant of self-attention H Kim, G Papamakarios, A Mnih International Conference on Machine Learning, 5562-5571, 2021 | 177 | 2021 |
| Pre-training via denoising for molecular property prediction S Zaidi, M Schaarschmidt, J Martens, H Kim, YW Teh, ... arXiv preprint arXiv:2206.00133, 2022 | 132 | 2022 |
| Lietransformer: Equivariant self-attention for lie groups MJ Hutchinson, C Le Lan, S Zaidi, E Dupont, YW Teh, H Kim International Conference on Machine Learning, 4533-4543, 2021 | 126* | 2021 |
| Metafun: Meta-learning with iterative functional updates J Xu, JF Ton, H Kim, A Kosiorek, YW Teh International Conference on Machine Learning, 10617-10627, 2020 | 82 | 2020 |
| Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes H Kim, YW Teh Proceedings of the Twenty-First International Conference on Artificial …, 2017 | 66 | 2017 |
| Empirical evaluation of neural process objectives TA Le, H Kim, M Garnelo, D Rosenbaum, J Schwarz, YW Teh NeurIPS workshop on Bayesian Deep Learning 4, 2018 | 53 | 2018 |
| Conditional set generation with transformers AR Kosiorek, H Kim, DJ Rezende arXiv preprint arXiv:2006.16841, 2020 | 46 | 2020 |
| 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 | 36 | 2023 |
| 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 | 33 | 2019 |
| 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 | 26 | 2024 |
| Group equivariant subsampling J Xu, H Kim, T Rainforth, Y Teh Advances in Neural Information Processing Systems 34, 5934-5946, 2021 | 24 | 2021 |
| When does re-initialization work? S Zaidi, T Berariu, H Kim, J Bornschein, C Clopath, YW Teh, R Pascanu Proceedings on, 12-26, 2023 | 20 | 2023 |
| Learning instance-specific augmentations by capturing local invariances N Miao, T Rainforth, E Mathieu, Y Dubois, YW Teh, A Foster, H Kim arXiv preprint arXiv:2206.00051, 2022 | 13 | 2022 |
| Collaborative filtering with side information: a gaussian process perspective H Kim, X Lu, S Flaxman, YW Teh arXiv preprint arXiv:1605.07025, 2016 | 8* | 2016 |
| Finding increasingly large extremal graphs with alphazero and tabu search A Mehrabian, A Anand, H Kim, N Sonnerat, M Balog, G Comanici, ... arXiv preprint arXiv:2311.03583, 2023 | 5 | 2023 |
| Interpretable models in probabilistic machine learning H Kim University of Oxford, 2019 | 4 | 2019 |
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindبريد إلكتروني تم التحقق منه على stats.ox.ac.uk