| Convolutional Conditional Neural Processes J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner International Conference on Learning Representations (ICLR) 2020, 2019 | 149 | 2019 |
| On the Expressiveness of Approximate Inference in Bayesian Neural Networks AYK Foong, DR Burt, Y Li, RE Turner Neural Information Processing Systems (NeurIPS) 2020, 2019 | 116 | 2019 |
| 'In-Between' Uncertainty in Bayesian Neural Networks AYK Foong, Y Li, JM Hernández-Lobato, RE Turner Uncertainty in Deep Learning Workshop, ICML 2019, 2019 | 114 | 2019 |
| Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes AYK Foong, WP Bruinsma, J Gordon, Y Dubois, J Requeima, RE Turner Neural Information Processing Systems (NeurIPS) 2020, 2020 | 60 | 2020 |
| The Gaussian Neural Process WP Bruinsma, J Requeima, AYK Foong, J Gordon, RE Turner Advances in Approximate Bayesian Inference (AABI) 2020, 2021 | 33 | 2021 |
| How Tight Can PAC-Bayes be in the Small Data Regime? AYK Foong, WP Bruinsma, DR Burt, RE Turner Neural Information Processing Systems (NeurIPS) 2021, 2021 | 27 | 2021 |
| Neural process family Y Dubois, J Gordon, AY Foong https://yanndubs.github.io/Neural-Process-Family/text/Intro.html, 2020 | 27 | 2020 |
| Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks AYK Foong, DR Burt, Y Li, RE Turner Bayesian Deep Learning Workshop, NeurIPS 2019, 2019 | 22 | 2019 |
| Autoregressive conditional neural processes WP Bruinsma, S Markou, J Requiema, AYK Foong, TR Andersson, ... arXiv preprint arXiv:2303.14468, 2023 | 15 | 2023 |
| Timewarp: Transferable acceleration of molecular dynamics by learning time-coarsened dynamics L Klein, A Foong, T Fjelde, B Mlodozeniec, M Brockschmidt, S Nowozin, ... Advances in Neural Information Processing Systems 36, 2024 | 13 | 2024 |
| Collapsed variational bounds for Bayesian neural networks M Tomczak, S Swaroop, A Foong, R Turner Advances in Neural Information Processing Systems 34, 25412-25426, 2021 | 12 | 2021 |
| Evaluating approximate inference in Bayesian deep learning AG Wilson, P Izmailov, MD Hoffman, Y Gal, Y Li, MF Pradier, S Vikram, ... NeurIPS 2021 Competitions and Demonstrations Track, 113-124, 2022 | 11 | 2022 |
| Fast protein backbone generation with SE (3) flow matching J Yim, A Campbell, AYK Foong, M Gastegger, J Jiménez-Luna, S Lewis, ... arXiv preprint arXiv:2310.05297, 2023 | 8 | 2023 |
| Structured Weight Priors for Convolutional Neural Networks T Pearce, AYK Foong, A Brintrup Uncertainty in Deep Learning Workshop, ICML 2020, 2020 | 5 | 2020 |
| In-Between AY Foong, Y Li, JM Hernández-Lobato, RE Turner Uncertainty in Bayesian Neural Networks. arXiv preprint arXiv, 1906 | 5 | 1906 |
| A note on the chernoff bound for random variables in the unit interval AYK Foong, WP Bruinsma, DR Burt arXiv preprint arXiv:2205.07880, 2022 | 3 | 2022 |
| Approximate inference in Bayesian neural networks and translation equivariant neural processes YK Foong | 1 | 2022 |
| Denoising Diffusion Probabilistic Models in Six Simple Steps RE Turner, CD Diaconu, S Markou, A Shysheya, AYK Foong, ... arXiv preprint arXiv:2402.04384, 2024 | | 2024 |
| Improved motif-scaffolding with SE (3) flow matching J Yim, A Campbell, E Mathieu, AYK Foong, M Gastegger, J Jiménez-Luna, ... arXiv preprint arXiv:2401.04082, 2024 | | 2024 |
| Supplement: On the Expressiveness of Approximate Inference in Bayesian Neural Networks AYK Foong, DR Burt, Y Li, RE Turner | | |
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
