| Fast and flexible multi-task classification using conditional neural adaptive processes J Requeima, J Gordon, J Bronskill, S Nowozin, RE Turner Advances in Neural Information Processing Systems, 7959-7970, 2019 | 273 | 2019 |
| Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space JM Hernández-Lobato, J Requeima, EO Pyzer-Knapp, A Aspuru-Guzik Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 202 | 2017 |
| Convolutional Conditional Neural Processes J Gordon, W Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner | 168 | 2020 |
| Tasknorm: Rethinking batch normalization for meta-learning J Bronskill, J Gordon, J Requeima, S Nowozin, R Turner International Conference on Machine Learning, 1153-1164, 2020 | 116 | 2020 |
| Meta-learning stationary stochastic process prediction with convolutional neural processes A Foong, W Bruinsma, J Gordon, Y Dubois, J Requeima, R Turner Advances in Neural Information Processing Systems 33, 2020 | 69 | 2020 |
| Mapping Gaussian Process Priors to Bayesian Neural Networks D Flam-Shepherd, J Requeima, D Duvenaud NIPS Bayesian deep learning workshop, 2017 | 61 | 2017 |
| The gaussian process autoregressive regression model (gpar) J Requeima, W Tebbutt, W Bruinsma, RE Turner The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 48 | 2019 |
| The Gaussian neural process WP Bruinsma, J Requeima, AYK Foong, J Gordon, RE Turner arXiv preprint arXiv:2101.03606, 2021 | 35 | 2021 |
| Practical conditional neural processes via tractable dependent predictions S Markou, J Requeima, WP Bruinsma, A Vaughan, RE Turner arXiv preprint arXiv:2203.08775, 2022 | 24 | 2022 |
| Meta-optimization of optimal power flow M Jamei, L Mones, A Robson, L White, J Requeima, C Ududec ICML Workshop on Climate Change: How Can AI Help, 2019 | 13 | 2019 |
| Efficient gaussian neural processes for regression S Markou, J Requeima, W Bruinsma, R Turner arXiv preprint arXiv:2108.09676, 2021 | 12 | 2021 |
| Characterizing and Warping the Function Space of Bayesian Neural Networks D Flam-Shepherd, J Requeima, D Duvenaud NeurIPS Workshop on Bayesian Deep Learning, 2018 | 12 | 2018 |
| Environmental sensor placement with convolutional Gaussian neural processes TR Andersson, WP Bruinsma, S Markou, J Requeima, A Coca-Castro, ... Environmental Data Science 2, e32, 2023 | 9 | 2023 |
| Challenges and pitfalls of Bayesian unlearning A Rawat, J Requeima, W Bruinsma, R Turner arXiv preprint arXiv:2207.03227, 2022 | 5 | 2022 |
| Aardvark Weather: end-to-end data-driven weather forecasting A Vaughan, S Markou, W Tebbutt, J Requeima, WP Bruinsma, ... arXiv preprint arXiv:2404.00411, 2024 | 3 | 2024 |
| Sim2real for environmental neural processes J Scholz, TR Andersson, A Vaughan, J Requeima, RE Turner arXiv preprint arXiv:2310.19932, 2023 | 3 | 2023 |
| Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement TR Andersson, WP Bruinsma, S Markou, DC Jones, JS Hosking, ... arXiv preprint arXiv:2211.10381, 2022 | 3 | 2022 |
| LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language J Requeima, J Bronskill, D Choi, RE Turner, D Duvenaud arXiv preprint arXiv:2405.12856, 2024 | 2 | 2024 |
| Translation Equivariant Transformer Neural Processes M Ashman, C Diaconu, J Kim, L Sivaraya, S Markou, J Requeima, ... arXiv preprint arXiv:2406.12409, 2024 | 1 | 2024 |
| Diffusion-Augmented Neural Processes L Bonito, J Requeima, A Shysheya, RE Turner arXiv preprint arXiv:2311.09848, 2023 | 1 | 2023 |
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
