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 | 244 | 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 | 184 | 2017 |
Convolutional Conditional Neural Processes J Gordon, W Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner | 148 | 2020 |
Convolutional Conditional Neural Processes J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner arXiv preprint arXiv:1910.13556, 2019 | 148 | 2019 |
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 | 106 | 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 | 59 | 2020 |
Mapping Gaussian Process Priors to Bayesian Neural Networks D Flam-Shepherd, J Requeima, D Duvenaud NIPS Bayesian deep learning workshop, 2017 | 56 | 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 | 43 | 2019 |
The Gaussian Neural Process WP Bruinsma, J Requeima, AYK Foong, J Gordon, RE Turner arXiv preprint arXiv:2101.03606, 2021 | 32 | 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 | 20 | 2022 |
Meta-optimization of optimal power flow M Jamei, L Mones, A Robson, L White, J Requeima, C Ududec ICML Workshop, Climate Change: How Can AI Help, 2019 | 12 | 2019 |
Characterizing and Warping the Function Space of Bayesian Neural Networks D Flam-Shepherd, J Requeima, D Duvenaud NeurIPS Workshop on Bayesian Deep Learning, 2018 | 11 | 2018 |
Efficient gaussian neural processes for regression S Markou, J Requeima, W Bruinsma, R Turner arXiv preprint arXiv:2108.09676, 2021 | 9 | 2021 |
Challenges and Pitfalls of Bayesian Unlearning A Rawat, J Requeima, W Bruinsma, R Turner arXiv preprint arXiv:2207.03227, 2022 | 4 | 2022 |
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 |
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 | 2 | 2023 |
Sim2Real for Environmental Neural Processes J Scholz, TR Andersson, A Vaughan, J Requeima, RE Turner arXiv preprint arXiv:2310.19932, 2023 | 1 | 2023 |
Multi-scaling of wholesale electricity prices F Caravelli, J Requeima, C Ududec, A Ashtari, T Di Matteo, T Aste arXiv preprint arXiv:1507.06219, 2015 | 1 | 2015 |
Diffusion-Augmented Neural Processes L Bonito, J Requeima, A Shysheya, RE Turner arXiv preprint arXiv:2311.09848, 2023 | | 2023 |
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 32, 7959-7970, 2019 | | 2019 |