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James Requeima
James Requeima
Vector Institute
Zweryfikowany adres z vectorinstitute.ai - Strona główna
Tytuł
Cytowane przez
Cytowane przez
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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
2582019
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
1902017
Convolutional Conditional Neural Processes
J Gordon, W Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner
1562020
Convolutional Conditional Neural Processes
J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner
arXiv preprint arXiv:1910.13556, 2019
1562019
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
1122020
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
642020
Mapping Gaussian Process Priors to Bayesian Neural Networks
D Flam-Shepherd, J Requeima, D Duvenaud
NIPS Bayesian deep learning workshop, 2017
582017
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
442019
The Gaussian Neural Process
WP Bruinsma, J Requeima, AYK Foong, J Gordon, RE Turner
arXiv preprint arXiv:2101.03606, 2021
332021
Practical conditional neural processes via tractable dependent predictions
S Markou, J Requeima, WP Bruinsma, A Vaughan, RE Turner
arXiv preprint arXiv:2203.08775, 2022
232022
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
132019
Characterizing and Warping the Function Space of Bayesian Neural Networks
D Flam-Shepherd, J Requeima, D Duvenaud
NeurIPS Workshop on Bayesian Deep Learning, 2018
112018
Efficient gaussian neural processes for regression
S Markou, J Requeima, W Bruinsma, R Turner
arXiv preprint arXiv:2108.09676, 2021
102021
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
62023
Challenges and Pitfalls of Bayesian Unlearning
A Rawat, J Requeima, W Bruinsma, R Turner
arXiv preprint arXiv:2207.03227, 2022
62022
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
32022
Sim2Real for Environmental Neural Processes
J Scholz, TR Andersson, A Vaughan, J Requeima, RE Turner
arXiv preprint arXiv:2310.19932, 2023
22023
Diffusion-Augmented Neural Processes
L Bonito, J Requeima, A Shysheya, RE Turner
arXiv preprint arXiv:2311.09848, 2023
12023
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
12015
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
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