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Andrew Y. K. Foong
Andrew Y. K. Foong
Senior Researcher, Microsoft Research
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
Convolutional Conditional Neural Processes
J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner
International Conference on Learning Representations (ICLR) 2020, 2019
1412019
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
1122019
'In-Between' Uncertainty in Bayesian Neural Networks
AYK Foong, Y Li, JM Hernández-Lobato, RE Turner
Uncertainty in Deep Learning Workshop, ICML 2019, 2019
1122019
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
562020
The Gaussian Neural Process
WP Bruinsma, J Requeima, AYK Foong, J Gordon, RE Turner
Advances in Approximate Bayesian Inference (AABI) 2020, 2021
322021
Neural process family
Y Dubois, J Gordon, AY Foong
https://yanndubs.github.io/Neural-Process-Family/text/Intro.html, 2020
262020
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
252021
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
212019
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
112021
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
102024
Autoregressive conditional neural processes
WP Bruinsma, S Markou, J Requiema, AYK Foong, TR Andersson, ...
arXiv preprint arXiv:2303.14468, 2023
102023
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
92022
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
52023
Structured Weight Priors for Convolutional Neural Networks
T Pearce, AYK Foong, A Brintrup
Uncertainty in Deep Learning Workshop, ICML 2020, 2020
52020
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
42022
Approximate Inference in Bayesian Neural Networks and Translation Equivariant Neural Processes
YK Foong
University of Cambridge, 2022
12022
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
Supplementary Material: Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
AYK Foong, WP Bruinsma, J Gordon, Y Dubois, J Requeima, RE Turner
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Articles 1–20