Andrew Y. K. Foong

According to our database, Andrew Y. K. Foong authored at least 15 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Denoising Diffusion Probabilistic Models in Six Simple Steps.

Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou, Aliaksandra Shysheya, Andrew Y. K. Foong, Bruno Mlodozeniec

CoRR, 2024

Improved motif-scaffolding with SE(3) flow matching.

Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola

CoRR, 2024

2023

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics.

Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Autoregressive Conditional Neural Processes.

Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

A Note on the Chernoff Bound for Random Variables in the Unit Interval.

Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt

CoRR, 2022

2021

The Gaussian Neural Process.

Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner

CoRR, 2021

Evaluating Approximate Inference in Bayesian Deep Learning.

Andrew Gordon Wilson, Pavel Izmailov, Matthew D. Hoffman, Yarin Gal, Yingzhen Li, Melanie F. Pradier, Sharad Vikram, Andrew Y. K. Foong, Sanae Lotfi, Sebastian Farquhar

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Collapsed Variational Bounds for Bayesian Neural Networks.

Marcin Tomczak, Siddharth Swaroop, Andrew Y. K. Foong, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

How Tight Can PAC-Bayes be in the Small Data Regime?

Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Structured Weight Priors for Convolutional Neural Networks.

Tim Pearce, Andrew Y. K. Foong, Alexandra Brintrup

CoRR, 2020

On the Expressiveness of Approximate Inference in Bayesian Neural Networks.

Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes.

Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Convolutional Conditional Neural Processes.

Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks.

Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner

CoRR, 2019

'In-Between' Uncertainty in Bayesian Neural Networks.

Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner

CoRR, 2019