| De novo design of protein structure and function with RFdiffusion JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, ... Nature 620 (7976), 1089-1100, 2023 | 737 | 2023 |
| Disentangling disentanglement in variational autoencoders E Mathieu, T Rainforth, N Siddharth, YW Teh International conference on machine learning, 4402-4412, 2019 | 346 | 2019 |
| Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders E Mathieu, C Le Lan, CJ Maddison, R Tomioka, YW Teh Advances in neural information processing systems, 12544-12555, 2019 | 223* | 2019 |
| Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, ... BioRxiv, 2022.12. 09.519842, 2022 | 199 | 2022 |
| Riemannian score-based generative modelling V De Bortoli, E Mathieu, M Hutchinson, J Thornton, YW Teh, A Doucet Advances in Neural Information Processing Systems 35, 2406-2422, 2022 | 172 | 2022 |
| SE (3) diffusion model with application to protein backbone generation J Yim, BL Trippe, V De Bortoli, E Mathieu, A Doucet, R Barzilay, ... arXiv preprint arXiv:2302.02277, 2023 | 156 | 2023 |
| Riemannian continuous normalizing flows E Mathieu, M Nickel Advances in Neural Information Processing Systems 33, 2503-2515, 2020 | 115 | 2020 |
| Diffusion models for constrained domains N Fishman, L Klarner, V De Bortoli, E Mathieu, M Hutchinson arXiv preprint arXiv:2304.05364, 2023 | 32 | 2023 |
| Riemannian score-based generative modelling VD Bortoli, E Mathieu, MJ Hutchinson, J Thornton, YW Teh, A Doucet Advances in Neural Information Processing Systems, 46, 2022 | 20 | 2022 |
| Riemannian diffusion schr\" odinger bridge J Thornton, M Hutchinson, E Mathieu, V De Bortoli, YW Teh, A Doucet arXiv preprint arXiv:2207.03024, 2022 | 18 | 2022 |
| 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 | 17 | 2024 |
| On contrastive representations of stochastic processes E Mathieu, A Foster, Y Teh Advances in Neural Information Processing Systems 34, 28823-28835, 2021 | 17 | 2021 |
| SE (3) equivariant augmented coupling flows L Midgley, V Stimper, J Antorán, E Mathieu, B Schölkopf, ... Advances in Neural Information Processing Systems 36, 2024 | 16 | 2024 |
| A dynamic simulation model to support reduction in illegal trade within legal wildlife markets R Oyanedel, S Gelcich, E Mathieu, EJ Milner‐Gulland Conservation Biology 36 (2), e13814, 2022 | 15 | 2022 |
| Spectral diffusion processes A Phillips, T Seror, M Hutchinson, V De Bortoli, A Doucet, E Mathieu arXiv preprint arXiv:2209.14125, 2022 | 13 | 2022 |
| On incorporating inductive biases into vaes N Miao, E Mathieu, N Siddharth, YW Teh, T Rainforth arXiv preprint arXiv:2106.13746, 2021 | 13* | 2021 |
| Metropolis sampling for constrained diffusion models N Fishman, L Klarner, E Mathieu, M Hutchinson, V De Bortoli Advances in Neural Information Processing Systems 36, 2024 | 12 | 2024 |
| Learning instance-specific data augmentations N Miao, E Mathieu, Y Dubois, T Rainforth, YW Teh, A Foster, H Kim arXiv preprint arXiv:2206.00051, 2022 | 12* | 2022 |
| Sampling and inference for beta neutral-to-the-left models of sparse networks B Bloem-Reddy, A Foster, E Mathieu, YW Teh arXiv preprint arXiv:1807.03113, 2018 | 11 | 2018 |
| A framework for conditional diffusion modelling with applications in motif scaffolding for protein design K Didi, F Vargas, SV Mathis, V Dutordoir, E Mathieu, UJ Komorowska, ... arXiv preprint arXiv:2312.09236, 2023 | 9 | 2023 |
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindEmail verificata su stats.ox.ac.uk