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Emile Mathieu
Emile Mathieu
Postdoctoral Research Associate, University of Cambridge
Dirección de correo verificada de cam.ac.uk - Página principal
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Citado por
Citado por
Año
Disentangling disentanglement in variational autoencoders
E Mathieu, T Rainforth, N Siddharth, YW Teh
International conference on machine learning, 4402-4412, 2019
3002019
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
2032023
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
187*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
1422022
Riemannian continuous normalizing flows
E Mathieu, M Nickel
Advances in Neural Information Processing Systems 33, 2503-2515, 2020
992020
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
952022
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
672023
Diffusion Models for Constrained Domains
N Fishman, L Klarner, V De Bortoli, E Mathieu, M Hutchinson
arXiv preprint arXiv:2304.05364, 2023
122023
Riemannian Diffusion Schr\" odinger Bridge
J Thornton, M Hutchinson, E Mathieu, V De Bortoli, YW Teh, A Doucet
arXiv preprint arXiv:2207.03024, 2022
112022
On contrastive representations of stochastic processes
E Mathieu, A Foster, Y Teh
Advances in Neural Information Processing Systems 34, 28823-28835, 2021
112021
On incorporating inductive biases into VAEs
N Miao, E Mathieu, N Siddharth, YW Teh, T Rainforth
arXiv preprint arXiv:2106.13746, 2021
11*2021
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
102018
Spectral diffusion processes
A Phillips, T Seror, M Hutchinson, V De Bortoli, A Doucet, E Mathieu
arXiv preprint arXiv:2209.14125, 2022
92022
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
5*2022
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
52022
Sampling and inference for discrete random probability measures in probabilistic programs
ZG Benjamin Bloem-Reddy, Emile Mathieu, Adam Foster, Tom Rainforth, Yee Whye ...
NIPS 2017 Workshop on Advances in Approximate Bayesian Inference, 2017
42017
SE (3) equivariant augmented coupling flows
L Midgley, V Stimper, J Antorán, E Mathieu, B Schölkopf, ...
Advances in Neural Information Processing Systems 36, 2024
32024
Learning Instance-Specific Augmentations by Capturing Local Invariances
N Miao, T Rainforth, E Mathieu, Y Dubois, YW Teh, A Foster, H Kim
22023
Geometric neural diffusion processes
E Mathieu, V Dutordoir, M Hutchinson, V De Bortoli, YW Teh, R Turner
Advances in Neural Information Processing Systems 36, 2024
12024
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
12023
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Artículos 1–20