Emile Mathieu

According to our database, Emile Mathieu authored at least 24 papers between 2018 and 2024.

Collaborative distances:

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

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

Trans. Mach. Learn. Res., 2024

On conditional diffusion models for PDE simulations.

Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu

CoRR, 2024

DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform.

Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

CoRR, 2024

2023

Diffusion Models for Constrained Domains.

Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson

Trans. Mach. Learn. Res., 2023

A framework for conditional diffusion modelling with applications in motif scaffolding for protein design.

Kieran Didi, Francisco Vargas, Simon V. Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

CoRR, 2023

SE(3) Equivariant Augmented Coupling Flows.

Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato

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

Geometric Neural Diffusion Processes.

Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner

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

Metropolis Sampling for Constrained Diffusion Models.

Nic Fishman, Leo Klarner, Emile Mathieu, Michael J. Hutchinson, Valentin De Bortoli

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

SE(3) diffusion model with application to protein backbone generation.

Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola

Proceedings of the International Conference on Machine Learning, 2023

Learning Instance-Specific Augmentations by Capturing Local Invariances.

Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim

Proceedings of the International Conference on Machine Learning, 2023

2022

Spectral Diffusion Processes.

Angus Phillips, Thomas Seror, Michael J. Hutchinson, Valentin De Bortoli, Arnaud Doucet, Emile Mathieu

CoRR, 2022

Riemannian Diffusion Schrödinger Bridge.

James Thornton, Michael J. Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet

CoRR, 2022

Learning Instance-Specific Data Augmentations.

Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim

CoRR, 2022

Riemannian Score-Based Generative Modeling.

Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet

CoRR, 2022

Riemannian Score-Based Generative Modelling.

Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Incorporating Inductive Biases into VAEs.

Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents.

Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth

CoRR, 2021

On Contrastive Representations of Stochastic Processes.

Emile Mathieu, Adam Foster, Yee Whye Teh

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

2020

Riemannian Continuous Normalizing Flows.

Emile Mathieu, Maximilian Nickel

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

2019

Hierarchical Representations with Poincaré Variational Auto-Encoders.

Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

CoRR, 2019

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders.

Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Disentangling Disentanglement in Variational Autoencoders.

Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Disentangling Disentanglement.

Emile Mathieu, Tom Rainforth, Siddharth Narayanaswamy, Yee Whye Teh

CoRR, 2018

Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks.

Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018