Emile Mathieu

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 32

Top Topics

Fisher Information

Riemannian Metric

Diffusion Models

Diffusion Process

Top Venues

Trans. Mach. Learn. Res.

Publications

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
Improved motif-scaffolding with SE(3) flow matching. Trans. Mach. Learn. Res. 2024 (2024)
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
Improved motif-scaffolding with SE(3) flow matching. CoRR (2024)
Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio
DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. CoRR (2024)
Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael Hutchinson
Diffusion Models for Constrained Domains. CoRR (2023)
Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
SE(3) Equivariant Augmented Coupling Flows. CoRR (2023)
Kieran Didi, Francisco Vargas, Simon V. Mathis, Vincent Dutordoir, Emile Mathieu, Urszula J. Komorowska, Pietro Lio
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. CoRR (2023)
Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
Geometric Neural Diffusion Processes. CoRR (2023)
Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson
Diffusion Models for Constrained Domains. Trans. Mach. Learn. Res. 2023 (2023)
Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim
Learning Instance-Specific Augmentations by Capturing Local Invariances. ICML (2023)
Emile Mathieu, Vincent Dutordoir, Michael Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
Geometric Neural Diffusion Processes. NeurIPS (2023)
Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola
SE(3) diffusion model with application to protein backbone generation. ICML (2023)
Nic Fishman, Leo Klarner, Emile Mathieu, Michael Hutchinson, Valentin De Bortoli
Metropolis Sampling for Constrained Diffusion Models. NeurIPS (2023)
Nic Fishman, Leo Klarner, Emile Mathieu, Michael J. Hutchinson, Valentin De Bortoli
Metropolis Sampling for Constrained Diffusion Models. CoRR (2023)
Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
SE(3) Equivariant Augmented Coupling Flows. NeurIPS (2023)
Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola
SE(3) diffusion model with application to protein backbone generation. CoRR (2023)
Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth
On Incorporating Inductive Biases into VAEs. ICLR (2022)
Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim
Learning Instance-Specific Data Augmentations. CoRR (2022)
James Thornton, Michael Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet
Riemannian Diffusion Schrödinger Bridge. CoRR (2022)
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet
Riemannian Score-Based Generative Modeling. CoRR (2022)
Angus Phillips, Thomas Seror, Michael Hutchinson, Valentin De Bortoli, Arnaud Doucet, Emile Mathieu
Spectral Diffusion Processes. CoRR (2022)
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet
Riemannian Score-Based Generative Modelling. NeurIPS (2022)
Emile Mathieu, Adam Foster, Yee Whye Teh
On Contrastive Representations of Stochastic Processes. CoRR (2021)
Emile Mathieu, Adam Foster, Yee Whye Teh
On Contrastive Representations of Stochastic Processes. NeurIPS (2021)
Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth
InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. CoRR (2021)
Emile Mathieu, Maximilian Nickel
Riemannian Continuous Normalizing Flows. CoRR (2020)
Emile Mathieu, Maximilian Nickel
Riemannian Continuous Normalizing Flows. NeurIPS (2020)
Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
Hierarchical Representations with Poincaré Variational Auto-Encoders. CoRR (2019)
Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh
Disentangling Disentanglement in Variational Autoencoders. ICML (2019)
Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. NeurIPS (2019)
Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. CoRR (2018)
Emile Mathieu, Tom Rainforth, Siddharth Narayanaswamy, Yee Whye Teh
Disentangling Disentanglement. CoRR (2018)
Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. UAI (2018)