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ORCIDEmile Mathieu
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2020 – today
- 2024
[j2]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)- [i18]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 abs/2401.04082 (2024) - [i17]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 abs/2406.01781 (2024) - 2023
- [j1]Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson:
Diffusion Models for Constrained Domains. Trans. Mach. Learn. Res. 2023 (2023) - [c12]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: 24720-24736 - [c11]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: 40001-40039 - [c10]Nic Fishman, Leo Klarner, Emile Mathieu, Michael J. Hutchinson, Valentin De Bortoli:
Metropolis Sampling for Constrained Diffusion Models. NeurIPS 2023 - [c9]Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner:
Geometric Neural Diffusion Processes. NeurIPS 2023 - [c8]Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato:
SE(3) Equivariant Augmented Coupling Flows. NeurIPS 2023 - [i16]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 abs/2302.02277 (2023) - [i15]Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson:
Diffusion Models for Constrained Domains. CoRR abs/2304.05364 (2023) - [i14]Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner:
Geometric Neural Diffusion Processes. CoRR abs/2307.05431 (2023) - [i13]Nic Fishman, Leo Klarner, Emile Mathieu, Michael J. Hutchinson, Valentin De Bortoli:
Metropolis Sampling for Constrained Diffusion Models. CoRR abs/2307.05439 (2023) - [i12]Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato:
SE(3) Equivariant Augmented Coupling Flows. CoRR abs/2308.10364 (2023) - [i11]Kieran Didi, Francisco Vargas, Simon V. Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio:
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. CoRR abs/2312.09236 (2023) - 2022
- [c7]Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth:
On Incorporating Inductive Biases into VAEs. ICLR 2022 - [c6]Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet:
Riemannian Score-Based Generative Modelling. NeurIPS 2022 - [i10]Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet:
Riemannian Score-Based Generative Modeling. CoRR abs/2202.02763 (2022) - [i9]Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim:
Learning Instance-Specific Data Augmentations. CoRR abs/2206.00051 (2022) - [i8]James Thornton, Michael J. Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet:
Riemannian Diffusion Schrödinger Bridge. CoRR abs/2207.03024 (2022) - [i7]Angus Phillips, Thomas Seror, Michael J. Hutchinson, Valentin De Bortoli, Arnaud Doucet, Emile Mathieu:
Spectral Diffusion Processes. CoRR abs/2209.14125 (2022) - 2021
- [c5]Emile Mathieu, Adam Foster, Yee Whye Teh:
On Contrastive Representations of Stochastic Processes. NeurIPS 2021: 28823-28835 - [i6]Emile Mathieu, Adam Foster, Yee Whye Teh:
On Contrastive Representations of Stochastic Processes. CoRR abs/2106.10052 (2021) - [i5]Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth:
InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. CoRR abs/2106.13746 (2021) - 2020
- [c4]Emile Mathieu, Maximilian Nickel:
Riemannian Continuous Normalizing Flows. NeurIPS 2020 - [i4]Emile Mathieu, Maximilian Nickel:
Riemannian Continuous Normalizing Flows. CoRR abs/2006.10605 (2020)
2010 – 2019
- 2019
- [c3]Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh:
Disentangling Disentanglement in Variational Autoencoders. ICML 2019: 4402-4412 - [c2]Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh:
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. NeurIPS 2019: 12544-12555 - [i3]Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh:
Hierarchical Representations with Poincaré Variational Auto-Encoders. CoRR abs/1901.06033 (2019) - 2018
- [c1]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: 477-486 - [i2]Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh:
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. CoRR abs/1807.03113 (2018) - [i1]Emile Mathieu, Tom Rainforth, Siddharth Narayanaswamy, Yee Whye Teh:
Disentangling Disentanglement. CoRR abs/1812.02833 (2018)
Coauthor Index
aka: Michael John Hutchinson
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