Sébastien Racanière

According to our database, Sébastien Racanière authored at least 33 papers between 2011 and 2024.

Collaborative distances:

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

2024

Applications of flow models to the generation of correlated lattice QCD ensembles.

Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2024

2023

Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics.

Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan

CoRR, 2023

Normalizing flows for lattice gauge theory in arbitrary space-time dimension.

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2023

2022

Normalizing flows for atomic solids.

Peter Wirnsberger, George Papamakarios, Borja Ibarz, Sébastien Racanière, Andrew J. Ballard, Alexander Pritzel, Charles Blundell

Mach. Learn. Sci. Technol., 2022

Symmetry-Based Representations for Artificial and Biological General Intelligence.

Irina Higgins, Sébastien Racanière, Danilo J. Rezende

Frontiers Comput. Neurosci., 2022

Aspects of scaling and scalability for flow-based sampling of lattice QCD.

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2022

Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions.

Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban

CoRR, 2022

Flow-based sampling in the lattice Schwinger model at criticality.

Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2022

2021

Implicit Riemannian Concave Potential Maps.

Danilo J. Rezende, Sébastien Racanière

CoRR, 2021

Flow-based sampling for fermionic lattice field theories.

Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

CoRR, 2021

Introduction to Normalizing Flows for Lattice Field Theory.

Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan

CoRR, 2021

2020

Physically Embedded Planning Problems: New Challenges for Reinforcement Learning.

Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt, Arthur Guez, Saran Tunyasuvunakool, Alistair Muldal, Théophane Weber, Péter Karkus, Sébastien Racanière, Lars Buesing, Timothy P. Lillicrap, Nicolas Heess

CoRR, 2020

Sampling using SU(N) gauge equivariant flows.

Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

CoRR, 2020

Equivariant flow-based sampling for lattice gauge theory.

Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan

CoRR, 2020

Disentangling by Subspace Diffusion.

David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière

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

Normalizing Flows on Tori and Spheres.

Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer

Proceedings of the 37th International Conference on Machine Learning, 2020

Hamiltonian Generative Networks.

Peter Toth, Danilo J. Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins

Proceedings of the 8th International Conference on Learning Representations, 2020

Automated curriculum generation through setter-solver interactions.

Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Differentiable Game Mechanics.

Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel

J. Mach. Learn. Res., 2019

Equivariant Hamiltonian Flows.

Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, Peter Toth

CoRR, 2019

Automated curricula through setter-solver interactions.

Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap

CoRR, 2019

An Investigation of Model-Free Planning.

Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap

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

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search.

Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sébastien Racanière, Arthur Guez, Jean-Baptiste Lespiau

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Towards a Definition of Disentangled Representations.

Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner

CoRR, 2018

Learning and Querying Fast Generative Models for Reinforcement Learning.

Lars Buesing, Theophane Weber, Sébastien Racanière, S. M. Ali Eslami, Danilo Jimenez Rezende, David P. Reichert, Fabio Viola, Frederic Besse, Karol Gregor, Demis Hassabis, Daan Wierstra

CoRR, 2018

The Mechanics of n-Player Differentiable Games.

David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Imagination-Augmented Agents for Deep Reinforcement Learning.

Theophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, David Silver, Daan Wierstra

CoRR, 2017

Learning model-based planning from scratch.

Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sébastien Racanière, David P. Reichert, Theophane Weber, Daan Wierstra, Peter W. Battaglia

CoRR, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.

Sébastien Racanière, Theophane Weber, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, Demis Hassabis, David Silver, Daan Wierstra

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Recurrent Environment Simulators.

Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed

Proceedings of the 5th International Conference on Learning Representations, 2017

2013

An FPGA-Based Data Flow Engine for Gaussian Copula Model.

Huabin Ruan, Xiaomeng Huang, Haohuan Fu, Guangwen Yang, Wayne Luk, Sébastien Racanière, Oliver Pell, Wenjing Han

Proceedings of the 21st IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2013

2012

Rapid computation of value and risk for derivatives portfolios.

Stephen Weston, James Spooner, Sébastien Racanière, Oskar Mencer

Concurr. Comput. Pract. Exp., 2012

2011

Accelerating Large-Scale HPC Applications Using FPGAs.

Robert G. Dimond, Sébastien Racanière, Oliver Pell

Proceedings of the 20th IEEE Symposium on Computer Arithmetic, 2011