Accelerating Large-Scale HPC Applications Using FPGAs. - Robert G. Dimond; Sébastien Racanière; Oliver Pell (IEEE Symposium on Computer Arithmetic, 2011)

Rapid computation of value and risk for derivatives portfolios. - Stephen Weston; James Spooner; Sébastien Racanière; Oskar Mencer (Concurr. Comput. Pract. Exp., 2012)

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 (FCCM, 2013)

Recurrent Environment Simulators. - Silvia Chiappa; Sébastien Racanière; Daan Wierstra; Shakir Mohamed (ICLR (Poster), 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 001; Razvan Pascanu; Peter W. Battaglia; et al. (NIPS, 2017)

Learning model-based planning from scratch. - Razvan Pascanu; Yujia Li 001; 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. - Theophane Weber; Sébastien Racanière; David P. Reichert; Lars Buesing; Arthur Guez; Danilo Jimenez Rezende; Adrià Puigdomènech Badia; Oriol Vinyals; Nicolas Heess; Yujia Li 001; Razvan Pascanu; Peter W. Battaglia; et al. (CoRR, 2017)

The Mechanics of n-Player Differentiable Games. - David Balduzzi; Sébastien Racanière; James Martens; Jakob N. Foerster; Karl Tuyls; Thore Graepel (ICML, 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)

Towards a Definition of Disentangled Representations. - Irina Higgins; David Amos; David Pfau; Sébastien Racanière; Loïc Matthey; Danilo J. Rezende; Alexander Lerchner (CoRR, 2018)

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. - Lars Buesing; Theophane Weber; Yori Zwols; Nicolas Heess; Sébastien Racanière; Arthur Guez; Jean-Baptiste Lespiau (ICLR (Poster), 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; et al. (ICML, 2019)

Automated curricula through setter-solver interactions. - Sébastien Racanière; Andrew K. Lampinen; Adam Santoro; David P. Reichert; Vlad Firoiu; Timothy P. Lillicrap (CoRR, 2019)

Equivariant Hamiltonian Flows. - Danilo Jimenez Rezende; Sébastien Racanière; Irina Higgins; Peter Toth (CoRR, 2019)

Differentiable Game Mechanics. - Alistair Letcher; David Balduzzi; Sébastien Racanière; James Martens; Jakob N. Foerster; Karl Tuyls; Thore Graepel (J. Mach. Learn. Res., 2019)

Automated curriculum generation through setter-solver interactions. - Sébastien Racanière; Andrew K. Lampinen; Adam Santoro; David P. Reichert; Vlad Firoiu; Timothy P. Lillicrap (ICLR, 2020)

Hamiltonian Generative Networks. - Peter Toth; Danilo J. Rezende; Andrew Jaegle; Sébastien Racanière; Aleksandar Botev; Irina Higgins (ICLR, 2020)

Normalizing Flows on Tori and Spheres. - Danilo Jimenez Rezende; George Papamakarios; Sébastien Racanière; Michael S. Albergo; Gurtej Kanwar; Phiala E. Shanahan; Kyle Cranmer (ICML, 2020)

Disentangling by Subspace Diffusion. - David Pfau; Irina Higgins; Aleksandar Botev; Sébastien Racanière (NeurIPS, 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)

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)

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)

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)

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)

Implicit Riemannian Concave Potential Maps. - Danilo J. Rezende; Sébastien Racanière (CoRR, 2021)

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)

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)

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; et al. (CoRR, 2022)

Symmetry-Based Representations for Artificial and Biological General Intelligence. - Irina Higgins; Sébastien Racanière; Danilo J. Rezende (Frontiers Comput. Neurosci., 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)

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; et al. (CoRR, 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)

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)

Advancing Geometry with AI: Multi-agent Generation of Polytopes. - Grzegorz Swirszcz; Adam Zsolt Wagner; Geordie Williamson; Sam Blackwell; Bogdan Georgiev; Alex Davies; S. M. Ali Eslami; Sébastien Racanière; Theophane Weber; Pushmeet Kohli (CoRR, 2025)

Drums of high width. - Alex Davies; Prateek Gupta; Sébastien Racanière; Grzegorz Swirszcz; Adam Zsolt Wagner; Theophane Weber; Geordie Williamson (CoRR, 2025)