Fetching the paper…
Reading the bibliography…
Consider a one-parameter family of Boltzmann distributions $p_t(x) = \tfrac{1}{Z_t}e^{-S_t(x)}$.
Equivariant flows: Exact likelihood generative learning for symmetric densities, 2020
Jonas Köhler, Leon Klein, and Frank Noé · 2006
Earlier work this paper cites.
A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
Earlier work this paper cites.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Earlier work this paper cites.
Boltzmann generators – sampling equilibrium states of many-body systems with deep learning, 2018
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2018
Earlier work this paper cites.
Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
Earlier work this paper cites.
Neural ordinary differential equations, 2018
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
Earlier work this paper cites.
torchdiffeq, 2018
Ricky T. Q. Chen · 2018
Cited alongside, same era.
Neural spline flows, 2019
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
Cited alongside, same era.
Asymptotically unbiased estimation of physical observables with neural samplers
Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, and Pan Kessel · 2020
Cited alongside, same era.
Integrable nonparametric flows
David Pfau and Danilo Rezende · 2020
Cited alongside, same era.
Sampling using SU ( n ) \mathrm{SU}(n) gauge equivariant flows
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, and Phiala E. Shanahan · 2021
Cited alongside, same era.
Introduction to normalizing flows for lattice field theory, 2021a
Scaling up machine learning for quantum field theory with equivariant continuous flows, 2021
Pim de Haan, Corrado Rainone, Miranda C. N. Cheng, and Roberto Bondesan · 2021
Later among the works it cites.
Estimation of thermodynamic observables in lattice field theories with deep generative models
Kim A Nicoli, Christopher J Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, and Paolo Stornati · 2021
Later among the works it cites.
Flow-based sampling in the lattice schwinger model at criticality, 2022
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, and Julian M. Urban · 2022
Closest in time.
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions, 2022
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, and Julian M. Urban · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan
Cited in the paper.
Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sé bastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, and Phiala E. Shanahan
Cited in the paper.
Learning lattice quantum field theories with equivariant continuous flows, 2022
Mathis Gerdes, Pim de Haan, Corrado Rainone, Roberto Bondesan, and Miranda C. N. Cheng · 2022
Closest in time.