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Coupling normalizing flows allow for fast sampling and density evaluation, making them the tool of choice for probabilistic modeling of physical systems.
A family of embedded runge-kutta formulae
John R Dormand and Peter J Prince · 1980
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A User’s Guide to Measure Theoretic Probability
David Pollard · 2002
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Divergence measures and message passing
Tom Minka · 2005
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Replica-exchange molecular dynamics simulations for various constant temperature algorithms
Yoshiharu Mori and Yuko Okamoto · 2010
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Linear Algebra
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Adam: A method for stochastic optimization
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Steerable cnns
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Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Se(3)-transformers: 3d roto-translation equivariant attention networks
Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, and Max Welling · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
Chin-Wei Huang, Laurent Dinh, and Aaron Courville · 2020
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Equivariant flows: Exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noé · 2020
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Normalizing flows on tori and spheres
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, and Kyle Cranmer · 2020
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Stochastic normalizing flows
Hao Wu, Jonas Köhler, and Frank Noé · 2020
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A gradient based strategy for Hamiltonian Monte Carlo hyperparameter optimization
Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi S. Jaakkola · 2022
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Spherical message passing for 3d molecular graphs
Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2022
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Resampling Base Distributions of Normalizing Flows
Vincent Stimper, Bernhard Schölkopf, and José Miguel Hernández-Lobato · 2022
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Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2022
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GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Andrew Campbell, Wenlong Chen, Vincent Stimper, Jose Miguel Hernandez-Lobato, and Yichuan Zhang · 2021
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Highly accurate protein structure prediction with AlphaFold
John M. Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andy Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David A. Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Normalizing flows for probabilistic modeling and inference
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E(n) equivariant normalizing flows
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E(n) equivariant graph neural networks
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MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Ilyes Batatia, David Peter Kovacs, Gregor N. C. Simm, Christoph Ortner, and Gabor Csanyi · 2022
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Qinsheng Zhang and Yongxin Chen · 2022
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Yi-Lun Liao and Tess Smidt · 2023
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Flow matching for generative modeling
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Flow annealed importance sampling bootstrap
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Learning local equivariant representations for large-scale atomistic dynamics
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Improving and generalizing flow-based generative models with minibatch optimal transport
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Denoising diffusion samplers
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SE(3) diffusion model with application to protein backbone generation
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