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Generative modeling seeks to uncover the underlying factors that give rise to observed data that can often be modeled as the natural symmetries that manifest themselves through invariances and equivariances to certain transformation laws.
On the volume elements on a manifold
J. Moser · 1965
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Taco S Cohen and Max Welling · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
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A general theory of equivariant cnns on homogeneous spaces
Taco Cohen, Mario Geiger, and Maurice Weiler · 2018
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3d steerable cnns: Learning rotationally equivariant features in volumetric data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen · 2018
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Invertible residual networks
Jens Behrmann, Will Grathwohl, Ricky TQ Chen, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
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Emilien Dupont, Arnaud Doucet, and Yee Whye Teh · 2019
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Anode: Unconditionally accurate memory-efficient gradients for neural odes
Amir Gholami, Kurt Keutzer, and George Biros · 2019
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2019
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Set flow: A permutation invariant normalizing flow
Kashif Rasul, Ingmar Schuster, Roland Vollgraf, and Urs Bergmann · 2019
The convolution exponential and generalized sylvester flows
Emiel Hoogeboom, Victor Garcia Satorras, Jakub M Tomczak, and Max Welling · 2020
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Equivariant flow-based sampling for lattice gauge theory
Gurtej Kanwar, Michael S Albergo, Denis Boyda, Kyle Cranmer, Daniel C Hackett, Sébastien Racaniere, Danilo Jimenez Rezende, and Phiala E Shanahan · 2020
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Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon Prince, and Marcus Brubaker · 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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Generative flows with matrix exponential
Changyi Xiao and Ligang Liu · 2020
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Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, and Peter Toth · 2019
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General e ( 2 ) e(2) -equivariant steerable cnns
Maurice Weiler and Gabriele Cesa · 2019
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Exchangeable generative models with flow scans
Christopher Bender, Kevin O’Connor, Yang Li, Juan Garcia, Junier Oliva, and Manzil Zaheer · 2020
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Sampling using s u ( n ) 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 · 2020
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Approximation capabilities of neural odes and invertible residual networks
Han Zhang, Xi Gao, Jacob Unterman, and Tom Arodz · 2020
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Scalable normalizing flows for permutation invariant densities
Marin Biloš and Stephan Günnemann · 2021
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Equivariant manifold flows
Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, and Christopher De Sa · 2021
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E (n) equivariant normalizing flows for molecule generation in 3d
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B Fuchs, Ingmar Posner, and Max Welling · 2021
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