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We present a framework for learning probability distributions on topologically non-trivial manifolds, utilizing normalizing flows.
A RAD approach to deep mixture models
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, and Hugo Larochelle · 1903
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Michael F Hutchinson · 1989
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Fitting mixtures of kent distributions to aid in joint set identification
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The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth
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Jacinto C. Nascimento, Jorge G. Silva, Jorge S. Marques, and João Miranda Lemos · 2014
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan · 2016
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Normalizing flows on riemannian manifolds
Mevlana C Gemici, Danilo Rezende, and Shakir Mohamed · 2016
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Johann Brehmer and Kyle Cranmer · 2020
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Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer · 2020
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Rob Cornish, Anthony Caterini, George Deligiannidis, and Arnaud Doucet · 2020
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Chart auto-encoders for manifold structured data
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Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, and Christopher De Sa · 2020
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Riemannian continuous normalizing flows
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Pie: Pseudo-invertible encoder
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Moser flow: Divergence-based generative modeling on manifolds
Noam Rozen, Aditya Grover, Maximilian Nickel, and Yaron Lipman · 2021
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