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Normalizing flows map an independent set of latent variables to their samples using a bijective transformation.
Neutra-lizing bad geometry in hamiltonian monte carlo using neural transport
Matthew D. Hoffman, Pavel Sountsov, Josh Dillon, Ian Langmore, Dustin Tran, and Srinivas Vasudevan · 1903
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Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 1912
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A note on the delta method
Gary W. Oehlert · 1992
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Nonlinear independent component analysis: Existence and uniqueness results
Aapo Hyvärinen and Petteri Pajunen · 1999
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Normalizing Flows Across Dimensions
Edmond Cunningham, Renos Zabounidis, Abhinav Agrawal, Ina Fiterau, and Daniel Sheldon · 2006
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Amos Gropp, Matan Atzmon, and Yaron Lipman · 2006
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Principal Component Analysis , pages 1094–1096
Ian Jolliffe · 2011
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Testing the Manifold Hypothesis
Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan · 2013
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Normalizing Flows on Riemannian Manifolds
Mevlana C. Gemici, Danilo Rezende, and Shakir Mohamed · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Sylvester normalizing flows for variational inference
Rianne van den Berg, Leonard Hasenclever, Jakub Tomczak, and Max Welling · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Glow: Generative Flow with Invertible 1x1 Convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2018
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Relaxing bijectivity constraints with continuously indexed normalising flows, 2019
Rob Cornish, Anthony L. Caterini, George Deligiannidis, and Arnaud Doucet · 2019
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Residual Flows for Invertible Generative Modeling
Ricky T. Q. Chen, Jens Behrmann, David K Duvenaud, and Joern-Henrik Jacobsen · 2019
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A style-based generator architecture for generative adversarial networks
Adabelief optimizer: Adapting stepsizes by the belief in observed gradients
Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar C Tatikonda, Nicha Dvornek, Xenophon Papademetris, and James Duncan · 2020
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Rectangular Flows for Manifold Learning
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, and John Patrick Cunningham · 2021
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Diffeomorphic explanations with normalizing flows
Ann-Kathrin Dombrowski, Jan E Gerken, and Pan Kessel · 2021
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Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, and Aaron Courville · 2021
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Independent mechanism analysis, a new concept?
Luigi Gresele, Julius Von Kügelgen, Vincent Stimper, Bernhard Schölkopf, and Michel Besserve · 2021
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Tero Karras, Samuli Laine, and Timo Aila · 2019
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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
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Neural Spline Flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
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Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
Lynton Ardizzone, Radek Mackowiak, Carsten Rother, and Ullrich Köthe · 2020
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Flows for simultaneous manifold learning and density estimation
Johann Brehmer and Kyle Cranmer · 2020
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Regularized autoencoders via relaxed injective probability flow
Abhishek Kumar, Ben Poole, and Kevin Murphy · 2020
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Normalizing Flows on Tori and Spheres
Danilo Jimenez Rezende, George Papamakarios, Sebastien Racaniere, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, and Kyle Cranmer · 2020
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Dimitris Kalatzis, Johan Ziruo Ye, Jesper Wohlert, and Søren Hauberg · 2021
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Trumpets: Injective Flows for Inference and Inverse Problems
Konik Kothari, AmirEhsan Khorashadizadeh, Maarten de Hoop, and Ivan Dokmanić · 2021
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On the latent space of flow-based models, 2021
Mingtian Zhang, Yitong Sun, Steven McDonagh, and Chen Zhang · 2021
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A change of variables method for rectangular matrix-vector products
Edmond Cunningham and Madalina Fiterau · 2021
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Conformal embedding flows: Tractable density estimation on learned manifolds
Brendan Leigh Ross and Jesse C Cresswell · 2021
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Orthogonal jacobian regularization for unsupervised disentanglement in image generation
Yuxiang Wei, Yupeng Shi, Xiao Liu, Zhilong Ji, Yuan Gao, Zhongqin Wu, and Wangmeng Zuo · 2021
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Principal component density estimation for scenario generation using normalizing flows
Eike Cramer, Alexander Mitsos, Raul Tempone, and Manuel Dahmen · 2021
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Generative locally linear embedding
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, and Mark Crowley · 2021
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