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Computing optimal transport (OT) for general high-dimensional data has been a long-standing challenge.
Simulating ratios of normalizing constants via a simple identity: a theoretical exploration
Xiao-Li Meng and Wing Hung Wong · 1996
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Inferences for case-control and semiparametric two-sample density ratio models
Jing Qin · 1998
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A computational fluid mechanics solution to the monge-kantorovich mass transfer problem
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Annealed importance sampling
Radford M Neal · 2001
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The mnist database of handwritten digits
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Direct importance estimation for covariate shift adaptation
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Discriminative learning under covariate shift
Steffen Bickel, Michael Brückner, and Tobias Scheffer · 2009
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Covariate shift by kernel mean matching
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Change-point detection in time-series data by direct density-ratio estimation
Yoshinobu Kawahara and Masashi Sugiyama · 2009
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Optimal transport: old and new , volume 338
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Density estimation by dual ascent of the log-likelihood
Esteban G Tabak and Eric Vanden-Eijnden · 2010
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Density ratio estimation in machine learning
Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori · 2012
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Domain adaptation with regularized optimal transport
Nicolas Courty, Rémi Flamary, and Devis Tuia · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Fine-grained visual comparisons with local learning
Aron Yu and Kristen Grauman · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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A note on the evaluation of generative models
L Theis, A van den Oord, and M Bethge · 2016
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Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2016
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Joint distribution optimal transportation for domain adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy · 2017
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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Learning to discover cross-domain relations with generative adversarial networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, and Jiwon Kim · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm · 2018
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 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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Three-player wasserstein gan via amortised duality
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Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
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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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Non-negative bregman divergence minimization for deep direct density ratio estimation
Masahiro Kato and Takeshi Teshima · 2021
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Ot-flow: Fast and accurate continuous normalizing flows via optimal transport
Derek Onken, S Wu Fung, Xingjian Li, and Lars Ruthotto · 2021
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Density ratio estimation via infinitesimal classification
Kristy Choi, Chenlin Meng, Yang Song, and Stefano Ermon · 2022
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Scalable reversible generative models with free-form continuous dynamics
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Wasserstein gan with quadratic transport cost
Huidong Liu, Xianfeng Gu, and Dimitris Samaras · 2019
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Training neural networks for likelihood/density ratio estimation
George V Moustakides and Kalliopi Basioti · 2019
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Computational optimal transport: With applications to data science
Gabriel Peyré, Marco Cuturi, et al · 2019
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Amirhossein Taghvaei and Amin Jalali · 2019
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On scalable and efficient computation of large scale optimal transport
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Progressive distillation for fast sampling of diffusion models
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Invertible neural networks for graph prediction
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Building normalizing flows with stochastic interpolants
Michael Samuel Albergo and Eric Vanden-Eijnden · 2023
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On amortizing convex conjugates for optimal transport
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Diffusion schrödinger bridge matching
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Improving and generalizing flow-based generative models with minibatch optimal transport
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