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The task of conditional generation is one of the most important applications of generative models, and numerous methods have been developed to date based on the celebrated flow-based models.
Sobolev-type classes of functions with values in a metric space
Yu. G. Reshetnyak · 1997
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Extended Monge–Kantorovich Theory , pages 91–121
Yann Brenier · 2003
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Gradient flows in metric spaces and in the space of probability measures
Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré · 2008
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Probability: Theory and Examples
Rick Durrett · 2019
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Harmonic mappings valued in the wasserstein space
Hugo Lavenant · 2019
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Caroline Moosmüller and Alexander Cloninger · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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ZINC 250K data sets
Tagir Akhmetshin, Arkadii I. Lin, Daniyar Mazitov, Evgenii Ziaikin, Timur Madzhidov, and Alexandre Varnek · 2021
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CcGAN: Continuous conditional generative adversarial networks for image generation
Xin Ding, Yongwei Wang, Zuheng Xu, William J Welch, and Z. Jane Wang · 2021
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Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Molecular design method using a reversible tree representation of chemical compounds and deep reinforcement learning
Ryuichiro Ishitani, Toshiki Kataoka, and Kentaro Rikimaru · 2022
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Neural Lagrangian Schrödinger bridge: Diffusion modeling for population dynamics
Takeshi Koshizuka and Issei Sato · 2022
Inferring atmospheric properties of exoplanets with flow matching and neural importance sampling
Timothy D Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P Quanz, and Bernhard Schölkopf · 2023
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Latent space editing in transformer-based flow matching
Vincent Tao Hu, David W Zhang, Meng Tang, Pascal Mettes, Deli Zhao, and Cees G. M. Snoek · 2023
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A convergence result of a continuous model of deep learning via Łojasiewicz–Simon inequality
Noboru Isobe · 2023
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Gavin Kerrigan, Giosue Migliorini, and Padhraic Smyth · 2023
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Wasserstein geodesic generator for conditional distributions
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On the complexity of approximating multimarginal optimal transport
Tianyi Lin, Nhat Ho, Marco Cuturi, and Michael I. Jordan · 2022
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Stochastic interpolants with data-dependent couplings
Michael S Albergo, Mark Goldstein, Nicholas M Boffi, Rajesh Ranganath, and Eric Vanden-Eijnden · 2023
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SE(3)-stochastic flow matching for protein backbone generation
Avishek Joey Bose, Tara Akhound-Sadegh, Kilian Fatras, Guillaume Huguet, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael Bronstein, and Alexander Tong · 2023
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Riemannian flow matching on general geometries
Ricky TQ Chen and Yaron Lipman · 2023
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Quan Dao, Hao Phung, Binh Nguyen, and Anh Tran · 2023
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Efficient video prediction via sparsely conditioned flow matching
Aram Davtyan, Sepehr Sameni, and Paolo Favaro · 2023
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Generating synthetic datasets by interpolating along generalized geodesics
Jiaojiao Fan and David Alvarez-Melis · 2023
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Young-geun Kim, Kyungbok Lee, Youngwon Choi, Joong-Ho Won, and Myunghee Cho Paik · 2023
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Leon Klein, Andreas Krämer, and Frank Noé · 2023
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Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
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Guided flows for generative modeling and decision making
Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, and Ricky TQ Chen · 2023
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Conditional wasserstein distances with applications in bayesian ot flow matching, 2024
Jannis Chemseddine, Paul Hagemann, Christian Wald, and Gabriele Steidl · 2024
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Dynamic conditional optimal transport through simulation-free flows, 2024
Gavin Kerrigan, Giosue Migliorini, and Padhraic Smyth · 2024
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Contrasting multiple representations with the multi-marginal matching gap
Zoe Piran, Michal Klein, James Thornton, and Marco Cuturi · 2024
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Improved motif-scaffolding with SE(3) flow matching
Jason Yim, Andrew Campbell, Emile Mathieu, Andrew YK Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S Veeling, Frank Noé, et al · 2024
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