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Flow matching (FM) has gained significant attention as a simulation-free generative model.
Multivariate density estimation: Theory, practice, and visualization
David W Scott · 1992
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Optimal approximation of piecewise smooth functions using deep ReLU neural networks
Philipp Petersen and Felix Voigtlaender · 2018
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Deep ReLU network approximation of functions on a manifold
Johannes Schmidt-Hieber · 2019
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Adaptivity of deep ReLU network for learning in besov and mixed smooth besov spaces: optimal rate and curse of dimensionality
Taiji Suzuki · 2019
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On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces
Satoshi Hayakawa and Taiji Suzuki · 2020
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Adaptive approximation and generalization of deep neural network with intrinsic dimensionality
Ryumei Nakada and Masaaki Imaizumi · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Equivariant diffusion for molecule generation in 3D
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Minimax estimation of smooth densities in Wasserstein distance
Jonathan Niles-Weed and Quentin Berthet · 2022
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Building normalizing flows with stochastic interpolants
Michael Samuel Albergo and Eric Vanden-Eijnden · 2023
Earlier work this paper cites.
SE(3)-Stochastic flow matching for protein backbone generation
Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet, Kilian Fatras, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael M Bronstein, and Alexander Tong · 2023
Cited alongside, same era.
Improved analysis of score-based generative modeling: User-Friendly bounds under minimal smoothness assumptions
Hongrui Chen, Holden Lee, and Jianfeng Lu · 2023
Cited alongside, same era.
3D equivariant diffusion for Target-Aware molecule generation and affinity prediction
Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma · 2023
Cited alongside, same era.
Motion flow matching for human motion synthesis and editing
Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma, Yunlu Chen, Basura Fernando, Yuki M Asano, Efstratios Gavves, Pascal Mettes, Bjorn Ommer, and Cees G M Snoek · 2023
Cited alongside, same era.
Functional flow matching
Gavin Kerrigan, Giosue Migliorini, and Padhraic Smyth · 2023
Cited alongside, same era.
Neural optimal transport with Lagrangian costs
Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, and Brandon Amos · 2023
Later among the works it cites.
Mixed continuous and categorical flow matching for 3D de novo molecule generation
Ian Dunn and David Ryan Koes · 2024
Closest in time.
Scaling rectified flow transformers for High-Resolution image synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach · 2024
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Flow matching for conditional text generation in a few sampling steps
Vincent Hu, Di Wu, Yuki Asano, Pascal Mettes, Basura Fernando, Björn Ommer, and Cees Snoek · 2024
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Extended flow matching: a method of conditional generation with generalized continuity equation
Noboru Isobe, Masanori Koyama, Kohei Hayashi, and Kenji Fukumizu · 2024
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Leon Klein, Andreas Krämer, and Frank No’e · 2023
Cited alongside, same era.
Voicebox: Text-Guided multilingual universal speech generation at scale
Matthew Le, Apoorv Vyas, Bowen Shi, Brian Karrer, Leda Sari, Rashel Moritz, Mary Williamson, Vimal Manohar, Yossi Adi, Jay Mahadeokar, and Wei-Ning Hsu · 2023
Cited alongside, same era.
Flow matching for generative modeling
Yaron Lipman, Ricky T Q Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
Cited alongside, same era.
Diffusion models are minimax optimal distribution estimators
Kazusato Oko, Shunta Akiyama, and Taiji Suzuki · 2023
Cited alongside, same era.
Multimarginal generative modeling with stochastic interpolants
Michael S Albergo, Nicholas M Boffi, Michael Lindsey, and Eric Vanden-Eijnden
Cited in the paper.
Stochastic interpolants: A unifying framework for flows and diffusions, 2023b
Michael S. Albergo, Nicholas M. Boffi, and Eric Vanden-Eijnden
Cited in the paper.
Stochastic interpolants with data-dependent couplings
Michael S Albergo, Mark Goldstein, Nicholas M Boffi, Rajesh Ranganath, and Eric Vanden-Eijnden
Cited in the paper.
Convergence analysis of flow matching in latent space with transformers
Yuling Jiao, Yanming Lai, Yang Wang, and Bokai Yan · 2024
Closest in time.
Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, and Yoshua Bengio · 2024
Closest in time.
Consistency flow matching: Defining straight flows with velocity consistency
Ling Yang, Zixiang Zhang, Zhilong Zhang, Xingchao Liu, Minkai Xu, Wentao Zhang, Chenlin Meng, Stefano Ermon, and Bin Cui · 2024
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Minimax optimality of score-based diffusion models: Beyond the density lower bound assumptions
Kaihong Zhang, Heqi Yin, Feng Liang, and Jingbo Liu · 2024
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