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We formulate a hierarchical rectified flow to model data distributions.
A family of embedded runge-kutta formulae
John R Dormand and Peter J Prince · 1980
Earlier work this paper cites.
The eigenvalues of mega-dimensional matrices
J. Skilling · 1989
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A stochastic estimator of the trace of the influence matrix for Laplacian smoothing splines
M. Hutchinson · 1990
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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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Products and convolutions of gaussian probability density functions
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Imagenet: A large-scale hierarchical image database
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A family of nonparametric density estimation algorithms
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Generative adversarial nets
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Earlier work this paper cites.
Auto-Encoding Variational Bayes
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Variational inference with normalizing flows
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Deep unsupervised learning using nonequilibrium thermodynamics
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Density estimation using real nvp
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2017
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Neural ordinary differential equations
R. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud · 2018
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FFJORD: Free-form continuous dynamics for scalable reversible generative models
W. Grathwohl, R. Chen, J. Bettencourt, I. Sutskever, and D. Duvenaud · 2018
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Random Ordinary Differential Equations and Their Numerical Solution
Xiaoying. Han · 2018
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Neural autoregressive flows
C.-W. Huang, D. Krueger, A. Lacoste, and A. Courville · 2018
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Neural spline flows
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios · 2019
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Scalable reversible generative models with free-form continuous dynamics
W. Grathwohl, R. Chen, J. Bettencourt, and D. Duvenaud · 2019
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Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, J. Ho, D. J. Fleet, and M. Mohammad Norouzi · 2022
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Solving inverse problems in medical imaging with score-based generative models
Y. Song, L. Shen, L. Xing, and S. Ermon · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Z. Xiao, K. Kreis, and A. Vahdat · 2022
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Building normalizing flows with stochastic interpolants
M. Albergo and E. Vanden-Eijnden · 2023
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Stochastic interpolants: A unifying framework for flows and diffusions
M. Albergo, N. Boffi, and E. Vanden-Eijnden · 2023
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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OT-Flow: Fast and accurate continuous normalizing flows via optimal transport
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Moser flow: Divergence-based generative modeling on manifolds
N. Rozen, A. Grover, M. Nickel, and Y. Lipman · 2021
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Matching normalizing flows and probability paths on manifolds
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Equivariant diffusion for molecule generation in 3d
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DALL-E-Bot: Introducing web-scale diffusion models to robotics
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Flow matching for generative modeling
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Flow straight and fast: Learning to generate and transfer data with rectified flow
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Multisample flow matching: Straightening flows with minibatch couplings
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Cold diffusion: Inverting arbitrary image transforms without noise
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Diffusion models in bioinformatics and computational biology
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Improving and generalizing flow-based generative models with minibatch optimal transport
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Variational Rectified Flow Matching
P. Guo and A. G. Schwing · 2025
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