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Diffusion (score-based) generative models have been widely used for modeling various types of complex data, including images, audios, and point clouds.
Hypoelliptic second order differential equations
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
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Sample complexity of testing the manifold hypothesis
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Sybren Ruurds De Groot and Peter Mazur · 2013
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Giuseppe Da Prato · 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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Christof Seiler, Simon Rubinstein-Salzedo, and Susan Holmes · 2014
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Analysis for diffusion processes on riemannian manifolds
F. Y. Wang · 2014
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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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Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan · 2016
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A conceptual introduction to hamiltonian monte carlo
Michael Betancourt · 2017
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Statistical Mechanics of Lattice Systems: A Concrete Mathematical Introduction
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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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Equivariant vector field network for many-body system modeling, 2021
Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Bin Shao, and Tie-Yan Liu · 2021
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A variational perspective on diffusion-based generative models and score matching
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Gotta go fast when generating data with score-based models, 2021
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Learning from uncertain curves: The 2-wasserstein metric for gaussian processes
Anton Mallasto and Aasa Feragen · 2017
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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Glow: Generative flow with invertible 1x1 convolutions
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Deep generative models: Survey
Achraf Oussidi and Azeddine Elhassouny · 2018
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Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Diffsvc: A diffusion probabilistic model for singing voice conversion
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Ot-flow: Fast and accurate continuous normalizing flows via optimal transport
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Learning disentangled representation by exploiting pretrained generative models: A contrastive learning view
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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Towards building a group-based unsupervised representation disentanglement framework
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