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Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains.
An entropy approach to the time reversal of diffusion processes
Hans Föllmer · 1984
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Time reversal of diffusions
Ulrich G Haussmann and Etienne Pardoux · 1986
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Paolo Dai Pra · 1991
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MNIST handwritten digit database
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Pascal Vincent · 2011
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Optimal control as a graphical model inference problem
Hilbert J Kappen, Vicenç Gómez, and Manfred Opper · 2012
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Introduction to Stochastic Calculus with Applications
Fima C Klebaner · 2012
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Diederik P. Kingma and Jimmy Ba · 2015
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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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Adaptive importance sampling for control and inference
Hilbert J Kappen and Hans Christian Ruiz · 2016
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Molecular Dynamics with Deterministic and Stochastic Numerical Methods
Ben Leimkuhler and Charles Matthews · 2016
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Toward automatic model comparison: an adaptive sequential monte carlo approach
Yan Zhou, Adam M Johansen, and John AD Aston · 2016
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James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Neural ordinary differential equations
Diffusion Schrödinger bridge with applications to score-based generative modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
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MCMC variational inference via uncorrected Hamiltonian annealing
Tomas Geffner and Justin Domke · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 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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Monte Carlo variational auto-encoders
Achille Thin, Nikita Kotelevskii, Alain Durmus, Eric Moulines, Maxim Panov, and Arnaud Doucet · 2021
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Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Multi-object representation learning with iterative variational inference
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Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen and Maxim Raginsky · 2019
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Quantum ground states from reinforcement learning
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Sampling via controlled stochastic dynamical systems
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Differentiable annealed importance sampling and the perils of gradient noise
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Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, and Anru R. Zhang · 2022
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Convergence of denoising diffusion models under the manifold hypothesis
Valentin De Bortoli · 2022
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Score-based generative modeling with critically-damped Langevin diffusion
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Score-based diffusion meets annealed importance sampling
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Langevin diffusion variational inference
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Emiel Hoogeboom and Tim Salimans · 2022
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Torsional diffusion for molecular conformer generation
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Elucidating the design space of diffusion-based generative models
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Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, and Yixin Tan · 2022
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Path integral sampler: a stochastic control approach for sampling
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