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Recently, a series of papers proposed deep learning-based approaches to sample from target distributions using controlled diffusion processes, being trained only on the unnormalized target densities without access to samples.
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A variational perspective on diffusion-based generative models and score matching
Chin-Wei Huang, Jae Hyun Lim, and Aaron C Courville · 2021
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Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Solving high-dimensional Hamilton–Jacobi–Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space
Nikolas Nüsken and Lorenz Richter · 2021
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric T Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Stochastic flows and jump-diffusions
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Score-based generative modeling in latent space
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Machine-learning approaches for the empirical Schrödinger bridge problem
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Path integral stochastic optimal control for sampling transition paths
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Neural Lagrangian Schrödinger bridge
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Deep generalized Schrödinger bridge
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On local entropy, stochastic control and deep neural networks
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Robust SDE-based variational formulations for solving linear PDEs via deep learning
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Resampling base distributions of normalizing flows
Vincent Stimper, Bernhard Schölkopf, and José Miguel Hernández-Lobato · 2022
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Path Integral Sampler: a stochastic control approach for sampling
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Flow annealed importance sampling bootstrap
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, and José Miguel Hernández-Lobato · 2023
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