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Diffusion models are generative models that have recently demonstrated impressive performances in terms of sampling quality and density estimation in high dimensions.
MCMC using Hamiltonian dynamics
Radford M. Neal · 1901
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Yang Song and Stefano Ermon · 1907
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Hybrid Monte Carlo
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Introduction to Hilbert spaces with applications
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Ordinary Differential Equations and Dynamical Systems
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Large-scale log-determinant computation through stochastic chebyshev expansions
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Deep unsupervised learning using nonequilibrium thermodynamics
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Conservativeness of untied auto-encoders
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Gauge freedom within the class of linear feedback particle filters
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Principal Component Flows
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Compositional Visual Generation with Composable Diffusion Models
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Lidl: Local intrinsic dimension estimation using approximate likelihood
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Score-based generative model learn manifold-like structures with constrained mixing
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Diffusion Models: A Comprehensive Survey of Methods and Applications, October 2022
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Sreyas Mohan, Zahra Kadkhodaie, Eero P Simoncelli, and Carlos Fernandez-Granda · 2019
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Generative adversarial networks
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Your diffusion model secretly knows the dimension of the data manifold
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