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To smooth a cloud or to pin it down: Guarantees and Insights on Score
  Matching in Denoising Diffusion Models

To smooth a cloud or to pin it down: Guarantees and Insights on Score Matching in Denoising Diffusion Models

16 May 2023
Francisco Vargas
Teodora Reu
A. Kerekes
Michael M Bronstein
    DiffM
ArXivPDFHTML

Papers citing "To smooth a cloud or to pin it down: Guarantees and Insights on Score Matching in Denoising Diffusion Models"

14 / 14 papers shown
Title
Score-based generative models break the curse of dimensionality in
  learning a family of sub-Gaussian probability distributions
Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian probability distributions
Frank Cole
Yulong Lu
DiffM
51
5
0
12 Feb 2024
R-divergence for Estimating Model-oriented Distribution Discrepancy
R-divergence for Estimating Model-oriented Distribution Discrepancy
Zhilin Zhao
Longbing Cao
111
1
0
02 Oct 2023
An optimal control perspective on diffusion-based generative modeling
An optimal control perspective on diffusion-based generative modeling
Julius Berner
Lorenz Richter
Karen Ullrich
DiffM
101
91
0
02 Nov 2022
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity
Jian Huang
Yuling Jiao
Lican Kang
Xu Liao
Jin Liu
Yanyan Liu
67
28
0
21 Jun 2021
Diffusion Schrödinger Bridge with Applications to Score-Based
  Generative Modeling
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Valentin De Bortoli
James Thornton
J. Heng
Arnaud Doucet
DiffM
OT
93
465
0
01 Jun 2021
Maximum Likelihood Training of Score-Based Diffusion Models
Maximum Likelihood Training of Score-Based Diffusion Models
Yang Song
Conor Durkan
Iain Murray
Stefano Ermon
DiffM
126
663
0
22 Jan 2021
Score-Based Generative Modeling through Stochastic Differential
  Equations
Score-Based Generative Modeling through Stochastic Differential Equations
Yang Song
Jascha Narain Sohl-Dickstein
Diederik P. Kingma
Abhishek Kumar
Stefano Ermon
Ben Poole
DiffM
SyDa
302
6,409
0
26 Nov 2020
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios
Eric T. Nalisnick
Danilo Jimenez Rezende
S. Mohamed
Balaji Lakshminarayanan
TPM
AI4CE
195
1,687
0
05 Dec 2019
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in
  the Diffusion Limit
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit
Belinda Tzen
Maxim Raginsky
DiffM
160
209
0
23 May 2019
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry
  Suffices
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices
Santosh Vempala
Andre Wibisono
79
264
0
20 Mar 2019
Theoretical guarantees for sampling and inference in generative models
  with latent diffusions
Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen
Maxim Raginsky
DiffM
59
101
0
05 Mar 2019
Variational Inference: A Review for Statisticians
Variational Inference: A Review for Statisticians
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
BDL
250
4,778
0
04 Jan 2016
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Narain Sohl-Dickstein
Eric A. Weiss
Niru Maheswaranathan
Surya Ganguli
SyDa
DiffM
265
6,887
0
12 Mar 2015
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
290
3,276
0
09 Jun 2012
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