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On learning higher-order cumulants in diffusion models
v1v2 (latest)

On learning higher-order cumulants in diffusion models

28 October 2024
Gert Aarts
Diaa E. Habibi
Lei Wang
K. Zhou
ArXiv (abs)PDFHTML

Papers citing "On learning higher-order cumulants in diffusion models"

40 / 40 papers shown
Title
Physics-Conditioned Diffusion Models for Lattice Gauge Theory
Qianteng Zhu
Gert Aarts
Wei Wang
K. Zhou
Lei Wang
159
1
0
08 Feb 2025
Diffusion models learn distributions generated by complex Langevin
  dynamics
Diffusion models learn distributions generated by complex Langevin dynamics
Diaa E. Habibi
Gert Aarts
Lei Wang
K. Zhou
DiffM
132
2
0
02 Dec 2024
Stochastic weight matrix dynamics during learning and Dyson Brownian
  motion
Stochastic weight matrix dynamics during learning and Dyson Brownian motion
Gert Aarts
B. Lucini
Chanju Park
83
1
0
23 Jul 2024
Improved Noise Schedule for Diffusion Training
Improved Noise Schedule for Diffusion Training
Tiankai Hang
Shuyang Gu
DiffM
91
18
0
03 Jul 2024
Neural network representation of quantum systems
Neural network representation of quantum systems
Koji Hashimoto
Yuji Hirono
Jun Maeda
Jojiro Totsuka-Yoshinaka
79
2
0
18 Mar 2024
Understanding Diffusion Models by Feynman's Path Integral
Understanding Diffusion Models by Feynman's Path Integral
Yuji Hirono
A. Tanaka
Kenji Fukushima
DiffM
80
6
0
17 Mar 2024
A Phase Transition in Diffusion Models Reveals the Hierarchical Nature
  of Data
A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data
Antonio Sclocchi
Alessandro Favero
Matthieu Wyart
DiffM
115
37
0
26 Feb 2024
Generative Diffusion Models for Lattice Field Theory
Generative Diffusion Models for Lattice Field Theory
Lei Wang
Gert Aarts
Kai Zhou
DiffM
91
10
0
06 Nov 2023
Diffusion Models as Stochastic Quantization in Lattice Field Theory
Diffusion Models as Stochastic Quantization in Lattice Field Theory
Lei Wang
Gert Aarts
Kai Zhou
DiffM
85
14
0
29 Sep 2023
Advances in machine-learning-based sampling motivated by lattice quantum
  chromodynamics
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Kyle Cranmer
G. Kanwar
S. Racanière
Danilo Jimenez Rezende
P. Shanahan
AI4CE
96
26
0
03 Sep 2023
Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
M. Demirtaş
James Halverson
Anindita Maiti
M. Schwartz
Keegan Stoner
AI4CE
75
10
0
06 Jul 2023
Sampling the lattice Nambu-Goto string using Continuous Normalizing
  Flows
Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
M. Caselle
E. Cellini
A. Nada
62
14
0
03 Jul 2023
Bayesian Renormalization
Bayesian Renormalization
D. Berman
Marc S. Klinger
A. G. Stapleton
96
17
0
17 May 2023
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of
  Lattice Field Theories
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
K. Nicoli
Christopher J. Anders
T. Hartung
K. Jansen
Pan Kessel
Shinichi Nakajima
94
25
0
27 Feb 2023
On the Importance of Noise Scheduling for Diffusion Models
On the Importance of Noise Scheduling for Diffusion Models
Ting Chen
DiffM
114
161
0
26 Jan 2023
The Inverse of Exact Renormalization Group Flows as Statistical
  Inference
The Inverse of Exact Renormalization Group Flows as Statistical Inference
D. Berman
Marc S. Klinger
87
16
0
21 Dec 2022
Learning Lattice Quantum Field Theories with Equivariant Continuous
  Flows
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
Mathis Gerdes
P. D. Haan
Corrado Rainone
Roberto Bondesan
Miranda C. N. Cheng
AI4CE
98
41
0
01 Jul 2022
Elucidating the Design Space of Diffusion-Based Generative Models
Elucidating the Design Space of Diffusion-Based Generative Models
Tero Karras
M. Aittala
Timo Aila
S. Laine
DiffM
289
2,036
0
01 Jun 2022
Hierarchical Text-Conditional Image Generation with CLIP Latents
Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya A. Ramesh
Prafulla Dhariwal
Alex Nichol
Casey Chu
Mark Chen
VLMDiffM
514
6,944
0
13 Apr 2022
A duality connecting neural network and cosmological dynamics
A duality connecting neural network and cosmological dynamics
Sven Krippendorf
M. Spannowsky
AI4CE
75
8
0
22 Feb 2022
Stochastic normalizing flows as non-equilibrium transformations
Stochastic normalizing flows as non-equilibrium transformations
M. Caselle
E. Cellini
A. Nada
M. Panero
97
35
0
21 Jan 2022
High-Resolution Image Synthesis with Latent Diffusion Models
High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach
A. Blattmann
Dominik Lorenz
Patrick Esser
Bjorn Ommer
3DV
615
15,855
0
20 Dec 2021
Scaling Up Machine Learning For Quantum Field Theory with Equivariant
  Continuous Flows
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
P. D. Haan
Corrado Rainone
Miranda C. N. Cheng
Roberto Bondesan
AI4CE
87
35
0
06 Oct 2021
Quantum field-theoretic machine learning
Quantum field-theoretic machine learning
Dimitrios Bachtis
Gert Aarts
B. Lucini
AI4CE
69
28
0
18 Feb 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
230
676
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
DiffMSyDa
562
6,606
0
26 Nov 2020
Neural Networks and Quantum Field Theory
Neural Networks and Quantum Field Theory
James Halverson
Anindita Maiti
Keegan Stoner
113
78
0
19 Aug 2020
Denoising Diffusion Probabilistic Models
Denoising Diffusion Probabilistic Models
Jonathan Ho
Ajay Jain
Pieter Abbeel
DiffM
1.0K
18,532
0
19 Jun 2020
Fourier Features Let Networks Learn High Frequency Functions in Low
  Dimensional Domains
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik
Pratul P. Srinivasan
B. Mildenhall
Sara Fridovich-Keil
N. Raghavan
Utkarsh Singhal
R. Ramamoorthi
Jonathan T. Barron
Ren Ng
135
2,458
0
18 Jun 2020
Improved Techniques for Training Score-Based Generative Models
Improved Techniques for Training Score-Based Generative Models
Yang Song
Stefano Ermon
DiffM
284
1,170
0
16 Jun 2020
Equivariant flow-based sampling for lattice gauge theory
Equivariant flow-based sampling for lattice gauge theory
G. Kanwar
M. S. Albergo
D. Boyda
Kyle Cranmer
D. Hackett
S. Racanière
Danilo Jimenez Rezende
P. Shanahan
AI4CE
75
176
0
13 Mar 2020
Stochastic Normalizing Flows
Stochastic Normalizing Flows
Hao Wu
Jonas Köhler
Frank Noé
156
186
0
16 Feb 2020
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any
  Architecture are Gaussian Processes
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Greg Yang
181
202
0
28 Oct 2019
Generative Modeling by Estimating Gradients of the Data Distribution
Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song
Stefano Ermon
SyDaDiffM
273
3,972
0
12 Jul 2019
Flow-based generative models for Markov chain Monte Carlo in lattice
  field theory
Flow-based generative models for Markov chain Monte Carlo in lattice field theory
M. S. Albergo
G. Kanwar
P. Shanahan
AI4CE
120
220
0
26 Apr 2019
Neural Ordinary Differential Equations
Neural Ordinary Differential Equations
T. Chen
Yulia Rubanova
J. Bettencourt
David Duvenaud
AI4CE
526
5,187
0
19 Jun 2018
Gaussian Process Behaviour in Wide Deep Neural Networks
Gaussian Process Behaviour in Wide Deep Neural Networks
A. G. Matthews
Mark Rowland
Jiri Hron
Richard Turner
Zoubin Ghahramani
BDL
210
561
0
30 Apr 2018
Deep Neural Networks as Gaussian Processes
Deep Neural Networks as Gaussian Processes
Jaehoon Lee
Yasaman Bahri
Roman Novak
S. Schoenholz
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCVBDL
162
1,100
0
01 Nov 2017
Variational Inference with Normalizing Flows
Variational Inference with Normalizing Flows
Danilo Jimenez Rezende
S. Mohamed
DRLBDL
554
4,208
0
21 May 2015
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Narain Sohl-Dickstein
Eric A. Weiss
Niru Maheswaranathan
Surya Ganguli
SyDaDiffM
365
7,054
0
12 Mar 2015
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