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On learning higher-order cumulants in diffusion models
28 October 2024
Gert Aarts
Diaa E. Habibi
Lei Wang
K. Zhou
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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
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
Gert Aarts
B. Lucini
Chanju Park
83
1
0
23 Jul 2024
Improved Noise Schedule for Diffusion Training
Tiankai Hang
Shuyang Gu
DiffM
89
18
0
03 Jul 2024
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
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
Antonio Sclocchi
Alessandro Favero
Matthieu Wyart
DiffM
115
37
0
26 Feb 2024
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
Lei Wang
Gert Aarts
Kai Zhou
DiffM
85
14
0
29 Sep 2023
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
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
M. Caselle
E. Cellini
A. Nada
62
14
0
03 Jul 2023
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
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
Ting Chen
DiffM
114
161
0
26 Jan 2023
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
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
Tero Karras
M. Aittala
Timo Aila
S. Laine
DiffM
286
2,036
0
01 Jun 2022
Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya A. Ramesh
Prafulla Dhariwal
Alex Nichol
Casey Chu
Mark Chen
VLM
DiffM
514
6,944
0
13 Apr 2022
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
M. Caselle
E. Cellini
A. Nada
M. Panero
97
35
0
21 Jan 2022
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
P. D. Haan
Corrado Rainone
Miranda C. N. Cheng
Roberto Bondesan
AI4CE
87
35
0
06 Oct 2021
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
Yang Song
Conor Durkan
Iain Murray
Stefano Ermon
DiffM
230
676
0
22 Jan 2021
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
554
6,606
0
26 Nov 2020
Neural Networks and Quantum Field Theory
James Halverson
Anindita Maiti
Keegan Stoner
113
78
0
19 Aug 2020
Denoising Diffusion Probabilistic Models
Jonathan Ho
Ajay Jain
Pieter Abbeel
DiffM
1.0K
18,531
0
19 Jun 2020
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
Yang Song
Stefano Ermon
DiffM
284
1,170
0
16 Jun 2020
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
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
Greg Yang
172
202
0
28 Oct 2019
Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song
Stefano Ermon
SyDa
DiffM
273
3,972
0
12 Jul 2019
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
T. Chen
Yulia Rubanova
J. Bettencourt
David Duvenaud
AI4CE
526
5,187
0
19 Jun 2018
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
Jaehoon Lee
Yasaman Bahri
Roman Novak
S. Schoenholz
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCV
BDL
162
1,100
0
01 Nov 2017
Variational Inference with Normalizing Flows
Danilo Jimenez Rezende
S. Mohamed
DRL
BDL
534
4,208
0
21 May 2015
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Narain Sohl-Dickstein
Eric A. Weiss
Niru Maheswaranathan
Surya Ganguli
SyDa
DiffM
365
7,054
0
12 Mar 2015
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