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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"
42 / 42 papers shown
Scaling flow-based approaches for topology sampling in
S
U
(
3
)
\mathrm{SU}(3)
SU
(
3
)
gauge theory
Claudio Bonanno
Andrea Bulgarelli
E. Cellini
A. Nada
Dario Panfalone
Davide Vadacchino
Lorenzo Verzichelli
130
1
0
29 Oct 2025
Combining complex Langevin dynamics with score-based and energy-based diffusion models
Gert Aarts
Diaa E. Habibi
Lingxiao Wang
K. Zhou
DiffM
151
1
0
01 Oct 2025
Physics-Conditioned Diffusion Models for Lattice Gauge Theory
Qianteng Zhu
Gert Aarts
Wei Wang
K. Zhou
Lei Wang
377
5
0
08 Feb 2025
Diffusion models learn distributions generated by complex Langevin dynamics
Diaa E. Habibi
Gert Aarts
Lei Wang
K. Zhou
DiffM
225
3
0
02 Dec 2024
Stochastic weight matrix dynamics during learning and Dyson Brownian motion
Gert Aarts
B. Lucini
Chanju Park
175
3
0
23 Jul 2024
Improved Noise Schedule for Diffusion Training
Tiankai Hang
Shuyang Gu
DiffM
316
31
0
03 Jul 2024
Neural network representation of quantum systems
Koji Hashimoto
Yuji Hirono
Jun Maeda
Jojiro Totsuka-Yoshinaka
199
4
0
18 Mar 2024
Understanding Diffusion Models by Feynman's Path Integral
Yuji Hirono
A. Tanaka
Kenji Fukushima
DiffM
161
9
0
17 Mar 2024
A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data
Antonio Sclocchi
Alessandro Favero
Matthieu Wyart
DiffM
263
56
0
26 Feb 2024
Generative Diffusion Models for Lattice Field Theory
Lei Wang
Gert Aarts
Kai Zhou
DiffM
238
13
0
06 Nov 2023
Diffusion Models as Stochastic Quantization in Lattice Field Theory
Journal of High Energy Physics (JHEP), 2023
Lei Wang
Gert Aarts
Kai Zhou
DiffM
239
27
0
29 Sep 2023
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Nature Reviews Physics (Nat. Rev. Phys.), 2023
Kyle Cranmer
G. Kanwar
S. Racanière
Danilo Jimenez Rezende
P. Shanahan
AI4CE
245
32
0
03 Sep 2023
Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
M. Demirtaş
James Halverson
Anindita Maiti
M. Schwartz
Keegan Stoner
AI4CE
221
20
0
06 Jul 2023
Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
M. Caselle
E. Cellini
A. Nada
223
19
0
03 Jul 2023
Bayesian Renormalization
D. Berman
Marc S. Klinger
A. G. Stapleton
343
23
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
184
29
0
27 Feb 2023
On the Importance of Noise Scheduling for Diffusion Models
Ting Chen
DiffM
450
197
0
26 Jan 2023
The Inverse of Exact Renormalization Group Flows as Statistical Inference
D. Berman
Marc S. Klinger
211
20
0
21 Dec 2022
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
SciPost Physics (SciPost Phys.), 2022
Mathis Gerdes
P. D. Haan
Corrado Rainone
Roberto Bondesan
Miranda C. N. Cheng
AI4CE
376
52
0
01 Jul 2022
Elucidating the Design Space of Diffusion-Based Generative Models
Neural Information Processing Systems (NeurIPS), 2022
Tero Karras
M. Aittala
Timo Aila
S. Laine
DiffM
908
2,746
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01 Jun 2022
Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya A. Ramesh
Prafulla Dhariwal
Alex Nichol
Casey Chu
Mark Chen
VLM
DiffM
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8,283
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13 Apr 2022
A duality connecting neural network and cosmological dynamics
Sven Krippendorf
M. Spannowsky
AI4CE
161
11
0
22 Feb 2022
Stochastic normalizing flows as non-equilibrium transformations
Journal of High Energy Physics (JHEP), 2022
M. Caselle
E. Cellini
A. Nada
M. Panero
237
38
0
21 Jan 2022
High-Resolution Image Synthesis with Latent Diffusion Models
Computer Vision and Pattern Recognition (CVPR), 2021
Robin Rombach
A. Blattmann
Dominik Lorenz
Patrick Esser
Bjorn Ommer
DiffM
2.8K
20,975
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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
192
37
0
06 Oct 2021
Quantum field-theoretic machine learning
Dimitrios Bachtis
Gert Aarts
B. Lucini
AI4CE
177
33
0
18 Feb 2021
Maximum Likelihood Training of Score-Based Diffusion Models
Neural Information Processing Systems (NeurIPS), 2021
Yang Song
Conor Durkan
Iain Murray
Stefano Ermon
DiffM
755
799
0
22 Jan 2021
Score-Based Generative Modeling through Stochastic Differential Equations
International Conference on Learning Representations (ICLR), 2020
Yang Song
Jascha Narain Sohl-Dickstein
Diederik P. Kingma
Abhishek Kumar
Stefano Ermon
Ben Poole
DiffM
SyDa
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8,832
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26 Nov 2020
Neural Networks and Quantum Field Theory
James Halverson
Anindita Maiti
Keegan Stoner
326
91
0
19 Aug 2020
Denoising Diffusion Probabilistic Models
Jonathan Ho
Ajay Jain
Pieter Abbeel
DiffM
5.0K
25,697
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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
577
3,011
0
18 Jun 2020
Improved Techniques for Training Score-Based Generative Models
Yang Song
Stefano Ermon
DiffM
586
1,359
0
16 Jun 2020
Equivariant flow-based sampling for lattice gauge theory
Physical Review Letters (PRL), 2020
G. Kanwar
M. S. Albergo
D. Boyda
Kyle Cranmer
D. Hackett
S. Racanière
Danilo Jimenez Rezende
P. Shanahan
AI4CE
173
196
0
13 Mar 2020
Stochastic Normalizing Flows
Neural Information Processing Systems (NeurIPS), 2020
Hao Wu
Jonas Köhler
Frank Noé
508
203
0
16 Feb 2020
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Neural Information Processing Systems (NeurIPS), 2019
Greg Yang
474
220
0
28 Oct 2019
Generative Modeling by Estimating Gradients of the Data Distribution
Neural Information Processing Systems (NeurIPS), 2019
Yang Song
Stefano Ermon
SyDa
DiffM
797
4,853
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
320
247
0
26 Apr 2019
Neural Ordinary Differential Equations
T. Chen
Yulia Rubanova
J. Bettencourt
David Duvenaud
AI4CE
1.2K
6,190
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19 Jun 2018
Gaussian Process Behaviour in Wide Deep Neural Networks
A. G. Matthews
Mark Rowland
Jiri Hron
Richard Turner
Zoubin Ghahramani
BDL
417
595
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
660
1,182
0
01 Nov 2017
Variational Inference with Normalizing Flows
International Conference on Machine Learning (ICML), 2015
Danilo Jimenez Rezende
S. Mohamed
DRL
BDL
1.2K
4,634
0
21 May 2015
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Narain Sohl-Dickstein
Eric A. Weiss
Niru Maheswaranathan
Surya Ganguli
SyDa
DiffM
1.5K
8,825
0
12 Mar 2015
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