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On learning higher-order cumulants in diffusion models

28 October 2024

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