Cover Image

Energy based diffusion generator for efficient sampling of Boltzmann distributions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Yan, Guo, Ling, Wu, Hao, Zhou, Tao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Controllable diffusion-based generation for multi-channel biological data
by: Zhang, Haoran, et al.
Published: (2025)

Deep set based operator learning with uncertainty quantification
by: Ma, Lei, et al.
Published: (2025)

Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations
by: Zhou, Shaoqian, et al.
Published: (2025)

Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
by: Tran, Hoang, et al.
Published: (2025)

Estimating Solvation Free Energies with Boltzmann Generators
by: Schebek, Maximilian, et al.
Published: (2025)

Evaluating the design space of diffusion-based generative models
by: Wang, Yuqing, et al.
Published: (2024)

DFU: scale-robust diffusion model for zero-shot super-resolution image generation
by: Havrilla, Alex, et al.
Published: (2023)

BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching
by: OuYang, RuiKang, et al.
Published: (2024)

Equivariant score-based generative models provably learn distributions with symmetries efficiently
by: Chen, Ziyu, et al.
Published: (2024)

TAUDiff: Highly efficient kilometer-scale downscaling using generative diffusion models
by: Sundar, Rahul, et al.
Published: (2024)

Re-evaluating sample efficiency in de novo molecule generation
by: Thomas, Morgan, et al.
Published: (2022)

Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models
by: Sauvalle, Bruno, et al.
Published: (2024)

Learning distributed representations with efficient SoftMax normalization
by: Dall'Amico, Lorenzo, et al.
Published: (2023)

Bayesian computation with generative diffusion models by Multilevel Monte Carlo
by: Haji-Ali, Abdul-Lateef, et al.
Published: (2024)

ROAST: Rollout-based On-distribution Activation Steering Technique
by: Su, Xuanbo, et al.
Published: (2026)

Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities
by: Woo, Dongyeop, et al.
Published: (2024)

Spherical Boltzmann machines: a solvable theory of learning and generation in energy-based models
by: Tulinski, Thomas, et al.
Published: (2026)

Conditional sampling within generative diffusion models
by: Zhao, Zheng, et al.
Published: (2024)

The day-ahead scenario generation method for new energy based on an improved conditional generative diffusion model
by: Wang, Changgang, et al.
Published: (2025)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
by: Akhound-Sadegh, Tara, et al.
Published: (2024)

Residual Attention Physics-Informed Neural Networks for Robust Multiphysics Simulation of Steady-State Electrothermal Energy Systems
by: Zhou, Yuqing, et al.
Published: (2026)

Flow-based Bayesian filtering for high-dimensional nonlinear stochastic dynamical systems
by: Wang, Xintong, et al.
Published: (2025)

Transferable Boltzmann Generators
by: Klein, Leon, et al.
Published: (2024)

Theoretical research on generative diffusion models: an overview
by: Yeğin, Melike Nur, et al.
Published: (2024)

A phase transition in sampling from Restricted Boltzmann Machines
by: Kwon, Youngwoo, et al.
Published: (2024)

Formalized Hopfield Networks and Boltzmann Machines
by: Cipollina, Matteo, et al.
Published: (2025)

LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process
by: Feng, Xiaodong, et al.
Published: (2025)

Efficient Prompting for Continual Adaptation to Missing Modalities
by: Guo, Zirun, et al.
Published: (2025)

Configuration Interaction Guided Sampling with Interpretable Restricted Boltzmann Machine
by: Hernandez-Martinez, Jorge I., et al.
Published: (2024)

Physics Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation
by: Varagnolo, Antonio, et al.
Published: (2025)

Boltzmann Classifier: A Thermodynamic-Inspired Approach to Supervised Learning
by: Amin, Muhamed, et al.
Published: (2025)

Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection
by: Zeng, Cong, et al.
Published: (2025)

On scalable and efficient training of diffusion samplers
by: Kim, Minkyu, et al.
Published: (2025)

Joint Memory Frequency and Computing Frequency Scaling for Energy-efficient DNN Inference
by: Han, Yunchu, et al.
Published: (2025)

An optimal control perspective on diffusion-based generative modeling
by: Berner, Julius, et al.
Published: (2022)

Posterior sampling via Langevin dynamics based on generative priors
by: Purohit, Vishal, et al.
Published: (2024)

The Deep Learning model of Higher-Lower-Order Cognition, Memory, and Affection- More General Than KAN
by: Tao, Jun-Bo, et al.
Published: (2022)

Bridging the Gap for Test-Time Multimodal Sentiment Analysis
by: Guo, Zirun, et al.
Published: (2024)

Adaptive operator learning for infinite-dimensional Bayesian inverse problems
by: Gao, Zhiwei, et al.
Published: (2023)

An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation
by: Xiong, Yifei, et al.
Published: (2023)