Cover Image

A Score-Based Density Formula, with Applications in Diffusion Generative Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Gen, Yan, Yuling
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Probability Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models
by: Li, Gen, et al.
Published: (2024)

O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions
by: Li, Gen, et al.
Published: (2024)

Towards Efficient Online Exploration for Reinforcement Learning with Human Feedback
by: Li, Gen, et al.
Published: (2025)

Tail-Aware Information-Theoretic Generalization for RLHF and SGLD
by: Zhang, Huiming, et al.
Published: (2026)

Diffusion Models with Heavy-Tailed Targets: Score Estimation and Sampling Guarantees
by: Yu, Yifeng, et al.
Published: (2026)

Scaling Limits of Long-Context Transformers
by: Bruno, Giuseppe, et al.
Published: (2026)

Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2026)

Provable Diffusion Posterior Sampling for Bayesian Inversion
by: Chang, Jinyuan, et al.
Published: (2025)

Huber-energy measure quantization
by: Turinici, Gabriel
Published: (2022)

Variational Optimality of Föllmer Processes in Generative Diffusions
by: Chen, Yifan, et al.
Published: (2026)

Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities
by: Stéphanovitch, Arthur
Published: (2026)

U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models
by: Mei, Song
Published: (2024)

Learning with Expected Signatures: Theory and Applications
by: Lucchese, Lorenzo, et al.
Published: (2025)

Bayesian Inference with Deep Weakly Nonlinear Networks
by: Hanin, Boris, et al.
Published: (2024)

Characteristic Learning for Provable One Step Generation
by: Ding, Zhao, et al.
Published: (2024)

L$^2$M: Mutual Information Scaling Law for Long-Context Language Modeling
by: Chen, Zhuo, et al.
Published: (2025)

Sampling from Constrained Gibbs Measures: with Applications to High-Dimensional Bayesian Inference
by: Wang, Ruixiao, et al.
Published: (2026)

A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification
by: Corradini, Flavio, et al.
Published: (2024)

Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games
by: Yan, Yuling, et al.
Published: (2022)

Neural Thermodynamic Laws for Large Language Model Training
by: Liu, Ziming, et al.
Published: (2025)

When Attention Collapses: Residual Evidence Modeling for Compositional Inference
by: Houba, Niklas
Published: (2026)

Provable Efficiency of Guidance in Diffusion Models for General Data Distribution
by: Li, Gen, et al.
Published: (2025)

A Computational Transition for Detecting Multivariate Shuffled Linear Regression by Low-Degree Polynomials
by: Li, Zhangsong
Published: (2025)

Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing
by: Zhang, Yihan, et al.
Published: (2023)

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model
by: Wortsman-Zurich, Arie, et al.
Published: (2026)

Complexity of Markov Chain Monte Carlo for Generalized Linear Models
by: Chak, Martin, et al.
Published: (2025)

DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design
by: Yu, Xinling, et al.
Published: (2025)

Navigating the Exploration-Exploitation Tradeoff in Inference-Time Scaling of Diffusion Models
by: Su, Xun, et al.
Published: (2025)

Diffusion Models and the Manifold Hypothesis: Log-Domain Smoothing is Geometry Adaptive
by: Farghly, Tyler, et al.
Published: (2025)

Dimension-Free Bounds for Generalized First-Order Methods via Gaussian Coupling
by: Reeves, Galen
Published: (2025)

PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research
by: Miao, Tingjia, et al.
Published: (2026)

Dynamic Structural Causal Models
by: Boeken, Philip, et al.
Published: (2024)

Boosting Causal Additive Models
by: Kertel, Maximilian, et al.
Published: (2024)

Optimal Estimation in Orthogonally Invariant Generalized Linear Models: Spectral Initialization and Approximate Message Passing
by: Zhang, Yihan, et al.
Published: (2026)

Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil
by: Michelucci, Umberto, et al.
Published: (2024)

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
by: Li, Gen, et al.
Published: (2023)

A Neural Scaling Law from Lottery Ticket Ensembling
by: Liu, Ziming, et al.
Published: (2023)

A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
by: Kumar, Shivam, et al.
Published: (2024)

High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models
by: Liang, Tengyuan, et al.
Published: (2022)

Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
by: Zucker, Shai, et al.
Published: (2025)