Image de couverture de livre

Particle-based Energetic Variational Inference

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Wang, Yiwei, Chen, Jiuhai, Liu, Chun, Kang, Lulu
Format:	Preprint
Publié:	2020
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Accelerating Particle-based Energetic Variational Inference
par: Bao, Xuelian, et autres
Publié: (2025)

A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
par: Chen, Yindong, et autres
Publié: (2021)

Energetic Variational Gaussian Process Regression for Computer Experiments
par: Kang, Lulu, et autres
Publié: (2023)

Active Learning for Manifold Gaussian Process Regression
par: Cheng, Yuanxing, et autres
Publié: (2025)

Particle Semi-Implicit Variational Inference
par: Lim, Jen Ning, et autres
Publié: (2024)

Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference
par: Lou, Yujia, et autres
Publié: (2025)

Structure-Aware Variational Learning of a Class of Generalized Diffusions
par: Lu, Yubin, et autres
Publié: (2026)

Variational Inference via Smoothed Particle Hydrodynamics
par: Huang, Yongchao
Publié: (2024)

Censor Dependent Variational Inference
par: Liu, Chuanhui, et autres
Publié: (2025)

Stability-based Generalization Bounds for Variational Inference
par: Wei, Yadi, et autres
Publié: (2025)

Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements
par: Li, Ming, et autres
Publié: (2024)

Rotated Mean-Field Variational Inference and Iterative Gaussianization
par: Chen, Yifan, et autres
Publié: (2025)

Variational Formulation of Particle Flow
par: Yi, Yinzhuang, et autres
Publié: (2025)

Expected Free Energy-based Planning as Variational Inference
par: de Vries, Bert, et autres
Publié: (2025)

Enhancing Explainability in Solar Energetic Particle Event Prediction: A Global Feature Mapping Approach
par: Ji, Anli, et autres
Publié: (2025)

Robust Analysis for Resilient AI System
par: Wang, Yu, et autres
Publié: (2025)

Permutation-based Inference for Variational Learning of Directed Acyclic Graphs
par: Bonilla, Edwin V., et autres
Publié: (2024)

Gaussian Process Assisted Active Learning of Physical Laws
par: Chen, Jiuhai, et autres
Publié: (2019)

Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference
par: Lee, Jaewoo, et autres
Publié: (2026)

Global Variational Inference Enhanced Robust Domain Adaptation
par: Luo, Lingkun, et autres
Publié: (2025)

Energetic Variational Neural Network Discretizations of Gradient Flows
par: Hu, Ziqing, et autres
Publié: (2022)

Variational Rectification Inference for Learning with Noisy Labels
par: Sun, Haoliang, et autres
Publié: (2026)

Globally Convergent Variational Inference
par: McNamara, Declan, et autres
Publié: (2025)

Optimal Kernel Learning for Gaussian Process Models with High-Dimensional Input
par: Kang, Lulu, et autres
Publié: (2025)

Adaptive Exponential Integration for Stable Gaussian Mixture Black-Box Variational Inference
par: Che, Baojun, et autres
Publié: (2026)

Variational Inference, Entropy, and Orthogonality: A Unified Theory of Mixture-of-Experts
par: Su, Ye, et autres
Publié: (2026)

Variational Inference with Mixtures of Isotropic Gaussians
par: Petit-Talamon, Marguerite, et autres
Publié: (2025)

Amortized Variational Inference: When and Why?
par: Margossian, Charles C., et autres
Publié: (2023)

Deconfounding Imitation Learning with Variational Inference
par: Vuorio, Risto, et autres
Publié: (2022)

Learning with Importance Weighted Variational Inference
par: Daudel, Kamélia, et autres
Publié: (2024)

Kernel Semi-Implicit Variational Inference
par: Cheng, Ziheng, et autres
Publié: (2024)

Stepwise Variational Inference with Vine Copulas
par: Griesbauer, Elisabeth, et autres
Publié: (2026)

Variational Transdimensional Inference
par: Davies, Laurence, et autres
Publié: (2025)

Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors
par: Piriyakulkij, Wasu Top, et autres
Publié: (2024)

Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
par: Nguyen, Dai Hai, et autres
Publié: (2023)

Variational Inference with Coverage Guarantees in Simulation-Based Inference
par: Patel, Yash, et autres
Publié: (2023)

Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
par: Xu, Jian, et autres
Publié: (2023)

Large-scale Score-based Variational Posterior Inference for Bayesian Deep Neural Networks
par: Kim, Minyoung
Publié: (2026)

Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering
par: Shi, Yiwei, et autres
Publié: (2025)

Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields
par: Shi, Yiwei, et autres
Publié: (2025)