Cover Image

Direct Bethe Free Energy Minimization for Bayesian Neural Network

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Prochazka, Pavel
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization
by: Beckers, Jim, et al.
Published: (2022)

On the Convexity and Reliability of the Bethe Free Energy Approximation
by: Leisenberger, Harald, et al.
Published: (2024)

SGD as Free Energy Minimization: A Thermodynamic View on Neural Network Training
by: Sadrtdinov, Ildus, et al.
Published: (2025)

Convolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs
by: Procházka, Pavel, et al.
Published: (2024)

Minima and Critical Points of the Bethe Free Energy Are Invariant Under Deformation Retractions of Factor Graphs
by: Sergeant-Perthuis, Grégoire, et al.
Published: (2025)

Adaptive Variational Inference in Probabilistic Graphical Models: Beyond Bethe, Tree-Reweighted, and Convex Free Energies
by: Leisenberger, Harald, et al.
Published: (2025)

Free Energy Manifold: Score-Based Inference for Hybrid Bayesian Networks
by: Park, Cheol Young, et al.
Published: (2026)

Entropic Auto-Encoding via Implicit Free-Energy Minimization
by: Aliahmadi, Hazhir, et al.
Published: (2026)

Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
by: Smerkous, David, et al.
Published: (2024)

Spatial Bayesian Neural Networks
by: Zammit-Mangion, Andrew, et al.
Published: (2023)

Singular Bayesian Neural Networks
by: Toure, Mame Diarra, et al.
Published: (2026)

Bayesian Neural Networks: An Introduction and Survey
by: Goan, Ethan, et al.
Published: (2020)

Data Subsampling for Bayesian Neural Networks
by: Kawasaki, Eiji, et al.
Published: (2022)

A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
by: Li, Yucen Lily, et al.
Published: (2023)

FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting
by: Bahi, Abderaouf, et al.
Published: (2026)

Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds
by: Hennequin, Mehdi, et al.
Published: (2024)

Higher-Order Topological Directionality and Directed Simplicial Neural Networks
by: Lecha, Manuel, et al.
Published: (2024)

Bayesian Neighborhood Adaptation for Graph Neural Networks
by: Regmi, Paribesh, et al.
Published: (2026)

Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
by: Mitra, Pallavi, et al.
Published: (2024)

Precise Bayesian Neural Networks
by: Brito, Carlos Stein
Published: (2025)

Risk-Averse Certification of Bayesian Neural Networks
by: Zhang, Xiyue, et al.
Published: (2024)

Bayesian Neural Networks for Functional ANOVA model
by: Park, Seokhun, et al.
Published: (2025)

Structured Partial Stochasticity in Bayesian Neural Networks
by: Rochussen, Tommy
Published: (2024)

Bayesian Neural Networks with Domain Knowledge Priors
by: Sam, Dylan, et al.
Published: (2024)

Improved Depth Estimation of Bayesian Neural Networks
by: van Erp, Bart, et al.
Published: (2024)

Bayesian Neural Networks for Macroeconomic Analysis
by: Hauzenberger, Niko, et al.
Published: (2022)

Rapid Neural Network Prediction of Linear Block Copolymer Free Energies
by: Chen, Ian, et al.
Published: (2026)

Bayesian Regret Minimization in Offline Bandits
by: Petrik, Marek, et al.
Published: (2023)

Proof Minimization in Neural Network Verification
by: Isac, Omri, et al.
Published: (2025)

Bayesian Neural Network Surrogates for Bayesian Optimization of Carbon Capture and Storage Operations
by: Fotias, Sofianos Panagiotis, et al.
Published: (2025)

Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
by: Makrygiorgos, Georgios, et al.
Published: (2025)

Directed Homophily-Aware Graph Neural Network
by: Zhang, Aihu, et al.
Published: (2025)

On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
by: Kobialka, Julius, et al.
Published: (2026)

Functional Stochastic Gradient MCMC for Bayesian Neural Networks
by: Wu, Mengjing, et al.
Published: (2024)

The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters
by: Koermer, Scott, et al.
Published: (2025)

Training Feedforward Neural Networks with Bayesian Hyper-Heuristics
by: Schreuder, Arné, et al.
Published: (2023)

Graph Structure Learning with Interpretable Bayesian Neural Networks
by: Wasserman, Max, et al.
Published: (2024)

The Epistemic Uncertainty Hole: an issue of Bayesian Neural Networks
by: Fellaji, Mohammed, et al.
Published: (2024)

Function-Space MCMC for Bayesian Wide Neural Networks
by: Pezzetti, Lucia, et al.
Published: (2024)

Bayesian Sheaf Neural Networks
by: Gillespie, Patrick, et al.
Published: (2024)