Bayesian Neural Networks

insight - Bayesian Neural Networks

Can a Confident Prior Replace a Cold Posterior in Bayesian Neural Networks?

The author explores the possibility of replacing cold posteriors with confidence-inducing priors in Bayesian neural networks to control aleatoric uncertainty effectively.

Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on Weights

BNNs with Gaussian priors achieve optimal posterior concentration rates, filling a gap in theory and application.

신뢰할 수 있는 사전이 차가운 사후를 대체할 수 있을까?

후속 연구에서 사전을 통해 차가운 사후를 대체할 수 있는 방법을 탐구합니다.

Restricted Bayesian Neural Network: Efficient Storage and Uncertainty Handling

Bayesian Neural Networks offer efficient storage and uncertainty handling in deep learning models.

Effiziente Restricted Bayesian Neural Network Analyse

Bayesian Neural Networks bieten effiziente Lösungen für Speicherplatzkomplexität und Unsicherheiten in Vorhersagen.

Bayesian Neural Networks via Markov Chain Monte Carlo: A Python-Based Tutorial

This tutorial presents a comprehensive approach to implementing Bayesian neural networks using Markov Chain Monte Carlo (MCMC) sampling methods. It covers the theoretical foundations of Bayesian inference and MCMC, and provides detailed Python-based implementations for Bayesian linear models and Bayesian neural networks.

About

Products | Resources

Insights