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.
Sharpness-Aware Bayesian Model Averaging for Improved Generalization in Neural Networks
Bayesian neural networks can achieve better generalization performance by explicitly seeking flat posteriors during optimization, leading to more effective Bayesian Model Averaging.
ベイズモデル平均化における平坦な事後分布の重要性
ベイズニューラルネットワーク（BNN）の予測精度と頑健性を向上させるには、損失関数の平坦な領域に対応する、平坦な事後分布を獲得することが重要である。
베이지안 모델 평균에서 평평한 사후 분포의 중요성: 평평하지 않으면 효과 없다
베이지안 신경망에서 평평한 손실 영역에 위치한 모델 파라미터를 찾는 것은 효과적인 베이지안 모델 평균(BMA)과 향상된 일반화 성능을 위해 중요하다.
