The author explores the possibility of replacing cold posteriors with confidence-inducing priors in Bayesian neural networks to control aleatoric uncertainty effectively.
BNNs with Gaussian priors achieve optimal posterior concentration rates, filling a gap in theory and application.
후속 연구에서 사전을 통해 차가운 사후를 대체할 수 있는 방법을 탐구합니다.
Bayesian Neural Networks offer efficient storage and uncertainty handling in deep learning models.
Bayesian Neural Networks bieten effiziente Lösungen für Speicherplatzkomplexität und Unsicherheiten in Vorhersagen.
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.
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 신경망에서 평평한 손실 영역에 위치한 모델 파라미터를 찾는 것은 효과적인 베이지안 모델 평균(BMA)과 향상된 일반화 성능을 위해 중요하다.