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层 wise 缩放的高斯先验对马尔科夫链蒙特卡洛采样深度贝叶斯神经网络
1School of Computer Science, Technological University Dublin, Dublin, Ireland.
Frontiers in artificial intelligence
|May 12, 2025
概括
层 wise 缩放的高斯优先级提高了马尔科夫链蒙特卡洛训练的贝叶斯神经网络的效率. 这种方法还可以在较小的网络中防止寒冷的后端效应,从而提高贝叶斯神经网络的性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算统计学 计算统计学
背景情况:
- 初始化对于训练神经网络和贝叶斯神经网络至关重要.
- 贝叶斯神经网络提供了诸如不确定性估计和过拟合预防等优势.
研究的目的:
- 为了评估在马尔科夫链中层wise缩放高斯式priors的性能,蒙特卡洛训练了贝叶斯神经网络.
- 为了比较层 wise 缩放的高斯前置与同位素前置,以提高效率和冷后置效应.
主要方法:
- 在不同复杂度的8个分类数据集上进行了实验.
- 马尔科夫链蒙特卡洛 (MCMC) 方法用于训练贝叶斯神经网络.
- 层 wise 缩放的高斯普里尔与同位方高斯普里尔,考奇和拉普拉斯普里尔进行了比较.
主要成果:
- 与同位素先验相比,分层缩放高斯先验证明了更有效的采样.
- 在小的前网络中,没有观察到与异构高斯或层wise Scaled Priors 的冷后面效应.
结论:
- 层 wise 缩放的高斯先验在MCMC学习的贝叶斯神经网络的效率上提供了显著的改善.
- 这种先前的选择减轻了寒冷的后后效应,使贝叶斯神经网络更可靠.
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