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密度回归和不确定性量化与贝叶斯深噪声神经网络.
Daiwei Zhang1, Tianci Liu2, Jian Kang3
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
概括
我们引入贝叶斯深噪声神经网络 (B-DeepNoise),以准确量化深度学习预测中的不确定性. 这种新的方法改善了密度估计和不确定性量化连续结果.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算统计学 计算统计学
背景情况:
- 深度神经网络 (DNN) 提供高预测准确度,但难以量化预测不确定性,特别是连续结果.
- 准确的不确定性量化对于各种应用中可靠的决策至关重要.
研究的目的:
- 提出一个新的贝叶斯深度神经网络,B-DeepNoise,有效量化预测中的不确定性.
- 通过将所有隐藏层的随机噪声变量纳入,扩展贝叶斯DNN.
主要方法:
- 开发了贝叶斯深噪声神经网络 (B-DeepNoise) 模型.
- 实现了封闭形式的吉布斯采样算法用于后置计算,避免了复杂的调整.
- 建立了预测密度的递归表示,并分析了预测方差.
主要成果:
- 与现有方法相比,B-DeepNoise在密度估计和不确定性量化方面表现优越.
- 该模型有效地近似复杂的预测密度函数,并学习结果的随机变化.
- 通过实验和神经成像应用验证了模型的实用性.
结论:
- B-DeepNoise为深度学习中的不确定性量化提供了一个强大的解决方案.
- 拟议的吉布斯采样方法简化了后置计算.
- 该模型对需要准确不确定性估计的科学研究具有显著的前景.
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