神经网络中的分析不确定性传播
IEEE transactions on neural networks and learning systems
|January 12, 2024
概括
本研究介绍了一种分析方法,用于在神经网络中传播不确定性,这对于可靠的机器学习预测至关重要. 该技术在不改变培训的情况下提供可证明正确的误差条,有利于半导体制造等领域的决策.
科学领域:
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
- 物理 物理学 物理
背景情况:
- 机器学习,特别是神经网络,在各个领域广泛应用.
- 估计预测确定性对于高精度的决策至关重要,但神经网络中的不确定性传播仍然不明朗.
- 测量不确定性,通常表达为误差条,对于要求精度的应用是必不可少的.
研究的目的:
- 开发一种用于神经网络中 aleatoric 不确定性向前传播的分析方法.
- 提供一种计算价格低廉的技术,利用现有的神经网络权重和偏差.
- 确保可证明正确的不确定性估计,而不需要修改培训程序.
主要方法:
- 该研究从物理学和工程学中适应了已建立的分析不确定性传播原则.
- 该方法旨在在训练后应用,仅使用神经网络的权重和偏差.
- 实施这种不确定性量化方法不需要专门的培训制度.
主要成果:
- 开发的分析方法提供了一种方法来量化神经网络预测中的随机不确定性.
- 该方法已成功应用于真实世界的半导体行业问题,包括回归和图像分类任务.
- 证明了产生有意义的错误条,提高了机器学习在决策环境中的可靠性.
结论:
- 分析方法为神经网络中不确定性传播提供了强大而有效的解决方案.
- 这种技术通过量化预测确定性来提高机器学习模型的可靠性.
- 这些发现特别适用于诸如半导体制造业等行业的高风险应用,其中精确的决策至关重要.
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