对神经网络中的不确定性表示和量化进行了审查
IEEE transactions on pattern analysis and machine intelligence
|October 28, 2025
概括
估计神经网络中的不确定性对于可靠的AI至关重要. 本综述探讨了量化 aleatoric 和 epistemic 不确定性的方法,提高了模型可信度.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 不确定性定量化 不确定性定量化
背景情况:
- 神经网络需要强大的不确定性估计来进行可靠的预测.
- 区分 aleatoric (数据) 和 epistemic (模型) 不确定性是关键.
- 目前深度学习中的不确定性量化方法多样化.
研究的目的:
- 为神经网络中不确定性表示和量化方法提供全面的概述.
- 在神经网络模型中区分 aleatoric 和 epistemic 的不确定性.
- 分析各种不确定性估计技术的优点和局限性.
主要方法:
- 经典概率技术的审查:贝叶斯神经网络和深层合奏.
- 探讨概率概括方法:迪里克莱特分布,信念函数,随机集,概率间隔和信誉集.
- 使用间隔模型检查基于间隔的方法.
主要成果:
- 各种不确定性量化方法的分类.
- 分析每个方法的适用性和限制.
- 确定该领域的研究缺口和未来方向.
结论:
- 有效的不确定性估计对于可信的人工智能系统至关重要.
- 有各种各样的方法存在,每个都有独特的优点和缺点.
- 需要进一步的研究来推进神经网络中稳健的不确定性量化.
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