CreINNs:用于分类任务中的不确定性估计的信誉集间隔神经网络
Kaizheng Wang1, Keivan Shariatmadar2, Shireen Kudukkil Manchingal3
1DistriNet, Department of Computer Science, Campus Bruges, KU Leuven, Bruges, 8200, Belgium; Flanders Make@KU Leuven, Leuven, Belgium.
概括
信誉区间神经网络 (CreINNs) 为神经网络提供可靠的不确定性估计. 这种新的方法预测了概率界限,提高了分类准确度,与现有方法相比减少了计算负载.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 可靠的不确定性估计对于提高神经网络可靠性至关重要.
- 传统间隔神经网络使用确定性间隔捕捉权重不确定性.
研究的目的:
- 在分类任务中引入信誉集间隔神经网络 (CreINNs).
- 开发一种用于预测概率区间的方法,以估计各种不确定性.
主要方法:
- 通过预测每个类的上下概率界限,CreINNs扩展了间隔神经网络.
- 这些概率区间定义了一个用于不确定性定量化的信誉集.
- 该方法在多类和二进制分类任务上进行了测试.
主要成果:
- 与变量贝叶斯神经网络 (BNNs) 和深层集群相比,CreINNs表现出优越或可比的不确定性估计质量.
- 与变量BNN相比,在推理过程中计算复杂性的显著降低.
- 即使使用间隔输入数据,也证实了有效的不确定性量化.
结论:
- 在神经网络分类中,CreINNs提供了一种有效的不确定性估计方法.
- 该方法为现有方法提供了一个计算效率高的替代方案.
- 对于需要强大的不确定性定量化应用,包括那些具有间隔数据的应用,CreINNs显示出前景.
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