论文标题
模型解释的功能信息观点
A Functional Information Perspective on Model Interpretation
论文作者
论文摘要
当代预测模型很难解释，因为他们的深网利用了输入要素之间的许多复杂关系。这项工作通过测量相关特征对网络功能性熵的贡献，提出了模型可解释性的理论框架。我们依靠对数 - 核心不等式，该不等式通过功能性渔民信息在数据的协方差方面界定功能熵。这提供了一种衡量特征子集对决策功能的信息贡献的原则方法。通过广泛的实验，我们表明我们的方法超过了基于图像，文本和音频等各种数据信号的现有基于基于可解释性采样的方法。
Contemporary predictive models are hard to interpret as their deep nets exploit numerous complex relations between input elements. This work suggests a theoretical framework for model interpretability by measuring the contribution of relevant features to the functional entropy of the network with respect to the input. We rely on the log-Sobolev inequality that bounds the functional entropy by the functional Fisher information with respect to the covariance of the data. This provides a principled way to measure the amount of information contribution of a subset of features to the decision function. Through extensive experiments, we show that our method surpasses existing interpretability sampling-based methods on various data signals such as image, text, and audio.
