论文标题
人工科学发现的重新归一化互信息
Renormalized Mutual Information for Artificial Scientific Discovery
论文作者
论文摘要
我们得出了一个定义明确的重新归一化的互信息，该版本允许在重要情况下估计连续随机变量之间的依赖性，而一个确定性依赖于另一个。这是与特征提取相关的情况，其目标是产生高维系统的低维有效描述。我们的方法可以在物理系统中发现集体变量，从而增加了人工科学发现的工具箱，同时还有助于人工神经网络中信息流的分析。
We derive a well-defined renormalized version of mutual information that allows to estimate the dependence between continuous random variables in the important case when one is deterministically dependent on the other. This is the situation relevant for feature extraction, where the goal is to produce a low-dimensional effective description of a high-dimensional system. Our approach enables the discovery of collective variables in physical systems, thus adding to the toolbox of artificial scientific discovery, while also aiding the analysis of information flow in artificial neural networks.
