人工科学发现中的重正化互信息
Renormalized Mutual Information for Artificial Scientific Discovery
摘要：
我们推导出一个定义良好的重正化版本的互信息，允许估计连续随机变量之间的依赖关系的重要情况下，当一个是确定性依赖于其他。这是与特征提取相关的情况，其中目标是产生高维系统的低维有效描述。我们的方法能够发现物理系统中的集体变量，从而增加了人工科学发现的工具箱，同时也有助于分析人工神经网络中的信息流。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
