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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-05-29
摘要
Multivariate 資訊 theory provides a general and principled framework for understanding how the components of a system are connected. Existing analyses are coarse in nature-built up from characterizations of discrete subsystems-and can be 計算ly prohibitive. In this work, we propose to study the continuous space of possible descriptions of a composite system as a window into its organizational structure. A description consists of specific 資訊 conveyed about each of the components, and the space of possible descriptions is equivalent to the space of lossy compression schemes of the components. We introduce a machine-learning framework to optimize descriptions that extremize key 資訊 theoretic quantities used to characterize organization, such as total correlation and O-資訊. Through case studies on s
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