Khái niệm cốt lõi
Practical methodology using machine learning for information decomposition in complex systems.
Tóm tắt
The content discusses the application of machine learning to decompose information in complex systems. It introduces a practical methodology that uses the distributed information bottleneck to identify relevant variation in data. The analysis focuses on two paradigms: Boolean circuits and amorphous materials undergoing plastic deformation. The study aims to bridge micro- and macroscale structures in complex systems.
Directory:
- Introduction and Background
- Mutual information as a measure of statistical dependence.
- Importance of identifying relevant variation in complex systems.
- Methodology: Distributed Information Bottleneck
- Lossy compression of measurements using machine learning.
- Optimization process for extracting important variation.
- Results: Application to Boolean Circuits and Amorphous Materials
- Analysis of Boolean circuits with logistic regression, Shapley values, and distributed IB.
- Decomposing structural information in amorphous materials under deformation.
- Discussion: Comparison with Logistic Regression and Shapley Values
- Interpretability and insights provided by the distributed IB approach.
- Conclusion: Practical implications for studying complex systems.
Thống kê
Mutual information provides a natural means of linking variation across scales of a system.
The distributed information bottleneck method is used to decompose the information contained in measurements.
For every point on the spectrum, there is an allocation of information over the inputs.