首页|Data from Department of Bioengineering Advance Knowledge in Machine Learning (In formation decomposition in complex systems via machine learning)
Data from Department of Bioengineering Advance Knowledge in Machine Learning (In formation decomposition in complex systems via machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news originating from the Departm ent of Bioengineering by NewsRx editors, the research stated, "One of the fundam ental steps toward understanding a complex system is identifying variation at th e scale of the system's components that is most relevant to behavior on a macros copic scale." Our news correspondents obtained a quote from the research from Department of Bi oengineering: "Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship bet ween observables. However, characterizing the manner in which information is dis tributed across a set of observables is computationally challenging and generall y infeasible beyond a handful of measurements. Here, we propose a practical and general methodology that uses machine learning to decompose the information cont ained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as a learning obj ective, the information decomposition identifies the variation in the measuremen ts of the system state most relevant to specified macroscale behavior. We focus our analysis on two paradigmatic complex systems: a Boolean circuit and an amorp hous material undergoing plastic deformation."
Department of BioengineeringCyborgsE merging TechnologiesMachine Learning.
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