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BeyondPlanck VIII: Efficient sidelobe convolution and corrections through spin harmonics
No full textA Systems Approach to Understanding How Plants Transformed Earth’s Environment in Deep Time
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Television and Afghan Culture Wars: Brought to You by Foreigners, Warlords, and Activists [book review]
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A Perspective on Machine Learning for Autonomous Experimentation
No full textThis chapter introduces machine learning (ML) in the context of autonomous scientific experimentation. It outlines the types of roles that ML can play in supporting autonomous experimentation, and in doing so provide a language for discussing the applications. The chapter discusses specific applications of ML for autonomous experimentation and their successes. The most general definition of ML is as a method for creating functions (i.e., input-output relationships) not by explicit programming but instead by showing example data. “Function” here is used both in its mathematical sense, as in a relation that uniquely associates members of one set with members of another set, and in its computer programming sense, a sequence of computer instructions that perform a task. As famously noted by Breimann, the practice and epistemic philosophy of ML differs from the model-based parameter estimation traditionally practiced in statistics and the sciences