Normalizing flows trained on five population synthesis models interpolate between simulation inputs and, applied to gravitational wave data, favor low spins, high common-envelope efficiency, and a dominant common-envelope channel.
Bayesian Optimization for Machine Learning : A Practical Guidebook
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abstract
The engineering of machine learning systems is still a nascent field; relying on a seemingly daunting collection of quickly evolving tools and best practices. It is our hope that this guidebook will serve as a useful resource for machine learning practitioners looking to take advantage of Bayesian optimization techniques. We outline four example machine learning problems that can be solved using open source machine learning libraries, and highlight the benefits of using Bayesian optimization in the context of these common machine learning applications.
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Exploring the astrophysical origins of binary black holes using normalising flows
Normalizing flows trained on five population synthesis models interpolate between simulation inputs and, applied to gravitational wave data, favor low spins, high common-envelope efficiency, and a dominant common-envelope channel.