Michael Y. Li
2 Papers
Michael Y. Li is an academic researcher. The author has contributed to research in topics: Computer science & Bayesian inference. The author has co-authored 2 publications.
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Papers
Gaussian process surrogate models for neural networks
TL;DR: A class of surrogate models for neural networks using Gaussian processes is constructed in which the kernels of the Gaussian process are learned empirically from the naturalistic behavior of neural networks.
Learning to Learn Functions
Michael Y. Li,Frederick Callaway,Ryan P. Adams,Thomas L. Griffiths +3 more
TL;DR: In this article , the process of learning to learn functions is modeled as a form of hierarchical Bayesian inference about the Gaussian process hyperparameters, and it is shown that people learn to adjust these expectations through experience, learning about the likely forms of the functions they will encounter.
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