Distributed Gaussian Processes With Uncertain Inputs
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DOI: 10.1109/access.2024.3501409
Abstract: Gaussian Process regression is a powerful non-parametric approach that facilitates probabilistic uncertainty quantification in machine learning. Distributed Gaussian Process (DGP) methods offer scalable solutions by dividing data among multiple GP models (or “experts”). DGPs have… read more here.
Keywords: processes uncertain; distributed gaussian; gaussian process; uncertain inputs ... See more keywords
A cerebellar adaptation to uncertain inputs
Sign Up to like & getrecommendations! Published in 2018 at "Science Advances"
DOI: 10.1126/sciadv.aap9660
Abstract: Under uncertain conditions, the cerebellum keeps responses adaptive by scaling the probability, but not the size of response. Noise and variability are inherent and unavoidable features of neural processing. Despite this physiological challenge, brain systems… read more here.
Keywords: binary choice; response; cerebellar adaptation; adaptation uncertain ... See more keywords