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Cambridge Machine Learning Group Publications
https://mlg.eng.cam.ac.uk/pub/authors/13 Feb 2023: Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on inducing points, which are essential for the computational efficiency of GPs, nor do they handle ... Abstract: On a mathematical level, most -
Cambridge Machine Learning Group Publications
https://mlg.eng.cam.ac.uk/pub/13 Feb 2023: In many applications, particularly in physical sciences and engineering, but also in areas such as geostatistics and neuroscience, invariance to symmetries is one of the most fundamental forms of prior information ... We then provide an asymptotic -
Machine Learning Group Publications
https://mlg.eng.cam.ac.uk/pub/topics/13 Feb 2023: Journal of Machine Learning Research, 21, 2020. Abstract: Gaussian processes are distributions over functions that are versatile and mathematically convenient priors in Bayesian modelling. ... D. ) suffice and a method with an overall computational cost
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