SigGPDE: scaling sparse Gaussian Processes on sequential data

SigGPDE: scaling sparse Gaussian Processes on sequential data

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Making predictions and quantifying their uncertainty when the input data is sequential is a fundamental learning challenge, recently attracting increasing attention. We develop SigGPDE, a new scalable sparse variational inference framework for Gaussian Processes (GPs) on sequential data. Our contrib...

书目详细资料
Main Authors:	Lemercier, M, Salvi, C, Cass, T, Bonilla, EV, Damoulas, T, Lyons, T
格式:	Internet publication
语言:	English
出版:	2021

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