SigGPDE: scaling sparse Gaussian Processes on sequential data

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...

وصف كامل

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Lemercier, M, Salvi, C, Cass, T, Bonilla, EV, Damoulas, T, Lyons, T
التنسيق: Internet publication
اللغة:English
منشور في: 2021

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