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...
المؤلفون الرئيسيون: | Lemercier, M, Salvi, C, Cass, T, Bonilla, EV, Damoulas, T, Lyons, T |
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التنسيق: | Internet publication |
اللغة: | English |
منشور في: |
2021
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مواد مشابهة
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