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...
| Main Authors: | , , , , , |
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| Format: | Internet publication |
| Language: | English |
| Published: |
2021
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