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Real World Linear Data Examples

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Preview of Deep Kernel for GPs
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Deep Kernel for GPs (328_main_paper.pdf)

3.04 MBEN 10 pages
A novel Gaussian process kernel is proposed, leveraging a deep neural network structure while maintaining interpretability. It addresses issues of optimality, explainability, model complexity, and sample efficiency. The kernel design involves deriving an optimal non-stationary...

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kernel structure novel gaussian process proposed leveraging neural network maintaining interpretability addresses issues optimality explainability model complexity sample efficiency design involves deriving optimal stationary product

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