Matches in SemOpenAlex for { <https://semopenalex.org/work/W3033147585> ?p ?o ?g. }
- W3033147585 abstract "We provide approximation guarantees for a linear-time inferential framework for Gaussian processes, using two low-rank kernel approximations based on random Fourier features and truncation of Mercer expansions. In particular, we bound the Kullback-Leibler divergence between the idealized Gaussian process and the one resulting from a low-rank approximation to its kernel. Additionally, we present strong evidence that these two approximations, enhanced by an initial automatic feature extraction through deep neural networks, outperform a broad range of state-of-the-art methods in terms of time efficiency, negative log-predictive density, and root mean squared error." @default.
- W3033147585 created "2020-06-12" @default.
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- W3033147585 date "2020-04-03" @default.
- W3033147585 modified "2023-09-27" @default.
- W3033147585 title "Scalable Gaussian Processes, with Guarantees: Kernel Approximations and Deep Feature Extraction" @default.
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