FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3000508506> ?p ?o ?g. }

• W3000508506 endingPage "4423" @default.
• W3000508506 startingPage "4405" @default.
• W3000508506 abstract "The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process regression (GPR), a well-known nonparametric, and interpretable Bayesian model, which suffers from cubic complexity to data size. To improve the scalability while retaining desirable prediction quality, a variety of scalable GPs have been presented. However, they have not yet been comprehensively reviewed and analyzed to be well understood by both academia and industry. The review of scalable GPs in the GP community is timely and important due to the explosion of data size. To this end, this article is devoted to reviewing state-of-the-art scalable GPs involving two main categories: global approximations that distillate the entire data and local approximations that divide the data for subspace learning. Particularly, for global approximations, we mainly focus on sparse approximations comprising prior approximations that modify the prior but perform exact inference, posterior approximations that retain exact prior but perform approximate inference, and structured sparse approximations that exploit specific structures in kernel matrix; for local approximations, we highlight the mixture/product of experts that conducts model averaging from multiple local experts to boost predictions. To present a complete review, recent advances for improving the scalability and capability of scalable GPs are reviewed. Finally, the extensions and open issues of scalable GPs in various scenarios are reviewed and discussed to inspire novel ideas for future research avenues." @default.
• W3000508506 created "2020-01-23" @default.
• W3000508506 creator A5011600130 @default.
• W3000508506 creator A5017901486 @default.
• W3000508506 creator A5060723344 @default.
• W3000508506 creator A5061132796 @default.
• W3000508506 date "2020-11-01" @default.
• W3000508506 modified "2023-10-14" @default.
• W3000508506 title "When Gaussian Process Meets Big Data: A Review of Scalable GPs" @default.
• W3000508506 cites W143645861 @default.
• W3000508506 cites W1486164486 @default.
• W3000508506 cites W1499730991 @default.
• W3000508506 cites W1524392826 @default.
• W3000508506 cites W1567512734 @default.
• W3000508506 cites W1762233554 @default.
• W3000508506 cites W1870250857 @default.
• W3000508506 cites W1898904249 @default.
• W3000508506 cites W19296495 @default.
• W3000508506 cites W1968414728 @default.
• W3000508506 cites W1970679066 @default.
• W3000508506 cites W1972700889 @default.
• W3000508506 cites W1973310094 @default.
• W3000508506 cites W1987052429 @default.
• W3000508506 cites W1998092191 @default.
• W3000508506 cites W2004643132 @default.
• W3000508506 cites W2007864935 @default.
• W3000508506 cites W2013097030 @default.
• W3000508506 cites W2018044188 @default.
• W3000508506 cites W2021247854 @default.
• W3000508506 cites W2024697317 @default.
• W3000508506 cites W2025561056 @default.
• W3000508506 cites W2025653905 @default.
• W3000508506 cites W2040726420 @default.
• W3000508506 cites W2046916337 @default.
• W3000508506 cites W2051588691 @default.
• W3000508506 cites W2053652664 @default.
• W3000508506 cites W2054799446 @default.
• W3000508506 cites W2060624359 @default.
• W3000508506 cites W2061082730 @default.
• W3000508506 cites W2064675550 @default.
• W3000508506 cites W2064798076 @default.
• W3000508506 cites W2066334462 @default.
• W3000508506 cites W2066596787 @default.
• W3000508506 cites W2068238590 @default.
• W3000508506 cites W2080006911 @default.
• W3000508506 cites W2083291282 @default.
• W3000508506 cites W2087232378 @default.
• W3000508506 cites W2091651498 @default.
• W3000508506 cites W2092271904 @default.
• W3000508506 cites W2098316308 @default.
• W3000508506 cites W2099878672 @default.
• W3000508506 cites W2099994229 @default.
• W3000508506 cites W2101709642 @default.
• W3000508506 cites W2114355534 @default.
• W3000508506 cites W2116064496 @default.
• W3000508506 cites W2124611503 @default.
• W3000508506 cites W2125275885 @default.
• W3000508506 cites W2126877537 @default.
• W3000508506 cites W2128638437 @default.
• W3000508506 cites W2128973832 @default.
• W3000508506 cites W2129564505 @default.
• W3000508506 cites W2137133844 @default.
• W3000508506 cites W2138481796 @default.
• W3000508506 cites W2144533338 @default.
• W3000508506 cites W2150884987 @default.
• W3000508506 cites W2160299137 @default.
• W3000508506 cites W2170952786 @default.
• W3000508506 cites W2171647935 @default.
• W3000508506 cites W2171779101 @default.
• W3000508506 cites W2173213060 @default.
• W3000508506 cites W2192203593 @default.
• W3000508506 cites W2198195940 @default.
• W3000508506 cites W2290327149 @default.
• W3000508506 cites W2292090008 @default.
• W3000508506 cites W2296634216 @default.
• W3000508506 cites W2337086579 @default.
• W3000508506 cites W2395877431 @default.
• W3000508506 cites W2413379912 @default.
• W3000508506 cites W2465297489 @default.
• W3000508506 cites W2514303448 @default.
• W3000508506 cites W2558411832 @default.
• W3000508506 cites W2592279581 @default.
• W3000508506 cites W2729558485 @default.
• W3000508506 cites W2730546798 @default.
• W3000508506 cites W2734724549 @default.
• W3000508506 cites W2736716546 @default.
• W3000508506 cites W2736796540 @default.
• W3000508506 cites W2766447205 @default.
• W3000508506 cites W2775658044 @default.
• W3000508506 cites W2781973163 @default.
• W3000508506 cites W2914713740 @default.
• W3000508506 cites W2919115771 @default.
• W3000508506 cites W2962875621 @default.
• W3000508506 cites W2962993994 @default.
• W3000508506 cites W2963690361 @default.
• W3000508506 cites W2963754333 @default.
• W3000508506 cites W2963974141 @default.
• W3000508506 cites W2964326308 @default.

• next