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pro vyhledávání: '"Chang Paul"'
Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes elegantly tackle these problems, they struggle with scalability and hand
Externí odkaz:
http://arxiv.org/abs/2403.10929
Autor:
Kim, Sun-Chul
Publikováno v:
The Journal of Korean Studies (1979-), 2018 Oct 01. 23(2), 443-447.
Externí odkaz:
https://www.jstor.org/stable/48567915
Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data. We present a method that mitigates these issues by converting NNs from weight space to function space, via a dual parameterization. Importantly,
Externí odkaz:
http://arxiv.org/abs/2309.02195
Autor:
Moss, Dana M.
Publikováno v:
American Journal of Sociology, 2017 Nov 01. 123(3), 924-926.
Externí odkaz:
https://www.jstor.org/stable/26546059
Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accumulate over time due to inaccuracies in the posterior, hyperparameters,
Externí odkaz:
http://arxiv.org/abs/2306.03566
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with data and the need to run an optimization loop when using a non-Gaussian l
Externí odkaz:
http://arxiv.org/abs/2211.01053
Autor:
Bayler, Eric1 (AUTHOR) eric.bayler@noaa.gov, Chang, Paul S.1 (AUTHOR) jacqueline.shapo@noaa.gov, De La Cour, Jacqueline L.1,2 (AUTHOR) sean.helfrich@noaa.gov, Helfrich, Sean R.1 (AUTHOR) jeff.key@noaa.gov, Ignatov, Alexander1 (AUTHOR) veronica.lance@noaa.gov, Key, Jeff1 (AUTHOR) eric.leuliette@noaa.gov, Lance, Veronica1 (AUTHOR) deirdre.byrne@noaa.gov, Leuliette, Eric W.1 (AUTHOR) yinghui.liu@noaa.gov, Byrne, Deirdre A.1 (AUTHOR) xiaoming.liu@noaa.gov, Liu, Yinghui1 (AUTHOR) menghua.wang@noaa.gov, Liu, Xiaoming1,3 (AUTHOR) jianwei.wei@noaa.gov, Wang, Menghua1 (AUTHOR) paul.digiacomo@noaa.gov, Wei, Jianwei1,4 (AUTHOR), DiGiacomo, Paul M.1 (AUTHOR)
Publikováno v:
Remote Sensing. Jul2024, Vol. 16 Issue 14, p2656. 35p.
Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits. In this paper, we improve their computational efficiency by using a dual parameterization whe
Externí odkaz:
http://arxiv.org/abs/2111.03412
Autor:
Unschuld, Ulrike
Publikováno v:
East Asian Science, Technology, and Medicine, 2003 Jan 01(21), 167-170.
Externí odkaz:
https://www.jstor.org/stable/43150652