Heteroscedastic Gaussian Process Regression for ReRAM Device Modeling-Reference-Cited by-同舟云学术

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Heteroscedastic Gaussian Process Regression for ReRAM Device Modeling

Published:2022-03-06 Issue: Volume: Page:
ISSN:
Container-title:2022 6th IEEE Electron Devices Technology & Manufacturing Conference (EDTM)
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Author:

Hossen Imtiaz¹,Zang Yi²,Anders Mark A.³,Wang Lin²,Adam Gina C.¹

Affiliation:

1. George Washington University,Department of Electrical and Computer Engineering,Washington, DC,USA,20052

2. the George Washington University,Department of Statistics,Washington, Dc,USA,20052

3. National Institution of Standards and Technology,Alternative Computing Group,Gaithersburg,MD,USA,20899

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9797886/9797892/09798256.pdf?arnumber=9798256

Reference14 articles.

1. Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments

2. A fast algorithm for two-dimensional Kolmogorov–Smirnov two sample tests

3. A kernel method for the two-sample-problem;gretton;Adv Neural Inf Process Syst,2006

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Interpolative Device Models for Hafnia-Based FeFETs;2023 7th IEEE Electron Devices Technology & Manufacturing Conference (EDTM);2023-03-07

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