Output-Dependent Gaussian Process State-Space Model-Reference-Cited by-同舟云学术

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Output-Dependent Gaussian Process State-Space Model

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Lin Zhidi¹,Cheng Lei²,Yin Feng¹,Xu Lexi³,Cui Shuguang¹

Affiliation:

1. The Chinese University of Hong Kong,School of Science and Engineering,Shenzhen,China

2. Zhejiang University,College of Information Science and Electronic Engineering,Hangzhou,China

3. China United Network Communications Corporation,Research Institute,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10095784.pdf?arnumber=10095784

Reference28 articles.

1. Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data

2. Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series

3. Overcoming mean-field approximations in recurrent Gaussian process models;ialongo;Proc Int Conf Mach Learn (ICML),2019

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. Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

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