CALM: An Enhanced Encoding and Confidence Evaluating Framework for Trustworthy Multi-view Learning-Reference-Cited by-同舟云学术

CALM: An Enhanced Encoding and Confidence Evaluating Framework for Trustworthy Multi-view Learning

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
language:
Short-container-title:

Author:

Zhou Hai¹^ORCID,Xue Zhe¹^ORCID,Liu Ying¹^ORCID,Li Boang¹^ORCID,Du Junping¹^ORCID,Liang Meiyu¹^ORCID,Qi Yuankai²^ORCID

Affiliation:

1. Beijing University of Posts and Telecommunications, Beijing, China

2. Australian Institute for Machine Learning, The University of Adelaide, Adelaide, Australia

Funder

CCF-Tencent Open Research Fund

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3611965

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4. Paramveer Dhillon , Dean P Foster , and Lyle Ungar . 2011. Multi-view learning of word embeddings via cca. Advances in neural information processing systems , Vol. 24 ( 2011 ). Paramveer Dhillon, Dean P Foster, and Lyle Ungar. 2011. Multi-view learning of word embeddings via cca. Advances in neural information processing systems, Vol. 24 (2011).

5. Song Fang and Quanyan Zhu . 2020. Independent Gaussian Distributions Minimize the Kullback-Leibler (KL) Divergence from Independent Gaussian Distributions. arXiv preprint arXiv:2011.02560 ( 2020 ). Song Fang and Quanyan Zhu. 2020. Independent Gaussian Distributions Minimize the Kullback-Leibler (KL) Divergence from Independent Gaussian Distributions. arXiv preprint arXiv:2011.02560 (2020).