Geometry Flow-Based Deep Riemannian Metric Learning-Reference-Cited by-同舟云学术

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Geometry Flow-Based Deep Riemannian Metric Learning

Published:2023-09 Issue:9 Volume:10 Page:1882-1892
ISSN:2329-9266
Container-title:IEEE/CAA Journal of Automatica Sinica
language:
Short-container-title:IEEE/CAA J. Autom. Sinica

Author:

Li Yangyang¹,Fei Chaoqun¹,Wang Chuanqing¹,Shan Hongming²,Lu Ruqian¹

Affiliation:

1. Chinese Academy of Sciences,Key Lab of MADIS Academy of Mathematics and Systems Science,Beijing,China,100190

2. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Ministry of Education) and MOE Frontiers Center for Brain Science,Shanghai,200433

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Information Systems,Control and Systems Engineering,Control and Optimization

Link

http://xplorestaging.ieee.org/ielx7/6570654/10219075/10219076.pdf?arnumber=10219076

Reference47 articles.

1. Regional Manifold Learning for Disease Classification

2. Network Alignment by Discrete Ollivier-Ricci Flow

3. An iterative pose estimation algorithm based on epipolar geometry with application to multi-target tracking

4. Curvature graph network;ye;Proc Int Conf Learning Representations,0

5. Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data

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