Similarity-based Learning
Similarity-Based Learning is a cognitive learning paradigm within concept formation that categorizes instances based on shared perceptual or structural features, distinguishing itself by relying on similarity relations among stimuli rather than explicit rule-based or feedback-driven mechanisms.
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Publications
25.5K
Citations
1.3K
Authors
565
Institutions
Publications per year
2017–2026
218
Parents
474
Clustering and projected clustering with adaptive neighbors
Feiping Nie, Xiaoqian Wang, Heng Huang · 2014 · 973 citations
Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
Bo Wang, Junjie Zhu, Emma Pierson et al. · Nature Methods · 2017 · 763 citations
End-to-End Video Instance Segmentation with Transformers
Yuqing Wang, Zhaoliang Xu, Xinlong Wang et al. · 2021 · 639 citations
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Chinese Academy of Sciences
Beijing, China
13 h-index
43 publications
4.8K citations
Carnegie Mellon University
Pittsburgh, United States
9 h-index
12 publications
670 citations

Zhejiang University
Hangzhou, China
8 h-index
14 publications
1.7K citations
University of Illinois Urbana-Champaign
Urbana, United States
8 h-index
15 publications
628 citations
Stanford University
Stanford, United States
7 h-index
15 publications
4.8K citations
Harvard University
Cambridge, United States
7 h-index
24 publications
1.2K citations
University of California, Berkeley
Berkeley, United States
7 h-index
14 publications
1.3K citations
The Ohio State University
Columbus, United States
7 h-index
10 publications
1K citations
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Repository · 20 publications · 358 citations
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11 publications · 556 citations
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7 publications · 359 citations
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7 publications · 120 citations
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Conference · 6 publications · 210 citations
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