Latent Space Models
Latent Space Models is a type of probabilistic model in latent variable analysis that represents observed data as generated from unobserved (latent) variables structured within a geometric space, where the spatial relationships encode similarities or interactions among entities.
1.2K
Publications
95.8K
Citations
4K
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1K
Institutions
Publications per year
2017–2026
872
Parents
1.2K
Document clustering based on non-negative matrix factorization
Wei Xu, Xin Liu, Yihong Gong · 2003 · 1.9K citations
Scalable Diffusion Models with Transformers
William Peebles, Saining Xie · 2023 · 1.4K citations
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Carnegie Mellon University
Pittsburgh, United States
23 h-index
83 publications
4.3K citations
University of Washington
Seattle, United States
20 h-index
61 publications
13K citations
Tsinghua University
Beijing, China
19 h-index
51 publications
4.2K citations

Chinese Academy of Sciences
Beijing, China
18 h-index
68 publications
3.6K citations
Stanford University
Stanford, United States
18 h-index
50 publications
5.8K citations
University of Illinois Urbana-Champaign
Urbana, United States
18 h-index
38 publications
2.2K citations
Nanyang Technological University
Singapore, Singapore
15 h-index
32 publications
2K citations

University of Toronto
Toronto, Canada
15 h-index
27 publications
8.2K citations
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Repository · 171 publications · 9.4K citations
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Conference · 29 publications · 2.2K citations
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15 publications · 758 citations
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TPAMI · 13 publications · 3.1K citations
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TOG · 15 publications · 1.1K citations
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