Joint Embedding
Joint Embedding is a computational framework that integrates multiple single-cell omics datasets by mapping them into a shared latent space, enabling comparative analysis while preserving biological variation specific to each dataset.
205
Publications
18.2K
Citations
700
Authors
256
Institutions
Publications per year
2017–2026
173
Parents
205
Learning Deep Representations of Fine-Grained Visual Descriptions
Scott Reed, Zeynep Akata, Honglak Lee et al. · 2016 · 793 citations
Representing Text for Joint Embedding of Text and Knowledge Bases
Kristina Toutanova, Danqi Chen, Patrick Pantel et al. · 2015 · 789 citations · Full text
Learning a Deep Embedding Model for Zero-Shot Learning
Li Zhang, Tao Xiang, Shaogang Gong · 2017 · 786 citations
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256
Carnegie Mellon University
Pittsburgh, United States
8 h-index
16 publications
1.4K citations
Google (United States)
Mountain View, United States
6 h-index
15 publications
3.2K citations

Xidian University
Xi'an, China
6 h-index
15 publications
385 citations

Beihang University
Beijing, China
6 h-index
15 publications
885 citations
Tsinghua University
Beijing, China
5 h-index
18 publications
1.6K citations
Harvard University
Cambridge, United States
5 h-index
19 publications
843 citations
Tencent (China)
Shenzhen, China
5 h-index
21 publications
2.1K citations
University of Oxford
Oxford, United Kingdom
5 h-index
15 publications
4K citations
1–8 of 256
Repository · 34 publications · 2.7K citations
21
h-index
7 publications · 428 citations
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Conference · 4 publications · 693 citations
4
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4 publications · 399 citations
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4 publications · 773 citations
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