Intermediate Representation Selection
Intermediate Representation Selection is the process of choosing a suitable intermediate representation within a compiler framework that balances abstraction fidelity and optimization potential to facilitate efficient code generation and transformation during just-in-time compilation.
70
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7.4K
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268
Authors
111
Institutions
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2017–2026
33
Parents
70
Yang Zhou, Xintong Han, Eli Shechtman et al. · ACM Transactions on Graphics · 2020 · 311 citations · Full text
Group-Sensitive Triplet Embedding for Vehicle Reidentification
Yan Bai, Yihang Lou, Feng Gao et al. · IEEE Transactions on Multimedia · 2018 · 270 citations
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
Lluís Castrejón, Yusuf Aytar, Carl Vondrick et al. · 2016 · 197 citations
Group-sensitive multiple kernel learning for object categorization
Jingjing Yang, Yuanning Li, Yonghong Tian et al. · 2009 · 149 citations
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Massachusetts Institute of Technology
Cambridge, United States
3 h-index
9 publications
16.6K citations

Johns Hopkins University
Baltimore, United States
3 h-index
3 publications
63 citations
Google (United States)
Mountain View, United States
3 h-index
22 publications
1.7K citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
8 publications
158 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
2 h-index
3 publications
116 citations
Institute of Automation
Beijing, China
2 h-index
8 publications
295 citations

City University of Hong Kong
Hong Kong, Hong Kong
2 h-index
2 publications
291 citations

Peking University
Beijing, China
2 h-index
8 publications
1.7K citations
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Repository · 7 publications · 270 citations
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2 publications · 344 citations
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TPAMI · 2 publications · 4.2K citations
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Conference · 1 publications · 37 citations
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IJP · 1 publications · 10 citations
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