Tensor Product Features
Tensor Product Features is a method in machine learning that constructs high-dimensional feature representations by computing the tensor product of individual feature vectors, thereby capturing multiplicative interactions among input variables.
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3.4K
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129
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2017–2026
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Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang et al. · 2018 · 1K citations · Full text
Unsupervised Domain Adaptation with Residual Transfer Networks
Mingsheng Long, Zhu Han, Jianmin Wang et al. · arXiv (Cornell University) · 2016 · 1K citations · Full text
Unsupervised Domain Adaptation with Residual Transfer Networks
Mingsheng Long, Zhu Han, Jianmin Wang et al. · arXiv (Cornell University) · 2016 · 251 citations · Full text
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Centre National de la Recherche Scientifique
Paris, France
3 h-index
6 publications
115 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
5 publications
218 citations
Inserm
Paris, France
2 h-index
10 publications
362 citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
2 publications
100 citations
University of Science and Technology of China
Hefei, China
2 h-index
5 publications
3.2K citations
University of California, Berkeley
Berkeley, United States
2 h-index
2 publications
1.3K citations
Tsinghua University
Beijing, China
2 h-index
6 publications
3.8K citations
Georgia Institute of Technology
Atlanta, United States
2 h-index
4 publications
90 citations
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Repository · 5 publications · 1.3K citations
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