Crossed Features
Crossed Features is a methodological construct in machine learning that combines two or more distinct feature dimensions through Cartesian product expansion to generate higher-order interaction representations.
29
Publications
2.4K
Citations
105
Authors
46
Institutions
Publications per year
2017–2026
14
Variants
Feature Crosses
Parents
29
Deep & Cross Network for Ad Click Predictions
Ruoxi Wang, Bin Fu, Gang Fu et al. · 2017 · 1.1K citations · Full text
Ying Shan, T. Ryan Hoens, Jian Jiao et al. · 2016 · 417 citations
Yuanfei Luo, Mengshuo Wang, Hao Zhou et al. · 2019 · 95 citations
A sensory level on the trunk in lower lateral brainstem lesions
Sadayuki Matsumoto, Bungo Okuda, Terukuni Imai et al. · Neurology · 1988 · 52 citations
Medicine, Neurology, Neuroscience +15
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46
Google (United States)
Mountain View, United States
2 h-index
6 publications
3.5K citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
6 publications
531 citations
Stanford University
Stanford, United States
2 h-index
2 publications
1.2K citations
SPring-8
Sayō, Japan
1 h-index
7 publications
105 citations
National University of Singapore
Singapore, Singapore
1 h-index
2 publications
78 citations
University of Science and Technology of China
Hefei, China
1 h-index
1 publications
39 citations
Peng Cheng Laboratory
Shenzhen, China
1 h-index
1 publications
21 citations

Western University
London, Canada
1 h-index
2 publications
24 citations
1–8 of 46
Repository · 3 publications · 68 citations
3
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1 publications · 26 citations
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1 publications · 15 citations
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1 publications · 36 citations
1
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1 publications · 13 citations
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