Model Complexity
Model Complexity is a theoretical construct in statistical learning that characterizes the flexibility of a model in approximating the underlying data-generating function, distinguished by its capacity to fit varying levels of noise and signal in the training data.
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2017–2026
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ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
Qilong Wang, Banggu Wu, Pengfei Zhu et al. · 2020 · 8.1K citations
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Centre National de la Recherche Scientifique
Paris, France
24 h-index
72 publications
7K citations
Massachusetts Institute of Technology
Cambridge, United States
23 h-index
54 publications
4.2K citations
University of California, Berkeley
Berkeley, United States
22 h-index
53 publications
5.9K citations
Stanford University
Stanford, United States
21 h-index
48 publications
5.6K citations
Carnegie Mellon University
Pittsburgh, United States
21 h-index
59 publications
6K citations
Tsinghua University
Beijing, China
20 h-index
54 publications
9.6K citations

Chinese Academy of Sciences
Beijing, China
20 h-index
82 publications
4.8K citations
University of Oxford
Oxford, United Kingdom
19 h-index
50 publications
3.8K citations
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Repository · 151 publications · 10.2K citations
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31 publications · 5.3K citations
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22 publications · 1.7K citations
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19 publications · 2.2K citations
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17 publications · 800 citations
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