Underfitting
**Underfitting** is a model misspecification phenomenon in machine learning in which a hypothesis of insufficient complexity fails to capture the underlying structure of the training data, thereby exhibiting high bias and poor predictive performance on both training and unseen data.
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2017–2026
850
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. · 2014 · 34.3K citations
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · 2015 · 18.8K citations
Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, Christian Szegedy · arXiv (Cornell University) · 2014 · 8.2K citations · Full text
Cascade R-CNN: Delving Into High Quality Object Detection
Zhaowei Cai, Nuno Vasconcelos · 2018 · 6.8K citations
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Google (United States)
Mountain View, United States
23 h-index
82 publications
30.8K citations
Stanford University
Stanford, United States
19 h-index
49 publications
4.9K citations
University of California, Berkeley
Berkeley, United States
17 h-index
36 publications
4.6K citations

Chinese Academy of Sciences
Beijing, China
14 h-index
44 publications
2.1K citations
Carnegie Mellon University
Pittsburgh, United States
14 h-index
33 publications
4K citations
Tsinghua University
Beijing, China
14 h-index
52 publications
2.3K citations
University of Chinese Academy of Sciences
Beijing, China
14 h-index
37 publications
2.1K citations
University of Technology Sydney
Sydney, Australia
13 h-index
35 publications
1.7K citations
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Repository · 260 publications · 39.2K citations
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Conference · 27 publications · 1.6K citations
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22 publications · 2.1K citations
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Conference · 12 publications · 1.1K citations
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12 publications · 573 citations
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