Robust Loss
Robust Loss is a type of loss function in machine learning that is designed to minimize the influence of outliers and noisy data points by maintaining stable performance across deviations from model assumptions.
287
Publications
21.5K
Citations
867
Authors
364
Institutions
Publications per year
2017–2026
199
Parents
287
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Wei‐Sheng Lai, Jia‐Bin Huang, Narendra Ahuja et al. · 2017 · 3K citations
Theoretically Principled Trade-off between Robustness and Accuracy
Yaodong Yu, Jiantao Jiao, Eric P. Xing et al. · arXiv (Cornell University) · 2019 · 925 citations · Full text
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Chinese Academy of Sciences
Beijing, China
14 h-index
30 publications
1K citations
China Agricultural University
Beijing, China
8 h-index
16 publications
410 citations

Xi'an Jiaotong University
Xi'an, China
8 h-index
24 publications
1.4K citations
University of Chinese Academy of Sciences
Beijing, China
7 h-index
17 publications
454 citations

Indian Institute of Science Bangalore
Bengaluru, India
7 h-index
16 publications
2.7K citations
Google (United States)
Mountain View, United States
7 h-index
12 publications
548 citations
The University of Texas at Arlington
Arlington, United States
6 h-index
13 publications
1.4K citations
Stanford University
Stanford, United States
6 h-index
11 publications
493 citations
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Repository · 55 publications · 4.3K citations
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TPAMI · 12 publications · 1.8K citations
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Conference · 7 publications · 1.1K citations
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5 publications · 494 citations
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5 publications · 276 citations
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