Loss Validation
Loss Validation is a methodological process in catastrophe claims management that systematically verifies the accuracy, authenticity, and legitimacy of reported damages and associated costs to distinguish genuine claims from inaccuracies or fraud.
24
Publications
1.9K
Citations
88
Authors
38
Institutions
Publications per year
2017–2026
22
24
Searching for a Robust Neural Architecture in Four GPU Hours
Xuanyi Dong, Yi Yang · 2019 · 679 citations · Full text
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration
Yang He, Yuhang Ding, Ping Liu et al. · 2020 · 256 citations · Full text
One-Shot Neural Architecture Search via Self-Evaluated Template Network
Xuanyi Dong, Yi Yang · 2019 · 207 citations · Full text
Adel Rajab, Hira Farman, Noman Islam et al. · Water · 2023 · 75 citations · Full text
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38
Baidu (China)
Beijing, China
3 h-index
4 publications
1.3K citations
University of Technology Sydney
Sydney, Australia
3 h-index
7 publications
2.6K citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
7 publications
460 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
15 publications
904 citations

National Yang Ming Chiao Tung University
Hsinchu, Taiwan
1 h-index
1 publications
45 citations
Odisha University of Technology and Research
Bhubaneswar, India
1 h-index
2 publications
44 citations
Higher Institute of Engineering
Cairo, Egypt
1 h-index
1 publications
23 citations
University of Washington
Seattle, United States
1 h-index
1 publications
42 citations
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Repository · 3 publications · 67 citations
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2 publications · 75 citations
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2 publications · 152 citations
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ETASR · 1 publications · 13 citations
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IJACSA · 1 publications · 13 citations
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