James J. Sharp

1.2k citations
61 papers · 728 · 1 hit paper · h-index 11

Impact in

Papers in

James J. Sharp

50 papers receiving 692 citations

James J. Sharp's Hit Papers

A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability 2020 · 324 citations
3240+2+4Years since publication100200300

Peers

James J. Sharp
Comparison fields: 5 of 97
  • Software 141
  • Health Informatics 17
  • Artificial Intelligence 405
  • Signal Processing 49
  • Computer Vision and Pattern Recognition 93
Replace Graham Riley with:
Graham Riley United Kingdom
D. Chaudhuri India
Shahram Golzari Iran
Giulia DeSalvo United States
Jun Hu China
Jonathan Smith United States
Lifang Chen China
Xiaoya Li China
Kun Zhu China
Jiaxing Huang Singapore
James J. Sharp relative to Graham Riley United Kingdom Graham Riley's profile →
Citations per field
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Citations per year

Countries citing papers authored by James J. Sharp

Since Specialization
Citations

This map shows the geographic impact of James J. Sharp's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by James J. Sharp with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James J. Sharp more than expected).

Fields of papers citing papers by James J. Sharp

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James J. Sharp. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by James J. Sharp. The network helps show where James J. Sharp may publish in the future.

Co-authors

The 25 scholars most cited alongside James J. Sharp, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with James J. Sharp Line = papers co-authored together James J. Sharp links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 61 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
Hit paper breakdown →
2020324
2 201969
3 201945
4 202034
5 201922
6 198420
7 201015
8 196914
9 199114
10 199613
11 200010
12
The Jackson Laboratory Induced Mutant Resource.
199410
13 19939
14 19699
15
A Survey of Safety and Trustworthiness of Deep Neural Networks
20188
16 19928
17 19917
18 19907
19 19916
20 19906

About James J. Sharp

James J. Sharp is a scholar working on Civil and Structural Engineering, Ecology, Artificial Intelligence, Oceanography and Computational Mechanics, having authored 61 papers that have together received 728 indexed citations. Recurring topics across this work include Hydrology and Sediment Transport Processes (9 papers), Adversarial Robustness in Machine Learning (8 papers), Water Systems and Optimization (8 papers), Hydraulic flow and structures (8 papers), Anomaly Detection Techniques and Applications (6 papers), Underwater Acoustics Research (5 papers), Dam Engineering and Safety (4 papers) and Software Testing and Debugging Techniques (4 papers). The work is most often cited by research in Software (141 citations), Health Informatics (17 citations), Artificial Intelligence (405 citations), Signal Processing (49 citations) and Computer Vision and Pattern Recognition (93 citations). James J. Sharp has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include Xiaowei Huang, Youcheng Sun, Daniel Kroening, Min Wu, Wenjie Ruan, Xinping Yi, Matthew Q. Hill, Rob Ashmore, Trimbak M. Parchure and David Flynn. Their work appears in journals such as Journal of Hydraulic Engineering, Canadian Journal of Civil Engineering, Water International, Higher Education and Fuel.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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