Mark Brophy
Impact in
-
- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Advanced Radiotherapy Techniques 2
-
- Advanced Neural Network Applications 2
- Medical Image Segmentation Techniques 1
- Co-authors
- Aayush Prakash (2 shared papers)Stan T. Birchfield (2 shared papers)David Acuna (2 shared papers)Cem Anil (1 shared paper)Jonathan Tremblay (1 shared paper)Thang To (1 shared paper)Varun Jampani (1 shared paper)Gavriel State (1 shared paper)
- Journals
- Acta Oncologica (1 paper)IEEE Geoscience and Remote Sensing Letters (1 paper)IEEE Transactions on Computational Biology and Bioinformatics (1 paper)Physics in Medicine and Biology (1 paper)Conference on Computer and Robot Vision (CRV) (1 paper)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Mark Brophy
7 papers receiving 881 citations
Mark Brophy's Hit Papers
Peers
Comparison fields: 5 of 72
- Computer Vision and Pattern Recognition 421
- Artificial Intelligence 275
- Industrial and Manufacturing Engineering 69
- Automotive Engineering 45
- Control and Systems Engineering 94
Countries citing papers authored by Mark Brophy
This map shows the geographic impact of Mark Brophy'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 Mark Brophy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Brophy more than expected).
Fields of papers citing papers by Mark Brophy
This network shows the impact of papers produced by Mark Brophy. 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 Mark Brophy. The network helps show where Mark Brophy may publish in the future.
Co-authors
The 23 scholars most cited alongside Mark Brophy, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization Hit paper breakdown → | 2018 | 634 |
| 2 | 2019 | 189 | |
| 3 | 2018 | 70 | |
| 4 | 2014 | 19 | |
| 5 | 2015 | 6 | |
| 6 | 2015 | 5 | |
| 7 | 2016 | 5 |
About Mark Brophy
Mark Brophy is a scholar working on Radiation, Computer Vision and Pattern Recognition, Computational Mechanics, Artificial Intelligence and Plant Science, having authored 7 papers that have together received 928 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), 3D Shape Modeling and Analysis (2 papers), Advanced Radiotherapy Techniques (2 papers), Smart Agriculture and AI (1 paper), Greenhouse Technology and Climate Control (1 paper) and Medical Image Segmentation Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (421 citations), Artificial Intelligence (275 citations), Industrial and Manufacturing Engineering (69 citations), Automotive Engineering (45 citations) and Control and Systems Engineering (94 citations). Mark Brophy has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Aayush Prakash, Stan T. Birchfield, David Acuna, Cem Anil, Jonathan Tremblay, Thang To, Varun Jampani, Gavriel State, John A. Barron and Ali Talasaz. Their work appears in journals such as Acta Oncologica, IEEE Geoscience and Remote Sensing Letters, IEEE Transactions on Computational Biology and Bioinformatics, Physics in Medicine and Biology and Conference on Computer and Robot Vision (CRV).
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.