Marc T. Law
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 10%
- Statistical and Nonlinear Physics
- Media Technology
- Co-authors
- Richard S. ZemelRaquel UrtasunJixuan WangKuan-Chieh WangFrank RudziczMichael BrudnoMatthieu CordNicolas Thome
- Topics
- Face and Expression Recognition (5 papers)Neural Networks and Applications (3 papers)Video Surveillance and Tracking Methods (2 papers)
- Journals
- International Journal of Computer Vision2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv (Cornell University)
- Partner nations
- CanadaUnited KingdomFrance
In The Last Decade
Marc T. Law
12 papers receiving 213 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 155
- Computer Vision and Pattern Recognition 112
- Signal Processing 60
- Statistical and Nonlinear Physics 13
- Media Technology 13
Countries citing papers authored by Marc T. Law
This map shows the geographic impact of Marc T. Law'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 Marc T. Law with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc T. Law more than expected).
Fields of papers citing papers by Marc T. Law
This network shows the impact of papers produced by Marc T. Law. 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 Marc T. Law. The network helps show where Marc T. Law may publish in the future.
Co-authorship network of co-authors of Marc T. Law
This figure shows the co-authorship network connecting the top 25 collaborators of Marc T. Law. A scholar is included among the top collaborators of Marc T. Law based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marc T. Law. Marc T. Law is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 15 | |
| 3 | Ultrahyperbolic Neural Networks | 6 |
| 4 | 2 | |
| 5 | Lorentzian Distance Learning for Hyperbolic Representations | 22 |
| 6 | 1 | |
| 7 | 68 | |
| 8 | Dimensionality Reduction for Representing the Knowledge of Probabilistic Models | 3 |
| 9 | Lorentzian Distance Learning | 0 |
| 10 | Deep Spectral Clustering Learning. | 60 |
| 11 | 10 | |
| 12 | 22 | |
| 13 | 15 |
About Marc T. Law
Marc T. Law is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 13 papers that have together received 225 indexed citations. Recurring topics across this work include Face and Expression Recognition (5 papers), Neural Networks and Applications (3 papers) and Video Surveillance and Tracking Methods (2 papers). The work is most often cited by research in Signal Processing (60 citations), Computer Vision and Pattern Recognition (112 citations) and Artificial Intelligence (155 citations). Marc T. Law has collaborated with scholars based in Canada, United Kingdom and France. Frequent co-authors include Richard S. Zemel, Raquel Urtasun, Jixuan Wang, Kuan-Chieh Wang, Frank Rudzicz, Michael Brudno, Matthieu Cord, Nicolas Thome, Jake Snell and Eric P. Xing. Their work appears in journals such as International Journal of Computer Vision, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and arXiv (Cornell University).
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.