Chris K. Williams
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Computational Mechanics
- Signal Processing
- Computer Networks and Communications
- Co-authors
- John Shawe‐TaylorAron CulottaJohn LaffertyRichard S. ZemelNello CristianiniJaz KandolaYoshua BengioDale Schuurmans
- Topics
- Sparse and Compressive Sensing Techniques (1 paper)Random Matrices and Applications (1 paper)Internet Traffic Analysis and Secure E-voting (1 paper)
- Journals
- IEEE Transactions on Information TheoryApress eBooks
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Chris K. Williams
8 papers receiving 272 citations
Peers
Comparison fields: 5 of 87
- Artificial Intelligence 133
- Computer Vision and Pattern Recognition 93
- Computational Mechanics 40
- Signal Processing 36
- Computer Networks and Communications 21
Countries citing papers authored by Chris K. Williams
This map shows the geographic impact of Chris K. Williams'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 Chris K. Williams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris K. Williams more than expected).
Fields of papers citing papers by Chris K. Williams
This network shows the impact of papers produced by Chris K. Williams. 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 Chris K. Williams. The network helps show where Chris K. Williams may publish in the future.
Co-authorship network of co-authors of Chris K. Williams
This figure shows the co-authorship network connecting the top 25 collaborators of Chris K. Williams. A scholar is included among the top collaborators of Chris K. Williams 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 Chris K. Williams. Chris K. Williams is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 9 | |
| 3 | Technical Report: Articulated Part-based Model for Joint Object Detection and Pose Estimation | 1 |
| 4 | Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 1 | 42 |
| 5 | Proceedings of the 23rd International Conference on Neural Information Processing Systems | 77 |
| 6 | Proceedings of the 22nd International Conference on Neural Information Processing Systems | 66 |
| 7 | 1 | |
| 8 | 86 |
About Chris K. Williams
Chris K. Williams is a scholar working on Statistics and Probability, Signal Processing and Computer Vision and Pattern Recognition, having authored 8 papers that have together received 285 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (1 paper), Random Matrices and Applications (1 paper) and Internet Traffic Analysis and Secure E-voting (1 paper). The work is most often cited by research in Computational Mathematics (6 citations), Computer Vision and Pattern Recognition (93 citations) and Artificial Intelligence (133 citations). Chris K. Williams has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include John Shawe‐Taylor, Aron Culotta, John Lafferty, Richard S. Zemel, Nello Cristianini, Jaz Kandola, Yoshua Bengio, Dale Schuurmans, Silvio Savarese and Luc Van Gool. Their work appears in journals such as IEEE Transactions on Information Theory and Apress eBooks.
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.