G. Seroussi
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 0.5%
- Information Systems top 0.5%
- Signal Processing top 0.5%
- Computer Networks and Communications top 2%
- Co-authors
- M.J. WeinbergerGuillermo SapiroIan F. BlakeNigel P. SmartErik OrdentlichRon M. RothNader H. BshoutySergio Verdú
- Topics
- Algorithms and Data Compression (41 papers)Coding theory and cryptography (23 papers)Cellular Automata and Applications (20 papers)
- Partner nations
- United StatesIsraelUruguay
In The Last Decade
G. Seroussi
90 papers receiving 3.9k citations
Hit Papers
Peers
Comparison fields: 5 of 98
- Computer Vision and Pattern Recognition 2.3k
- Artificial Intelligence 2.2k
- Information Systems 889
- Signal Processing 841
- Computer Networks and Communications 618
Countries citing papers authored by G. Seroussi
This map shows the geographic impact of G. Seroussi'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 G. Seroussi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. Seroussi more than expected).
Fields of papers citing papers by G. Seroussi
This network shows the impact of papers produced by G. Seroussi. 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 G. Seroussi. The network helps show where G. Seroussi may publish in the future.
Co-authorship network of co-authors of G. Seroussi
This figure shows the co-authorship network connecting the top 25 collaborators of G. Seroussi. A scholar is included among the top collaborators of G. Seroussi 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 G. Seroussi. G. Seroussi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 5 | |
| 5 | 25 | |
| 6 | 4 | |
| 7 | 46 | |
| 8 | 39 | |
| 9 | 5 | |
| 10 | 27 | |
| 11 | 91 | |
| 12 | 17 | |
| 13 | Inequalities for the L1 Deviation of the Empirical Distribution | 74 |
| 14 | The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LSbreakdown → | 1119 |
| 15 | 21 | |
| 16 | 19 | |
| 17 | 36 | |
| 18 | 37 | |
| 19 | 8 | |
| 20 | 1 |
About G. Seroussi
G. Seroussi is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Signal Processing, having authored 100 papers that have together received 4.4k indexed citations. Recurring topics across this work include Algorithms and Data Compression (41 papers), Coding theory and cryptography (23 papers) and Cellular Automata and Applications (20 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.3k citations), Signal Processing (841 citations) and Artificial Intelligence (2.2k citations). G. Seroussi has collaborated with scholars based in United States, Israel and Uruguay. Frequent co-authors include M.J. Weinberger, Guillermo Sapiro, Ian F. Blake, Nigel P. Smart, Erik Ordentlich, Ron M. Roth, Nader H. Bshouty, Sergio Verdú, Tsachy Weissman and A. Lempel. Their work appears in journals such as Bioinformatics, Proceedings of the IEEE and IEEE Transactions on Information Theory.
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.