Efim Kinber
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 2%
- Computer Networks and Communications
- Molecular Biology
- Computer Science Applications
- Co-authors
- Rūsiņš FreivaldsRolf WiehagenCarl H. SmithFrank StephanSanjay JainThomas ZeugmannMartin KummerKai Salomaa
- Topics
- Machine Learning and Algorithms (41 papers)Computability, Logic, AI Algorithms (31 papers)Algorithms and Data Compression (22 papers)
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceComputer Networks and Communications
- Partner nations
- United StatesSingaporeGermany
In The Last Decade
Efim Kinber
47 papers receiving 333 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 336
- Computational Theory and Mathematics 301
- Computer Networks and Communications 34
- Molecular Biology 19
- Computer Science Applications 5
Countries citing papers authored by Efim Kinber
This map shows the geographic impact of Efim Kinber'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 Efim Kinber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Efim Kinber more than expected).
Fields of papers citing papers by Efim Kinber
This network shows the impact of papers produced by Efim Kinber. 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 Efim Kinber. The network helps show where Efim Kinber may publish in the future.
Co-authorship network of co-authors of Efim Kinber
This figure shows the co-authorship network connecting the top 25 collaborators of Efim Kinber. A scholar is included among the top collaborators of Efim Kinber 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 Efim Kinber. Efim Kinber 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 | 1 | |
| 3 | 4 | |
| 4 | 4 | |
| 5 | 2 | |
| 6 | 3 | |
| 7 | Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations | 2 |
| 8 | 2 | |
| 9 | Theory of Computing: A Gentle Introduction | 8 |
| 10 | 10 | |
| 11 | 5 | |
| 12 | 30 | |
| 13 | Language Learning from Texts: Mind Changes, Limited Memory and Monotonicity (Extended Abstract) | 1 |
| 14 | 26 | |
| 15 | 22 | |
| 16 | 1 | |
| 17 | On the Power of Probabilistic Inductive Inference in Nonstandard Numberings. | 0 |
| 18 | 5 | |
| 19 | A Note on Limit Identification of c-minimal Indices. | 3 |
| 20 | 13 |
About Efim Kinber
Efim Kinber is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Computer Science Applications, having authored 52 papers that have together received 360 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (41 papers), Computability, Logic, AI Algorithms (31 papers) and Algorithms and Data Compression (22 papers). The work is most often cited by research in Computational Theory and Mathematics (301 citations), Artificial Intelligence (336 citations) and Computer Networks and Communications (34 citations). Efim Kinber has collaborated with scholars based in United States, Singapore and Germany. Frequent co-authors include Rūsiņš Freivalds, Rolf Wiehagen, Carl H. Smith, Frank Stephan, Sanjay Jain, Thomas Zeugmann, Martin Kummer, Kai Salomaa, Sheng Yü and Arto Salomaa. Their work appears in journals such as Information Sciences, Journal of the ACM and Machine Learning.
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.