John Case
- Artificial Intelligence top 2%
- Computational Theory and Mathematics top 0.5%
- Cognitive Neuroscience top 5%
- Cellular and Molecular Neuroscience
- Computer Networks and Communications top 10%
- Co-authors
- Carl H. SmithRobert T. KnightBradley VoytekKyle Q. LepageAdam GazzaleyMark KramerSanjay JainJames S. Royer
- Topics
- Machine Learning and Algorithms (59 papers)Computability, Logic, AI Algorithms (53 papers)Algorithms and Data Compression (29 papers)
- Partner nations
- United StatesSingaporeGermany
In The Last Decade
John Case
87 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 120
- Artificial Intelligence 797
- Computational Theory and Mathematics 716
- Cognitive Neuroscience 446
- Cellular and Molecular Neuroscience 119
- Computer Networks and Communications 80
Countries citing papers authored by John Case
This map shows the geographic impact of John Case'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 John Case with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Case more than expected).
Fields of papers citing papers by John Case
This network shows the impact of papers produced by John Case. 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 John Case. The network helps show where John Case may publish in the future.
Co-authorship network of co-authors of John Case
This figure shows the co-authorship network connecting the top 25 collaborators of John Case. A scholar is included among the top collaborators of John Case 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 John Case. John Case 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 | 1 | |
| 3 | Algorithmic Scientific Inference: Within Our Computable Expected Reality? | 3 |
| 4 | 2 | |
| 5 | AUTOMATIC FUNCTIONS, LINEAR TIME AND LEARNING ∗ | 8 |
| 6 | 3 | |
| 7 | 9 | |
| 8 | Generality’s price: Inescapable deficiencies in machine-learned programs | 1 |
| 9 | 4 | |
| 10 | Divide and Conquer Machine Learning for a Genomics Analogy Problem (Progress Report) | 2 |
| 11 | 3 | |
| 12 | 10 | |
| 13 | 1 | |
| 14 | 4 | |
| 15 | 1 | |
| 16 | 13 | |
| 17 | Refinements of inductive inference by Popperian and reliable machines | 12 |
| 18 | Proceedings of the Third Annual Workshop on Computational Learning Theory : University of Rochester, Rochester, New York, August 6-8, 1990 | 1 |
| 19 | Proceedings of the third annual workshop on Computational learning theory | 28 |
| 20 | 28 |
About John Case
John Case is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Medical Laboratory Technology, having authored 92 papers that have together received 1.4k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (59 papers), Computability, Logic, AI Algorithms (53 papers) and Algorithms and Data Compression (29 papers). The work is most often cited by research in Computational Theory and Mathematics (716 citations), Artificial Intelligence (797 citations) and Cognitive Neuroscience (446 citations). John Case has collaborated with scholars based in United States, Singapore and Germany. Frequent co-authors include Carl H. Smith, Robert T. Knight, Bradley Voytek, Kyle Q. Lepage, Adam Gazzaley, Mark Kramer, Sanjay Jain, James S. Royer, Frank Stephan and Arun Sharma. Their work appears in journals such as Journal of Neuroscience, Journal of Pharmaceutical Sciences and Journal of the ACM.
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.