Jan Pfeifer
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Signal Processing
- Information Systems
- Computational Theory and Mathematics
- Co-authors
- Maya R. GuptaAndrew CotterKevin Robert CaniniXin DingSeungil YouSebastian BruchFlorian HintzIris Graessler
- Topics
- Machine Learning and Data Classification (2 papers)Systems Engineering Methodologies and Applications (1 paper)Sparse and Compressive Sensing Techniques (1 paper)
- Journals
- Journal of Machine Learning Researchneural information processing systemsNeural Information Processing Systems
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Jan Pfeifer
5 papers receiving 69 citations
Peers
Comparison fields: 5 of 40
- Artificial Intelligence 44
- Computer Vision and Pattern Recognition 11
- Signal Processing 10
- Information Systems 9
- Computational Theory and Mathematics 8
Countries citing papers authored by Jan Pfeifer
This map shows the geographic impact of Jan Pfeifer'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 Jan Pfeifer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Pfeifer more than expected).
Fields of papers citing papers by Jan Pfeifer
This network shows the impact of papers produced by Jan Pfeifer. 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 Jan Pfeifer. The network helps show where Jan Pfeifer may publish in the future.
Co-authorship network of co-authors of Jan Pfeifer
This figure shows the co-authorship network connecting the top 25 collaborators of Jan Pfeifer. A scholar is included among the top collaborators of Jan Pfeifer 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 Jan Pfeifer. Jan Pfeifer 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 | 3 | |
| 3 | Deep Lattice Networks and Partial Monotonic Functions | 13 |
| 4 | Fast and Flexible Monotonic Functions with Ensembles of Lattices | 10 |
| 5 | 37 | |
| 6 | A Light Touch for Heavily Constrained SGD | 6 |
About Jan Pfeifer
Jan Pfeifer is a scholar working on Management Science and Operations Research, Management of Technology and Innovation and Artificial Intelligence, having authored 6 papers that have together received 69 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (2 papers), Systems Engineering Methodologies and Applications (1 paper) and Sparse and Compressive Sensing Techniques (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (44 citations) and Signal Processing (10 citations). Jan Pfeifer has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Maya R. Gupta, Andrew Cotter, Kevin Robert Canini, Xin Ding, Seungil You, Sebastian Bruch, Florian Hintz and Iris Graessler. Their work appears in journals such as Journal of Machine Learning Research, neural information processing systems and Neural Information Processing Systems.
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.