Jan Pfeifer
Impact in
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- Explainable Artificial Intelligence (XAI)
- Machine Learning and Data Classification
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
- Evolutionary Algorithms and Applications
Papers in
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- Machine Learning and Data Classification 2
- Bayesian Modeling and Causal Inference 1
- Adversarial Robustness in Machine Learning 1
- Neural Networks and Applications 1
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- Generative Adversarial Networks and Image Synthesis 1
- Human Pose and Action Recognition 1
- Co-authors
- Maya R. Gupta (4 shared papers)Kevin Robert Canini (3 shared papers)Andrew Cotter (3 shared papers)Seungil You (1 shared paper)Xin Ding (1 shared paper)Sebastian Bruch (1 shared paper)Florian Hintz (1 shared paper)Iris Graessler (1 shared paper)
- Journals
- Journal of Machine Learning Research (1 paper)neural information processing systems (1 paper)INCOSE International Symposium (1 paper)Neural Information Processing Systems (1 paper)Conference on Learning Theory (1 paper)
- Partner nations
- United StatesGermanySwitzerland
In The Last Decade
Jan Pfeifer
5 papers receiving 69 citations
Peers
Comparison fields: 5 of 40
- Health Informatics 3
- Artificial Intelligence 44
- Signal Processing 10
- Statistics and Probability 5
- Management Science and Operations Research 7
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-authors
The 8 scholars most cited alongside Jan Pfeifer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 37 | |
| 2 | Deep Lattice Networks and Partial Monotonic Functions | 2017 | 13 |
| 3 | Fast and Flexible Monotonic Functions with Ensembles of Lattices | 2016 | 10 |
| 4 | A Light Touch for Heavily Constrained SGD | 2016 | 6 |
| 5 | 2023 | 3 | |
| 6 | 2024 | 0 |
About Jan Pfeifer
Jan Pfeifer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Control and Systems Engineering and Computational Mechanics, having authored 6 papers that have together received 69 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (2 papers), Product Development and Customization (1 paper), Bayesian Modeling and Causal Inference (1 paper), Multi-Criteria Decision Making (1 paper), Adversarial Robustness in Machine Learning (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Neural Networks and Applications (1 paper) and Human Pose and Action Recognition (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (44 citations), Signal Processing (10 citations), Statistics and Probability (5 citations) and Management Science and Operations Research (7 citations). Jan Pfeifer has collaborated with scholars based in United States, Germany and Switzerland. Frequent co-authors include Maya R. Gupta, Kevin Robert Canini, Andrew Cotter, Seungil You, Xin Ding, Sebastian Bruch, Florian Hintz and Iris Graessler. Their work appears in journals such as Journal of Machine Learning Research, neural information processing systems, INCOSE International Symposium, Neural Information Processing Systems and Conference on Learning 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.