Martin Pawelczyk
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Health Information Management top 5%
- Information Systems
- Civil and Structural Engineering
- Co-authors
- Gjergji KasneciKathrin SeßlerVadim BorisovTobias LeemannJohannes HaugKlaus BroelemannHamed Jalali
- Topics
- Explainable Artificial Intelligence (XAI) (2 papers)Advanced Neural Network Applications (1 paper)Machine Learning and Data Classification (1 paper)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsUncertainty in Artificial Intelligence2021 IEEE International Conference on Big Data (Big Data)
- Partner nations
- GermanyUnited States
In The Last Decade
Martin Pawelczyk
2 papers receiving 421 citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Artificial Intelligence 206
- Computer Vision and Pattern Recognition 45
- Health Information Management 33
- Information Systems 29
- Civil and Structural Engineering 28
Countries citing papers authored by Martin Pawelczyk
This map shows the geographic impact of Martin Pawelczyk'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 Martin Pawelczyk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Pawelczyk more than expected).
Fields of papers citing papers by Martin Pawelczyk
This network shows the impact of papers produced by Martin Pawelczyk. 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 Martin Pawelczyk. The network helps show where Martin Pawelczyk may publish in the future.
Co-authorship network of co-authors of Martin Pawelczyk
This figure shows the co-authorship network connecting the top 25 collaborators of Martin Pawelczyk. A scholar is included among the top collaborators of Martin Pawelczyk 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 Martin Pawelczyk. Martin Pawelczyk is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Deep Neural Networks and Tabular Data: A Surveybreakdown → | 428 |
| 2 | 0 | |
| 3 | On Counterfactual Explanations under Predictive Multiplicity. | 4 |
About Martin Pawelczyk
Martin Pawelczyk is a scholar working on Statistics and Probability, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 3 papers that have together received 432 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (2 papers), Advanced Neural Network Applications (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Health Informatics (12 citations), Health Information Management (33 citations) and Artificial Intelligence (206 citations). Martin Pawelczyk has collaborated with scholars based in Germany and United States. Frequent co-authors include Gjergji Kasneci, Kathrin Seßler, Vadim Borisov, Tobias Leemann, Johannes Haug, Klaus Broelemann and Hamed Jalali. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Uncertainty in Artificial Intelligence and 2021 IEEE International Conference on Big Data (Big Data).
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.