Igor T. Podolak
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Molecular Biology
- Cognitive Neuroscience
- Co-authors
- Tomasz DanelSeong‐Whan LeeSabina PodlewskaJacek TaborDominika DudekTadeusz MarekMagdalena FąfrowiczAdrian Andrzej Chrobak
- Topics
- Neural Networks and Applications (6 papers)Machine Learning and Data Classification (4 papers)Fuzzy Logic and Control Systems (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputational Theory and MathematicsArtificial Intelligence
- Partner nations
- PolandSouth KoreaUnited States
In The Last Decade
Igor T. Podolak
25 papers receiving 180 citations
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 60
- Computer Vision and Pattern Recognition 57
- Computational Theory and Mathematics 31
- Molecular Biology 26
- Cognitive Neuroscience 26
Countries citing papers authored by Igor T. Podolak
This map shows the geographic impact of Igor T. Podolak'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 Igor T. Podolak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Igor T. Podolak more than expected).
Fields of papers citing papers by Igor T. Podolak
This network shows the impact of papers produced by Igor T. Podolak. 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 Igor T. Podolak. The network helps show where Igor T. Podolak may publish in the future.
Co-authorship network of co-authors of Igor T. Podolak
This figure shows the co-authorship network connecting the top 25 collaborators of Igor T. Podolak. A scholar is included among the top collaborators of Igor T. Podolak 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 Igor T. Podolak. Igor T. Podolak is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 14 | |
| 2 | 2 | |
| 3 | 7 | |
| 4 | 2 | |
| 5 | 29 | |
| 6 | 2 | |
| 7 | 8 | |
| 8 | 21 | |
| 9 | 1 | |
| 10 | 8 | |
| 11 | Interpolation in generative models. | 1 |
| 12 | Distribution-Interpolation Trade off in Generative Models. | 3 |
| 13 | 5 | |
| 14 | 1 | |
| 15 | 6 | |
| 16 | 5 | |
| 17 | DISCOVERING STRUCTURE IN GEOGRAPHICAL METADATA | 4 |
| 18 | 1 | |
| 19 | 8 | |
| 20 | Phonematic translation of Polish texts by the neural network | 1 |
About Igor T. Podolak
Igor T. Podolak is a scholar working on Computational Theory and Mathematics, Discrete Mathematics and Combinatorics and Artificial Intelligence, having authored 25 papers that have together received 193 indexed citations. Recurring topics across this work include Neural Networks and Applications (6 papers), Machine Learning and Data Classification (4 papers) and Fuzzy Logic and Control Systems (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (57 citations), Computational Theory and Mathematics (31 citations) and Artificial Intelligence (60 citations). Igor T. Podolak has collaborated with scholars based in Poland, South Korea and United States. Frequent co-authors include Tomasz Danel, Seong‐Whan Lee, Sabina Podlewska, Jacek Tabor, Dominika Dudek, Tadeusz Marek, Magdalena Fąfrowicz, Adrian Andrzej Chrobak, Łukasz Struski and Michał Węgrzyn. Their work appears in journals such as Scientific Reports, Expert Systems with Applications and Computer Physics Communications.
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.