Maciej Zięba
- Artificial Intelligence top 5%
- Accounting top 5%
- Computer Vision and Pattern Recognition top 10%
- Finance top 10%
- Electrical and Electronic Engineering
- Co-authors
- Jakub M. TomczakSebastian Klaudiusz TomczakJerzy ŚwiątekManuel ArrueboAdam GonczarekPiotr KlukowskiJesús Santamarı́aMichał Walczak
- Topics
- Imbalanced Data Classification Techniques (8 papers)Financial Distress and Bankruptcy Prediction (6 papers)Generative Adversarial Networks and Image Synthesis (5 papers)
- Journals
- BioinformaticsIEEE Transactions on Pattern Analysis and Machine IntelligenceExpert Systems with Applications
- Partner nations
- PolandSpainUnited Kingdom
In The Last Decade
Maciej Zięba
36 papers receiving 816 citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 411
- Accounting 259
- Computer Vision and Pattern Recognition 129
- Finance 84
- Electrical and Electronic Engineering 76
Countries citing papers authored by Maciej Zięba
This map shows the geographic impact of Maciej Zięba'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 Maciej Zięba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maciej Zięba more than expected).
Fields of papers citing papers by Maciej Zięba
This network shows the impact of papers produced by Maciej Zięba. 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 Maciej Zięba. The network helps show where Maciej Zięba may publish in the future.
Co-authorship network of co-authors of Maciej Zięba
This figure shows the co-authorship network connecting the top 25 collaborators of Maciej Zięba. A scholar is included among the top collaborators of Maciej Zięba 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 Maciej Zięba. Maciej Zięba 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 | 4 | |
| 3 | 17 | |
| 4 | 1 | |
| 5 | 6 | |
| 6 | 3 | |
| 7 | 18 | |
| 8 | 4 | |
| 9 | UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree | 1 |
| 10 | 11 | |
| 11 | BinGAN: Learning Compact Binary Descriptors with a Regularized GAN | 21 |
| 12 | 1 | |
| 13 | Training Triplet Networks with GAN | 0 |
| 14 | 39 | |
| 15 | 10 | |
| 16 | 76 | |
| 17 | 20 | |
| 18 | 39 | |
| 19 | Analiza porównawcza wybranych technik eksploracji danych do klasyfikacji danych medycznych z brakującymi obserwacjami. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu = Research Papers of Wrocław University of Economics, 2012, Nr 242, s. 416-425 | 1 |
| 20 | 2 |
About Maciej Zięba
Maciej Zięba is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Medical Laboratory Technology, having authored 43 papers that have together received 858 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (8 papers), Financial Distress and Bankruptcy Prediction (6 papers) and Generative Adversarial Networks and Image Synthesis (5 papers). The work is most often cited by research in Accounting (259 citations), Artificial Intelligence (411 citations) and Health Information Management (59 citations). Maciej Zięba has collaborated with scholars based in Poland, Spain and United Kingdom. Frequent co-authors include Jakub M. Tomczak, Sebastian Klaudiusz Tomczak, Jerzy Świątek, Manuel Arruebo, Adam Gonczarek, Piotr Klukowski, Jesús Santamarı́a, Michał Walczak, Gema Martı́nez and Maja Pantić. Their work appears in journals such as Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence and Expert Systems with Applications.
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.