Evgeniy Bart
- Computational Mathematics top 5%
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- Advanced Image and Video Retrieval Techniques 10
- Image Retrieval and Classification Techniques 9
- Handwritten Text Recognition Techniques 3
- Advanced Vision and Imaging 3
- Artificial Intelligence top 5%
- Bayesian Methods and Mixture Models 2
- Signal Processing top 10%
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- Neural dynamics and brain function 3
- Face Recognition and Perception 2
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- Image Processing Techniques and Applications 3
- Co-authors
- Shimon UllmanMax WellingIan R. PorteousPietro PeronaJay HegdéVincent Chi‐Chung ChengJuan LiuOliver Brdiczka
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)PLoS ONE (2 papers)Current Biology (1 paper)
- Partner nations
- United StatesIsraelNetherlands
In The Last Decade
Evgeniy Bart
26 papers receiving 526 citations
Peers
Comparison fields: 5 of 82
- Computational Mathematics 20
- Computer Vision and Pattern Recognition 288
- Artificial Intelligence 296
- Signal Processing 62
- Cognitive Neuroscience 83
Countries citing papers authored by Evgeniy Bart
This map shows the geographic impact of Evgeniy Bart'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 Evgeniy Bart with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Evgeniy Bart more than expected).
Fields of papers citing papers by Evgeniy Bart
This network shows the impact of papers produced by Evgeniy Bart. 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 Evgeniy Bart. The network helps show where Evgeniy Bart may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Evgeniy Bart, 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 | 2019 | 2 | |
| 2 | 2018 | 4 | |
| 3 | 2018 | 4 | |
| 4 | 2012 | 6 | |
| 5 | 2012 | 2 | |
| 6 | 2012 | 2 | |
| 7 | 2011 | 10 | |
| 8 | 2010 | 9 | |
| 9 | Multi-HDP: a non parametric Bayesian model for tensor factorization | 2008 | 56 |
| 10 | Infinite State Bayesian Networks | 2008 | 2 |
| 11 | 2008 | 9 | |
| 12 | 2008 | 22 | |
| 13 | 2008 | 70 | |
| 14 | Infinite State Bayes-Nets for Structured Domains | 2007 | 3 |
| 15 | 2005 | 22 | |
| 16 | 2005 | 28 | |
| 17 | 2005 | 116 | |
| 18 | 2004 | 39 | |
| 19 | 2004 | 6 | |
| 20 | 1994 | 56 |
About Evgeniy Bart
Evgeniy Bart is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Biophysics, having authored 27 papers that have together received 572 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (10 papers), Image Retrieval and Classification Techniques (9 papers), Neural dynamics and brain function (3 papers), Handwritten Text Recognition Techniques (3 papers), Advanced Vision and Imaging (3 papers), Image Processing Techniques and Applications (3 papers), Bayesian Methods and Mixture Models (2 papers) and Face Recognition and Perception (2 papers). The work is most often cited by research in Computational Mathematics (20 citations), Computer Vision and Pattern Recognition (288 citations) and Artificial Intelligence (296 citations). Evgeniy Bart has collaborated with scholars based in United States, Israel and Netherlands. Frequent co-authors include Shimon Ullman, Max Welling, Ian R. Porteous, Pietro Perona, Jay Hegdé, Vincent Chi‐Chung Cheng, Juan Liu, Oliver Brdiczka, Hoda Eldardiry and John Hanley. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, PLoS ONE, Current Biology, Frontiers in Neuroscience and Journal of Computational Neuroscience.
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.