Evgeniy Bart

942 citations
27 papers · 572 · h-index 11

Impact in

Papers in

Evgeniy Bart

26 papers receiving 526 citations

Peers

Evgeniy Bart
Comparison fields: 5 of 82
  • Computational Mathematics 20
  • Computer Vision and Pattern Recognition 288
  • Artificial Intelligence 296
  • Signal Processing 62
  • Cognitive Neuroscience 83
Replace Akisato Kimura with:
Akisato Kimura Japan
Bokai Cao United States
Yuke Wang United States
Youngsok Kim South Korea
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Citations per field
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Citations per year

Countries citing papers authored by Evgeniy Bart

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

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.

Border = papers with Evgeniy Bart Line = papers co-authored together Evgeniy Bart links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2005116
2 201376
3 200870
4
Multi-HDP: a non parametric Bayesian model for tensor factorization
200856
5 199456
6 200439
7 200528
8 200522
9 200822
10 201011
11 201110
12 20089
13 20109
14 20057
15 20126
16 20046
17 20115
18 20184
19 20184
20 20124

About Evgeniy Bart

Evgeniy Bart is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience, Signal Processing and Media Technology, 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), Image Processing Techniques and Applications (3 papers), Handwritten Text Recognition Techniques (3 papers), Advanced Vision and Imaging (3 papers), Neural dynamics and brain function (3 papers), Data Management and Algorithms (2 papers) and Cell Image Analysis Techniques (2 papers). The work is most often cited by research in Computational Mathematics (20 citations), Computer Vision and Pattern Recognition (288 citations), Artificial Intelligence (296 citations), Signal Processing (62 citations) and Cognitive Neuroscience (83 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, Oliver Brdiczka, Juan Liu, 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.

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