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Researcher · Fujitsu (China) · China

Roman Vainshtein

Roman Vainshtein publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, on topics such as Topic Modeling, Machine Learning and Data Classification and Explainable Artificial Intelligence (XAI).

ORCID 0000-0001-7033-5373 OpenAlex
World rank
#3,208,796
of 7,710,434 ranked researchers
Rank in China
#504,371
of 978,568
Works
28
Citations
99
Citations per work
3.5
h-index
5
i10-index 3Top 82% in Artificial Intelligence
Roman Vainshtein

What does Roman Vainshtein research?

Roman Vainshtein's work is mostly in Computer Science (79%), Social Sciences (5%), Psychology (4%).

Physical Sciences: 22 works · 78.9% of their workComputer Science: 22 works · 78.9% of their workArtificial Intelligence: 14 works · 50.6% of their workComputer Vision and Pattern Recognition: 3 works · 11.8% of their workInformation Systems: 3 works · 10.6% of their workComputer Networks and Communications: 2 works · 5.9% of their workEverything else: 4 works · 12.9% of their workSocial Sciences: 2 works · 8.2% of their workSocial Sciences: 1 works · 4.7% of their workSociology and Political Science: 1 works · 4.7% of their workPsychology: 1 works · 3.5% of their workSocial Psychology: 1 works · 3.5% of their workTHEIR WORKRoman Vainshtein28works
Physical Sciences79%Social Sciences8%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science2278.9%
  2. Everything else412.9%
    1. Social Sciences14.7%
    2. Psychology13.5%
  1. 1Artificial Intelligence51% of their works
  2. 2Computer Vision and Pattern Recognition12% of their works
  3. 3Information Systems11% of their works
  4. 4Computer Networks and Communications6% of their works
  5. 5Sociology and Political Science5% of their works
  6. 6Social Psychology4% of their works

Research topics

Which keywords describe Roman Vainshtein's research?

The keywords of their largest research topics: Neural Networks, Deep Neural Networks, Deep Learning, Meta-Learning, Hyperparameter Optimization, Machine Learning, Machine Translation and Noisy Labels.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 23 words, with their numbers
  1. Neural Networks123 topics
  2. Deep Neural Networks112 topics
  3. Deep Learning93 topics
  4. Meta-Learning92 topics
  5. Hyperparameter Optimization61 topic
  6. Machine Learning62 topics
  7. Machine Translation61 topic
  8. Noisy Labels61 topic
  9. Semi-Supervised Learning62 topics
  10. Unsupervised Learning62 topics
  11. Word Representation61 topic
  12. Interpretable Models51 topic
  13. Machine Learning Interpretability51 topic
  14. SQL Injection41 topic
  15. Static Analysis41 topic
  16. Active Learning31 topic
  17. Adversarial Examples31 topic
  18. Domain Adaptation31 topic
  19. Gaussian Processes31 topic
  20. Generative Adversarial Networks31 topic
  21. Image Synthesis31 topic
  22. Robustness31 topic
  23. Transfer Learning31 topic

Where does Roman Vainshtein work?

Roman Vainshtein's main affiliation in the publication record is Fujitsu (China), China.

How many publications and citations does Roman Vainshtein have?

OpenAlex counts 28 works and 99 citations for Roman Vainshtein, an h-index of 5, ranking #3,208,796 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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