Michael Figurnov
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
Papers in
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- Topic Modeling 1
- Neural Networks and Applications 1
- Advanced Text Analysis Techniques 1
- AI in cancer detection 1
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- Advanced Neural Network Applications 2
- Image Processing and 3D Reconstruction 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Dmitry Vetrov (3 shared papers)Maxwell D. Collins (1 shared paper)Jonathan Huang (1 shared paper)Ruslan Salakhutdinov (1 shared paper)Yukun Zhu (1 shared paper)Li Zhang (1 shared paper)Pushmeet Kohli (1 shared paper)О. А. Иванов (1 shared paper)
- Journals
- International Conference on Learning Representations (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- RussiaUnited States
In The Last Decade
Michael Figurnov
3 papers receiving 260 citations
Peers
Comparison fields: 5 of 42
- Computer Vision and Pattern Recognition 223
- Artificial Intelligence 158
- Hardware and Architecture 12
- Computational Mathematics 1
- Neurology 11
Countries citing papers authored by Michael Figurnov
This map shows the geographic impact of Michael Figurnov'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 Michael Figurnov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Figurnov more than expected).
Fields of papers citing papers by Michael Figurnov
This network shows the impact of papers produced by Michael Figurnov. 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 Michael Figurnov. The network helps show where Michael Figurnov may publish in the future.
Co-authors
The 9 scholars most cited alongside Michael Figurnov, 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 | 2017 | 190 | |
| 2 | 2015 | 71 | |
| 3 | Variational Autoencoder with Arbitrary Conditioning | 2019 | 12 |
| 4 | Linear combination of random forests for the Relevance Prediction Challenge | 2012 | 0 |
About Michael Figurnov
Michael Figurnov is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Electrical and Electronic Engineering and Infectious Diseases, having authored 4 papers that have together received 273 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), Topic Modeling (1 paper), Neural Networks and Applications (1 paper), Ferroelectric and Negative Capacitance Devices (1 paper), Image Processing and 3D Reconstruction (1 paper), Advanced Text Analysis Techniques (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and AI in cancer detection (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (223 citations), Artificial Intelligence (158 citations), Hardware and Architecture (12 citations), Computational Mathematics (1 citation) and Neurology (11 citations). Michael Figurnov has collaborated with scholars based in Russia and United States. Frequent co-authors include Dmitry Vetrov, Maxwell D. Collins, Jonathan Huang, Ruslan Salakhutdinov, Yukun Zhu, Li Zhang, Pushmeet Kohli, О. А. Иванов and Alexander Kirillov. Their work appears in journals such as International Conference on Learning Representations and arXiv (Cornell University).
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.