Georgii Valuev
Impact in
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- Advanced Neural Network Applications
- Artificial Intelligence top 10%
- Anomaly Detection Techniques and Applications
- Neural Networks and Applications
- AI in cancer detection
- Cryptography and Data Security
Papers in
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- Cryptography and Residue Arithmetic 8
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- Advanced Neural Network Applications 3
- Image and Signal Denoising Methods 2
- Co-authors
- Pavel Lyakhov (10 shared papers)Maria Valueva (13 shared papers)Nikolay Nagornov (6 shared papers)N.I. Chervyakov (6 shared papers)Dmitrii Kaplun (4 shared papers)Maxim Deryabin (2 shared papers)Mikhail Babenko (5 shared papers)Andrei Tchernykh (5 shared papers)
In The Last Decade
Georgii Valuev
12 papers receiving 433 citations
Georgii Valuev's Hit Papers
Peers
Comparison fields: 5 of 120
- Computer Vision and Pattern Recognition 109
- Artificial Intelligence 147
- Media Technology 31
- Signal Processing 38
- Information Systems 63
Countries citing papers authored by Georgii Valuev
This map shows the geographic impact of Georgii Valuev'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 Georgii Valuev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Georgii Valuev more than expected).
Fields of papers citing papers by Georgii Valuev
This network shows the impact of papers produced by Georgii Valuev. 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 Georgii Valuev. The network helps show where Georgii Valuev may publish in the future.
Co-authors
The 12 scholars most cited alongside Georgii Valuev, 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 | Application of the residue number system to reduce hardware costs of the convolutional neural network implementation Hit paper breakdown → | 2020 | 365 |
| 2 | 2020 | 19 | |
| 3 | 2020 | 18 | |
| 4 | 2020 | 13 | |
| 5 | 2019 | 10 | |
| 6 | 2018 | 8 | |
| 7 | 2019 | 7 | |
| 8 | 2023 | 6 | |
| 9 | 2021 | 5 | |
| 10 | 2023 | 2 | |
| 11 | 2021 | 2 | |
| 12 | 2022 | 1 | |
| 13 | 2022 | 1 | |
| 14 | Area-Efficient FPGA Implementation of Minimalistic Convolutional Neural Network Using Residue Number System | 2018 | 0 |
| 15 | 2023 | 0 |
About Georgii Valuev
Georgii Valuev is a scholar working on Information Systems, Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering and Computational Theory and Mathematics, having authored 15 papers that have together received 457 indexed citations. Recurring topics across this work include Cryptography and Residue Arithmetic (8 papers), Advanced Data Processing Techniques (4 papers), Brain Tumor Detection and Classification (3 papers), Advanced Neural Network Applications (3 papers), Numerical Methods and Algorithms (2 papers), Image and Signal Denoising Methods (2 papers), Advanced Data Storage Technologies (2 papers) and Cryptographic Implementations and Security (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (109 citations), Artificial Intelligence (147 citations), Media Technology (31 citations), Signal Processing (38 citations) and Information Systems (63 citations). Georgii Valuev has collaborated with scholars based in Russia, Mexico and Germany. Frequent co-authors include Pavel Lyakhov, Maria Valueva, Nikolay Nagornov, N.I. Chervyakov, Dmitrii Kaplun, Maxim Deryabin, Mikhail Babenko, Andrei Tchernykh, Arutyun Avetisyan and Tatiana Ermakova. Their work appears in journals such as IEEE Access, Mathematics and Computers in Simulation, Big Data and Cognitive Computing, Computer Optics and Electronics.
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.