Maria Valueva
Impact in
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- Advanced Neural Network Applications
- Image and Signal Denoising Methods
- Artificial Intelligence top 10%
- Cryptographic Implementations and Security
- Anomaly Detection Techniques and Applications
Papers in
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- Image and Signal Denoising Methods 5
- Advanced Neural Network Applications 4
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- Cryptography and Residue Arithmetic 13
- Co-authors
- Pavel Lyakhov (23 shared papers)N.I. Chervyakov (16 shared papers)Nikolay Nagornov (9 shared papers)Georgii Valuev (13 shared papers)Dmitrii Kaplun (11 shared papers)Maxim Deryabin (2 shared papers)Mikhail Babenko (4 shared papers)Peter Boyvalenkov (2 shared papers)
In The Last Decade
Maria Valueva
22 papers receiving 484 citations
Maria Valueva's Hit Papers
Peers
Comparison fields: 5 of 120
- Computer Vision and Pattern Recognition 132
- Artificial Intelligence 162
- Media Technology 38
- Signal Processing 41
- Information Systems 80
Countries citing papers authored by Maria Valueva
This map shows the geographic impact of Maria Valueva'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 Maria Valueva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Valueva more than expected).
Fields of papers citing papers by Maria Valueva
This network shows the impact of papers produced by Maria Valueva. 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 Maria Valueva. The network helps show where Maria Valueva may publish in the future.
Co-authors
The 11 scholars most cited alongside Maria Valueva, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Application of the residue number system to reduce hardware costs of the convolutional neural network implementation Hit paper breakdown → | 2020 | 365 |
| 2 | 2017 | 20 | |
| 3 | 2020 | 19 | |
| 4 | 2020 | 18 | |
| 5 | 2020 | 13 | |
| 6 | 2019 | 10 | |
| 7 | 2018 | 8 | |
| 8 | 2017 | 7 | |
| 9 | 2019 | 7 | |
| 10 | 2020 | 6 | |
| 11 | 2020 | 5 | |
| 12 | 2022 | 5 | |
| 13 | 2021 | 5 | |
| 14 | 2016 | 4 | |
| 15 | 2020 | 4 | |
| 16 | 2020 | 3 | |
| 17 | 2022 | 3 | |
| 18 | 2019 | 2 | |
| 19 | 2021 | 2 | |
| 20 | 2022 | 1 |
About Maria Valueva
Maria Valueva is a scholar working on Computer Vision and Pattern Recognition, Information Systems, Artificial Intelligence, Computational Theory and Mathematics and Media Technology, having authored 26 papers that have together received 510 indexed citations. Recurring topics across this work include Cryptography and Residue Arithmetic (13 papers), Cryptographic Implementations and Security (6 papers), Coding theory and cryptography (5 papers), Image and Signal Denoising Methods (5 papers), Advanced Neural Network Applications (4 papers), Advanced Data Processing Techniques (4 papers), Numerical Methods and Algorithms (4 papers) and Brain Tumor Detection and Classification (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (132 citations), Artificial Intelligence (162 citations), Media Technology (38 citations), Signal Processing (41 citations) and Information Systems (80 citations). Maria Valueva has collaborated with scholars based in Russia, Bulgaria and Mexico. Frequent co-authors include Pavel Lyakhov, N.I. Chervyakov, Nikolay Nagornov, Georgii Valuev, Dmitrii Kaplun, Maxim Deryabin, Mikhail Babenko, Peter Boyvalenkov, Jorge M. Cortés-Mendoza and Aleksandr Sinitca. Their work appears in journals such as IEEE Access, Applied Sciences, Mathematics and Computers in Simulation, 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.