A. Sherstinsky
Impact in
- Artificial Intelligence top 1%
- Anomaly Detection Techniques and Applications
- Neural Networks and Applications
- Topic Modeling
- Signal Processing top 2%
Papers in
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- Neural Networks and Applications 5
-
- Advanced Vision and Imaging 2
- Image and Signal Denoising Methods 2
- Image Enhancement Techniques 1
- Co-authors
- Rosalind W. Picard (7 shared papers)C.G. Sodini (1 shared paper)R.W. Brodersen (1 shared paper)Brian Richards (1 shared paper)
- Journals
- Physica D Nonlinear Phenomena (1 paper)IEEE Transactions on Image Processing (1 paper)IEEE Transactions on Circuits and Systems (1 paper)Proceedings - International Conference on Image Processing (1 paper)IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications (1 paper)
- Partner nations
- United States
In The Last Decade
A. Sherstinsky
9 papers receiving 3.7k citations
A. Sherstinsky's Hit Papers
Peers
Comparison fields: 5 of 183
- Artificial Intelligence 1.2k
- Signal Processing 328
- Computer Vision and Pattern Recognition 499
- Environmental Engineering 324
- Management Science and Operations Research 247
Countries citing papers authored by A. Sherstinsky
This map shows the geographic impact of A. Sherstinsky'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 A. Sherstinsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Sherstinsky more than expected).
Fields of papers citing papers by A. Sherstinsky
This network shows the impact of papers produced by A. Sherstinsky. 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 A. Sherstinsky. The network helps show where A. Sherstinsky may publish in the future.
Co-authors
The 4 scholars most cited alongside A. Sherstinsky, 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 | Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network Hit paper breakdown → | 2020 | 3657 |
| 2 | 1996 | 83 | |
| 3 | 2002 | 20 | |
| 4 | 1996 | 14 | |
| 5 | 1990 | 7 | |
| 6 | 2002 | 7 | |
| 7 | 2002 | 2 | |
| 8 | 1998 | 2 | |
| 9 | 2002 | 1 | |
| 10 | 2005 | 0 |
About A. Sherstinsky
A. Sherstinsky is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computer Networks and Communications and Statistical and Nonlinear Physics, having authored 10 papers that have together received 3.8k indexed citations. Recurring topics across this work include Neural Networks and Applications (5 papers), Advanced Vision and Imaging (2 papers), Image and Signal Denoising Methods (2 papers), Neural Networks Stability and Synchronization (2 papers), Visual perception and processing mechanisms (1 paper), Color Science and Applications (1 paper), Color perception and design (1 paper) and Image Enhancement Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (1.2k citations), Signal Processing (328 citations), Computer Vision and Pattern Recognition (499 citations), Environmental Engineering (324 citations) and Management Science and Operations Research (247 citations). A. Sherstinsky has collaborated with scholars based in United States. Frequent co-authors include Rosalind W. Picard, C.G. Sodini, R.W. Brodersen and Brian Richards. Their work appears in journals such as Physica D Nonlinear Phenomena, IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems, Proceedings - International Conference on Image Processing and IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications.
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.