A. Sherstinsky

5.4k total citations · 1 hit paper
10 papers, 3.3k citations indexed

About

A. Sherstinsky is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, A. Sherstinsky has authored 10 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Signal Processing. Recurrent topics in A. Sherstinsky's work include Neural Networks and Applications (5 papers), Image and Signal Denoising Methods (2 papers) and Advanced Vision and Imaging (2 papers). A. Sherstinsky is often cited by papers focused on Neural Networks and Applications (5 papers), Image and Signal Denoising Methods (2 papers) and Advanced Vision and Imaging (2 papers). A. Sherstinsky collaborates with scholars based in United States. A. Sherstinsky's co-authors include Rosalind W. Picard, C.G. Sodini, Brian Richards and R.W. Brodersen and has published in prestigious journals such as IEEE Transactions on Image Processing, Physica D Nonlinear Phenomena and IEEE Transactions on Circuits and Systems.

In The Last Decade

A. Sherstinsky

8 papers receiving 3.2k citations

Hit Papers

Fundamentals of Recurrent Neural Network (RNN) and Long S... 2020 2026 2022 2024 2020 1000 2.0k 3.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
A. Sherstinsky United States 6 1.1k 589 436 330 304 10 3.3k
Laurene V. Fausett United States 10 1.0k 1.0× 495 0.8× 398 0.9× 340 1.0× 268 0.9× 21 3.7k
Khalid Mohiuddin Saudi Arabia 12 1.2k 1.1× 564 1.0× 576 1.3× 317 1.0× 277 0.9× 43 3.9k
Yunqian Ma United States 15 1.0k 1.0× 487 0.8× 417 1.0× 357 1.1× 276 0.9× 46 3.4k
Abhishek Jain United States 13 1.2k 1.1× 559 0.9× 386 0.9× 309 0.9× 275 0.9× 56 3.8k
Martin F. Møller Denmark 5 1.1k 1.1× 519 0.9× 507 1.2× 353 1.1× 262 0.9× 6 3.5k
Heinrich Braun Germany 11 1.1k 1.1× 529 0.9× 534 1.2× 381 1.2× 157 0.5× 27 3.1k
Fred Cummins Ireland 21 1.7k 1.6× 674 1.1× 507 1.2× 322 1.0× 373 1.2× 81 5.0k
Jianxun Zhang China 19 979 0.9× 702 1.2× 445 1.0× 717 2.2× 312 1.0× 79 4.3k
M.H. Hassoun United States 15 1.1k 1.1× 511 0.9× 379 0.9× 337 1.0× 146 0.5× 74 2.7k

Countries citing papers authored by A. Sherstinsky

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of A. Sherstinsky

This figure shows the co-authorship network connecting the top 25 collaborators of A. Sherstinsky. A scholar is included among the top collaborators of A. Sherstinsky based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with A. Sherstinsky. A. Sherstinsky is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Sherstinsky, A.. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D Nonlinear Phenomena. 404. 132306–132306. 3228 indexed citations breakdown →
2.
Richards, Brian, A. Sherstinsky, & R.W. Brodersen. (2005). A parameterized VLSI video-rate histogram processor. 12. 491–494.
3.
Sherstinsky, A. & Rosalind W. Picard. (2002). Orientation-sensitive image processing with M-lattice-a novel non-linear dynamical system. 3. 152–156. 2 indexed citations
4.
Sherstinsky, A. & Rosalind W. Picard. (2002). Color halftoning with M-lattice. Proceedings - International Conference on Image Processing. 2. 335–338. 1 indexed citations
5.
Sherstinsky, A. & Rosalind W. Picard. (2002). Restoration and enhancement of fingerprint images using M-lattice-a novel nonlinear dynamical system. 2. 195–200. 14 indexed citations
6.
Sherstinsky, A. & Rosalind W. Picard. (2002). M-lattice: a novel non-linear dynamical system and its application to halftoning. ii. II/565–II/568. 6 indexed citations
7.
Sherstinsky, A. & Rosalind W. Picard. (1998). On stability and equilibria of the M-lattice. IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications. 45(4). 408–415. 1 indexed citations
8.
Sherstinsky, A. & Rosalind W. Picard. (1996). On the efficiency of the orthogonal least squares training method for radial basis function networks. IEEE Transactions on Neural Networks. 7(1). 195–200. 76 indexed citations
9.
Sherstinsky, A. & Rosalind W. Picard. (1996). M-lattice: from morphogenesis to image processing. IEEE Transactions on Image Processing. 5(7). 1137–1149. 13 indexed citations
10.
Sherstinsky, A. & C.G. Sodini. (1990). A programmable demodulator for oversampled analog-to-digital modulators. IEEE Transactions on Circuits and Systems. 37(9). 1092–1103. 7 indexed citations

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.

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