Guy Revach

949 total citations · 1 hit paper
25 papers, 513 citations indexed

About

Guy Revach is a scholar working on Artificial Intelligence, Signal Processing and Control and Systems Engineering. According to data from OpenAlex, Guy Revach has authored 25 papers receiving a total of 513 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 9 papers in Signal Processing and 5 papers in Control and Systems Engineering. Recurrent topics in Guy Revach's work include Target Tracking and Data Fusion in Sensor Networks (15 papers), Neural Networks and Applications (5 papers) and Time Series Analysis and Forecasting (5 papers). Guy Revach is often cited by papers focused on Target Tracking and Data Fusion in Sensor Networks (15 papers), Neural Networks and Applications (5 papers) and Time Series Analysis and Forecasting (5 papers). Guy Revach collaborates with scholars based in Israel, Netherlands and Switzerland. Guy Revach's co-authors include Nir Shlezinger, Ruud J. G. van Sloun, Yonina C. Eldar, Xiaoyong Ni, Tirza Routtenberg, Itzik Klein, Rik Vullings, Kobi Cohen, Jindřich Duník and Oliver Hüsser and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Vehicular Technology and IEEE Signal Processing Magazine.

In The Last Decade

Guy Revach

23 papers receiving 503 citations

Hit Papers

KalmanNet: Neural Network Aided Kalman Filtering for Part... 2022 2026 2023 2024 2022 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
    2022 238 citations Guy Revach, Nir Shlezinger et al. IEEE Transactions on Signal Processing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guy Revach Israel 12 228 119 112 105 104 25 513
Hongguang Ma China 11 129 0.6× 108 0.9× 201 1.8× 77 0.7× 105 1.0× 56 499
Dongliang Peng China 10 136 0.6× 44 0.4× 144 1.3× 55 0.5× 74 0.7× 92 409
B.F. La Scala Australia 13 366 1.6× 77 0.6× 195 1.7× 211 2.0× 109 1.0× 25 638
Donka Angelova Bulgaria 12 353 1.5× 50 0.4× 183 1.6× 88 0.8× 149 1.4× 38 534
Yunfei Guo China 11 195 0.9× 43 0.4× 115 1.0× 53 0.5× 50 0.5× 71 341
Audrey Giremus France 13 304 1.3× 40 0.3× 286 2.6× 52 0.5× 182 1.8× 45 561
Lin Gao China 15 400 1.8× 43 0.4× 246 2.2× 107 1.0× 248 2.4× 83 835
Marcelo G. S. Bruno Brazil 13 400 1.8× 68 0.6× 141 1.3× 67 0.6× 128 1.2× 69 528
Dietrich Fränken Germany 11 594 2.6× 114 1.0× 298 2.7× 89 0.8× 158 1.5× 36 802
Carsten Fritsche Sweden 14 319 1.4× 99 0.8× 180 1.6× 73 0.7× 512 4.9× 60 745

Countries citing papers authored by Guy Revach

Since Specialization
Citations

This map shows the geographic impact of Guy Revach'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 Guy Revach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guy Revach more than expected).

Fields of papers citing papers by Guy Revach

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guy Revach. 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 Guy Revach. The network helps show where Guy Revach may publish in the future.

Co-authorship network of co-authors of Guy Revach

This figure shows the co-authorship network connecting the top 25 collaborators of Guy Revach. A scholar is included among the top collaborators of Guy Revach 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 Guy Revach. Guy Revach is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Shlezinger, Nir, Guy Revach, Saikat Chatterjee, et al.. (2025). Artificial Intelligence-Aided Kalman Filters: AI-Augmented Designs for Kalman-Type Algorithms. IEEE Signal Processing Magazine. 42(3). 52–76.
2.
Revach, Guy, et al.. (2024). Neural Augmented Kalman Filtering With Bollinger Bands for Pairs Trading. IEEE Transactions on Signal Processing. 72. 1974–1988. 3 indexed citations
3.
Revach, Guy, et al.. (2024). Outlier-Insensitive Kalman Filtering: Theory and Applications. IEEE Sensors Journal. 24(23). 39467–39477. 2 indexed citations
4.
Revach, Guy, et al.. (2024). HKF: Hierarchical Kalman Filtering With Online Learned Evolution Priors for Adaptive ECG Denoising. IEEE Transactions on Signal Processing. 72. 3990–4006. 3 indexed citations
5.
Revach, Guy, et al.. (2024). SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation. IEEE Transactions on Vehicular Technology. 74(3). 4962–4976. 19 indexed citations
6.
Revach, Guy, et al.. (2024). Uncertainty Quantification in Deep Learning Based Kalman Filters. 13121–13125. 5 indexed citations
7.
Ni, Xiaoyong, Guy Revach, & Nir Shlezinger. (2024). Adaptive Kalmannet: Data-Driven Kalman Filter with Fast Adaptation. 5970–5974. 13 indexed citations
8.
Revach, Guy, Xiaoyong Ni, Nir Shlezinger, Ruud J. G. van Sloun, & Yonina C. Eldar. (2023). RTSNet: Learning to Smooth in Partially Known State-Space Models. IEEE Transactions on Signal Processing. 71. 4441–4456. 10 indexed citations
9.
Revach, Guy, et al.. (2023). Latent-KalmanNet: Learned Kalman Filtering for Tracking From High-Dimensional Signals. IEEE Transactions on Signal Processing. 72. 352–367. 16 indexed citations
10.
Revach, Guy, et al.. (2023). DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm. IEEE Transactions on Vehicular Technology. 73(2). 2771–2785. 41 indexed citations
11.
Revach, Guy, et al.. (2023). Anomaly Search Over Discrete Composite Hypotheses in Hierarchical Statistical Models. IEEE Transactions on Signal Processing. 71. 202–217. 8 indexed citations
12.
Revach, Guy, et al.. (2023). Learned Kalman Filtering in Latent Space with High-Dimensional Data. 34. 1–5. 5 indexed citations
13.
Revach, Guy, et al.. (2023). Kalmanbot: Kalmannet-Aided Bollinger Bands for Pairs Trading. 47. 1–5. 1 indexed citations
14.
Revach, Guy, et al.. (2023). Deep Root Music Algorithm for Data-Driven Doa Estimation. TU/e Research Portal. 1–5. 19 indexed citations
15.
Revach, Guy, et al.. (2022). KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics. IEEE Transactions on Signal Processing. 70. 1532–1547. 238 indexed citations breakdown →
16.
Revach, Guy, et al.. (2022). Unsupervised Learned Kalman Filtering. 2022 30th European Signal Processing Conference (EUSIPCO). 1571–1575. 25 indexed citations
17.
Ni, Xiaoyong, Guy Revach, Nir Shlezinger, Ruud J. G. van Sloun, & Yonina C. Eldar. (2022). RTSNet: Deep Learning Aided Kalman Smoothing. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 5902–5906. 13 indexed citations
18.
Revach, Guy, et al.. (2022). Deep Augmented Music Algorithm for Data-Driven Doa Estimation. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 3598–3602. 30 indexed citations
19.
Revach, Guy, et al.. (2021). On the Influence of Home Appliances on the Smartphone’s Inertial Sensors. 2021 IEEE Sensors. 18. 1–4.
20.
Revach, Guy, et al.. (2021). Data-Driven Kalman-Based Velocity Estimation for Autonomous Racing. TU/e Research Portal. 1–5. 11 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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