Alexander Gepperth

1.4k total citations
46 papers, 415 citations indexed

About

Alexander Gepperth is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Alexander Gepperth has authored 46 papers receiving a total of 415 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 22 papers in Computer Vision and Pattern Recognition and 9 papers in Cognitive Neuroscience. Recurrent topics in Alexander Gepperth's work include Video Surveillance and Tracking Methods (10 papers), Domain Adaptation and Few-Shot Learning (9 papers) and Advanced Neural Network Applications (8 papers). Alexander Gepperth is often cited by papers focused on Video Surveillance and Tracking Methods (10 papers), Domain Adaptation and Few-Shot Learning (9 papers) and Advanced Neural Network Applications (8 papers). Alexander Gepperth collaborates with scholars based in Germany, France and Japan. Alexander Gepperth's co-authors include M. Ortíz, Jannik Fritsch, Franz Kümmert, Johann Edelbrunner, Stefan Roth, Christian Goerick, Uwe Handmann, Bernd Heisele, Marcus Kleinehagenbrock and Agnes Hotz‐Wagenblatt and has published in prestigious journals such as BMC Bioinformatics, Neurocomputing and Neural Networks.

In The Last Decade

Alexander Gepperth

42 papers receiving 400 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexander Gepperth Germany 11 205 191 85 65 31 46 415
Paul Watta United States 13 217 1.1× 189 1.0× 63 0.7× 48 0.7× 40 1.3× 51 483
Naiwala P. Chandrasiri Japan 10 78 0.4× 100 0.5× 104 1.2× 20 0.3× 31 1.0× 36 335
Unmesh Kurup United States 7 118 0.6× 97 0.5× 94 1.1× 53 0.8× 42 1.4× 25 337
Shiyang Yan China 13 197 1.0× 339 1.8× 37 0.4× 61 0.9× 18 0.6× 24 472
Alexandrina Rogozan France 13 150 0.7× 277 1.5× 55 0.6× 14 0.2× 14 0.5× 46 522
Esteban J. Palomo Spain 13 145 0.7× 207 1.1× 19 0.2× 42 0.6× 27 0.9× 47 430
Joel Janai Germany 5 128 0.6× 261 1.4× 107 1.3× 23 0.4× 24 0.8× 5 449
Jagannadan Varadarajan Singapore 11 232 1.1× 324 1.7× 22 0.3× 37 0.6× 13 0.4× 28 472
Yassin Kortli Tunisia 7 79 0.4× 366 1.9× 91 1.1× 34 0.5× 17 0.5× 12 523

Countries citing papers authored by Alexander Gepperth

Since Specialization
Citations

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

Fields of papers citing papers by Alexander Gepperth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexander Gepperth

This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Gepperth. A scholar is included among the top collaborators of Alexander Gepperth 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 Alexander Gepperth. Alexander Gepperth 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.
Cossu, Andrea, et al.. (2025). Don't drift away: Advances and Applications of Streaming and Continual Learning. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 35–44.
2.
Gepperth, Alexander, et al.. (2024). An analysis of best-practice strategies for replay and rehearsal in continual learning. 4196–4204. 1 indexed citations
3.
Gepperth, Alexander. (2022). Large-scale gradient-based training of Mixtures of Factor Analyzers. 2022 International Joint Conference on Neural Networks (IJCNN). 1–6. 1 indexed citations
4.
Gepperth, Alexander, et al.. (2021). Gradient-Based Training of Gaussian Mixture Models for High-Dimensional Streaming Data. Neural Processing Letters. 53(6). 4331–4348. 8 indexed citations
5.
Gepperth, Alexander, et al.. (2021). Multi-Pronged Safe Bayesian Optimization for High Dimensions. 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 33. 1966–1973. 1 indexed citations
6.
Gepperth, Alexander, et al.. (2020). A Survey of Machine Learning applied to Computer Networks.. The European Symposium on Artificial Neural Networks. 241–250. 2 indexed citations
7.
Gepperth, Alexander, et al.. (2020). SASBO: Self-Adapting Safe Bayesian Optimization. 37. 220–225. 3 indexed citations
8.
Gepperth, Alexander, et al.. (2019). A comprehensive, application-oriented study of catastrophic forgetting in DNNs. arXiv (Cornell University). 34 indexed citations
9.
Gepperth, Alexander, et al.. (2019). Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic. 18 indexed citations
10.
Gepperth, Alexander, et al.. (2018). Incremental learning with deep neural networks using a test-time oracle.. The European Symposium on Artificial Neural Networks. 3 indexed citations
11.
Gepperth, Alexander, et al.. (2016). Towards incremental deep learning: multi-level change detection in a hierarchical visual recognition architecture.. The European Symposium on Artificial Neural Networks. 1 indexed citations
12.
Gepperth, Alexander, et al.. (2016). A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems. Cognitive Computation. 8(5). 924–934. 91 indexed citations
13.
Gepperth, Alexander, M. Ortíz, & Bernd Heisele. (2013). Real-time pedestrian detection and pose classification on a GPU. 348–353. 11 indexed citations
14.
Gepperth, Alexander. (2012). Efficient online bootstrapping of sensory representations. Neural Networks. 41. 39–50. 5 indexed citations
15.
Gepperth, Alexander, et al.. (2012). The contribution of context information: A case study of object recognition in an intelligent car. Neurocomputing. 94. 77–86. 8 indexed citations
16.
Ortíz, M., Jannik Fritsch, Franz Kümmert, & Alexander Gepperth. (2011). Behavior prediction at multiple time-scales in inner-city scenarios. 1068–1073. 37 indexed citations
17.
Gepperth, Alexander, Jannik Fritsch, & Christian Goerick. (2008). Computationally Efficient Neural Field Dynamics. The European Symposium on Artificial Neural Networks. 179–184. 1 indexed citations
18.
Mersch, Britta, Alexander Gepperth, Sándor Suhai, & Agnes Hotz‐Wagenblatt. (2008). Automatic detection of exonic splicing enhancers (ESEs) using SVMs. BMC Bioinformatics. 9(1). 369–369. 8 indexed citations
19.
Gepperth, Alexander. (2006). Visual object classification by sparse convolutional neural networks. The European Symposium on Artificial Neural Networks. 179–184. 1 indexed citations
20.
Gepperth, Alexander & Stefan Roth. (2005). Applications of multi-objective structure optimization.. The European Symposium on Artificial Neural Networks. 279–284. 2 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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