Lukas Mauch

645 total citations
18 papers, 411 citations indexed

About

Lukas Mauch is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Lukas Mauch has authored 18 papers receiving a total of 411 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Lukas Mauch's work include Advanced Neural Network Applications (8 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Medical Imaging Techniques and Applications (3 papers). Lukas Mauch is often cited by papers focused on Advanced Neural Network Applications (8 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Medical Imaging Techniques and Applications (3 papers). Lukas Mauch collaborates with scholars based in Germany, United States and Taiwan. Lukas Mauch's co-authors include Bin Yang, M. Reuter, Fabian Bamberg, Petros Martirosian, Fritz Schick, Thomas Küstner, Sergios Gatidis, Konstantin Nikolaou, Fabien Cardinaux and Stefan Uhlich and has published in prestigious journals such as Journal of the Franklin Institute, IEEE Journal of Selected Topics in Signal Processing and Magnetic Resonance Materials in Physics Biology and Medicine.

In The Last Decade

Lukas Mauch

17 papers receiving 401 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lukas Mauch Germany 8 201 98 80 78 66 18 411
Zhi Cai China 10 134 0.7× 24 0.2× 12 0.1× 20 0.3× 19 0.3× 57 381
Zeynep Garip Türkiye 10 40 0.2× 66 0.7× 6 0.1× 140 1.8× 44 0.7× 40 331
Muhammad Bilal Qureshi Pakistan 11 270 1.3× 16 0.2× 16 0.2× 62 0.8× 30 0.5× 36 405
K. Balasubramanian Türkiye 8 83 0.4× 165 1.7× 11 0.1× 67 0.9× 47 0.7× 50 347
Ling Zhu China 13 190 0.9× 59 0.6× 6 0.1× 73 0.9× 82 1.2× 75 410
K. Porkumaran India 9 127 0.6× 64 0.7× 12 0.1× 47 0.6× 108 1.6× 61 393
Musa Yılmaz Türkiye 10 150 0.7× 19 0.2× 6 0.1× 129 1.7× 150 2.3× 46 331
Francisco Pérez-Hernández Spain 6 105 0.5× 194 2.0× 18 0.2× 178 2.3× 5 0.1× 6 377
Baihong Jin United States 9 99 0.5× 24 0.2× 64 0.8× 45 0.6× 13 0.2× 17 250

Countries citing papers authored by Lukas Mauch

Since Specialization
Citations

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

Fields of papers citing papers by Lukas Mauch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lukas Mauch

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

All Works

18 of 18 papers shown
1.
Uhlich, Stefan, et al.. (2025). Schemato – An LLM for Netlist-to-Schematic Conversion. 1–7.
2.
Gripon, Vincent, et al.. (2023). A Statistical Model for Predicting Generalization in Few-Shot Classification. SPIRE - Sciences Po Institutional REpository. 1260–1264. 2 indexed citations
3.
Cardinaux, Fabien, et al.. (2020). Iteratively Training Look-Up Tables for Network Quantization. IEEE Journal of Selected Topics in Signal Processing. 14(4). 860–870. 11 indexed citations
4.
Uhlich, Stefan, et al.. (2019). Differentiable Quantization of Deep Neural Networks.. arXiv (Cornell University). 15 indexed citations
5.
6.
Uhlich, Stefan, et al.. (2019). Mixed Precision DNNs: All you need is a good parametrization. arXiv (Cornell University). 17 indexed citations
7.
Küstner, Thomas, Lukas Mauch, Petros Martirosian, et al.. (2018). Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging. 995–999. 4 indexed citations
8.
Mauch, Lukas, et al.. (2018). Automated Detection of Solar Cell Defects with Deep Learning. 2035–2039. 66 indexed citations
9.
Wang, Chunlai, et al.. (2018). On the contextual aspects of using deep convolutional neural network for semantic image segmentation. Journal of Electronic Imaging. 27(5). 1–1. 2 indexed citations
10.
Mauch, Lukas, Petros Martirosian, Fabian Bamberg, et al.. (2018). Graphical User Interface for Medical Deep Learning - Application to Magnetic Resonance Imaging. 838–847. 2 indexed citations
11.
Mauch, Lukas & Bin Yang. (2018). Least-Squares Based Layerwise Pruning Of Convolutional Neural Networks. abs 1306 543. 60–64. 2 indexed citations
12.
Küstner, Thomas, Lukas Mauch, Petros Martirosian, et al.. (2017). Automated reference-free detection of motion artifacts in magnetic resonance images. Magnetic Resonance Materials in Physics Biology and Medicine. 31(2). 243–256. 70 indexed citations
13.
14.
Mauch, Lukas, Chunlai Wang, & Bin Yang. (2017). Subset selection for visualization of relevant image fractions for deep learning based semantic image segmentation. Journal of the Franklin Institute. 355(4). 1931–1944. 5 indexed citations
15.
Mauch, Lukas & Bin Yang. (2017). A novel layerwise pruning method for model reduction of fully connected deep neural networks. 2382–2386. 8 indexed citations
16.
Mauch, Lukas & Bin Yang. (2016). A novel DNN-HMM-based approach for extracting single loads from aggregate power signals. 2384–2388. 46 indexed citations
17.
Wang, Chunlai, Lukas Mauch, Ze Guo, & Bin Yang. (2016). On semantic image segmentation using deep convolutional neural network with shortcuts and easy class extension. 30. 1–6. 6 indexed citations
18.
Mauch, Lukas & Bin Yang. (2015). A new approach for supervised power disaggregation by using a deep recurrent LSTM network. 63–67. 150 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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