Philipp Petersen

807 total citations
16 papers, 96 citations indexed

About

Philipp Petersen is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Philipp Petersen has authored 16 papers receiving a total of 96 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Statistical and Nonlinear Physics and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Philipp Petersen's work include Neural Networks and Applications (8 papers), Model Reduction and Neural Networks (6 papers) and Image and Signal Denoising Methods (3 papers). Philipp Petersen is often cited by papers focused on Neural Networks and Applications (8 papers), Model Reduction and Neural Networks (6 papers) and Image and Signal Denoising Methods (3 papers). Philipp Petersen collaborates with scholars based in Austria, Germany and United Kingdom. Philipp Petersen's co-authors include V. S. Varadarajan, Stefano Soatto, Ganesh Sundaramoorthi, Felix Voigtlaender, Christoph Schwab, Gitta Kutyniok, Robert Calderbank, Rudolf Mathar, Holger Boche and Giuseppe Caire and has published in prestigious journals such as Neural Networks, Journal of Mathematical Analysis and Applications and The Annals of Applied Probability.

In The Last Decade

Philipp Petersen

15 papers receiving 89 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Philipp Petersen Austria	6	30	29	25	21	8	16	96
Simone Brugiapaglia Canada	6	18 0.6×	15 0.5×	10 0.4×	57 2.7×	2 0.3×	17	101
J. Y. Shi United States	6	30 1.0×	68 2.3×	26 1.0×	19 0.9×	16 2.0×	17	124
Ross Boczar United States	4	23 0.8×	13 0.4×	7 0.3×	60 2.9×	3 0.4×	7	88
Niklas Koep Germany	4	11 0.4×	11 0.4×	5 0.2×	24 1.1×	21 2.6×	9	87
Samaneh Azadi United States	7	133 4.4×	60 2.1×	5 0.2×	32 1.5×	4 0.5×	11	204
Xinyang Yi United States	4	32 1.1×	33 1.1×	3 0.1×	68 3.2×	4 0.5×	7	112
Pascal Peter Germany	8	113 3.8×	13 0.4×	8 0.3×	29 1.4×		14	150
Dimitris C. Dracopoulos United Kingdom	6	14 0.5×	55 1.9×	20 0.8×	6 0.3×	25 3.1×	14	117
Russell Howes United States	6	12 0.4×	11 0.4×	10 0.4×	50 2.4×		8	107

Countries citing papers authored by Philipp Petersen

Since Specialization

Citations

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

Fields of papers citing papers by Philipp Petersen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philipp Petersen

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

All Works

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16 of 16 papers shown

Petersen, Philipp, et al.. (2025). Dimension-independent learning rates for high-dimensional classification problems. Analysis and Applications. 1–33.

Kazeev, Vladimir, et al.. (2024). Limitations of neural network training due to numerical instability of backpropagation. Advances in Computational Mathematics. 50(1). 1 indexed citations

Petersen, Philipp, et al.. (2023). VC dimensions of group convolutional neural networks. Neural Networks. 169. 462–474. 5 indexed citations

Petersen, Philipp, et al.. (2023). Neural network approximation and estimation of classifiers with classification boundary in a Barron class. The Annals of Applied Probability. 33(4). 6 indexed citations

Kutyniok, Gitta, et al.. (2022). Deep microlocal reconstruction for limited-angle tomography. Applied and Computational Harmonic Analysis. 59. 155–197. 6 indexed citations

Petersen, Philipp, et al.. (2022). Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems. Analysis and Applications. 21(1). 49–91. 6 indexed citations

Petersen, Philipp, et al.. (2022). Exponential ReLU Neural Network Approximation Rates for Point and Edge Singularities. Foundations of Computational Mathematics. 23(3). 1043–1127. 14 indexed citations

Petersen, Philipp, et al.. (2019). Unfavorable structural properties of the set of neural networks with fixed architecture. Publication Server of the Catholic University Eichstätt-Ingolstadt (Catholic University of Eichstätt-Ingolstadt). 5. 1–4. 2 indexed citations

Boche, Holger, Giuseppe Caire, Robert Calderbank, et al.. (2019). Compressed Sensing and Its Applications. CERN Document Server (European Organization for Nuclear Research). 18 indexed citations

10.

Voigtlaender, Felix & Philipp Petersen. (2019). Approximation in L^p(µ) with deep ReLU neural networks. Publication Server of the Catholic University Eichstätt-Ingolstadt (Catholic University of Eichstätt-Ingolstadt). 521. 1–4. 3 indexed citations

11.

Petersen, Philipp, et al.. (2019). The structure of spaces of neural network functions. Publication Server of the Catholic University Eichstätt-Ingolstadt (Catholic University of Eichstätt-Ingolstadt). 1 indexed citations

12.

Petersen, Philipp, et al.. (2019). Deep ReLU networks and high-order finite element methods. Analysis and Applications. 18(5). 715–770. 5 indexed citations

13.

Lessig, Christian, Philipp Petersen, & Martin Schäfer. (2017). WITHDRAWN: Bendlets: A second-order shearlet transform with bent elements. Applied and Computational Harmonic Analysis. 2 indexed citations

14.

Kutyniok, Gitta, Philipp Grohs, Philipp Petersen, & Helmut Bölcskei. (2017). Memory-optimal neural network approximation. 23–23. 2 indexed citations

15.

Ma, Jackie & Philipp Petersen. (2015). Linear independence of compactly supported separable shearlet systems. Journal of Mathematical Analysis and Applications. 428(1). 238–257. 1 indexed citations

16.

Sundaramoorthi, Ganesh, Philipp Petersen, V. S. Varadarajan, & Stefano Soatto. (2009). On the set of images modulo viewpoint and contrast changes. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 832–839. 24 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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