Lukas Schott

660 total citations
4 papers, 90 citations indexed

About

Lukas Schott is a scholar working on Artificial Intelligence, Molecular Biology and Signal Processing. According to data from OpenAlex, Lukas Schott has authored 4 papers receiving a total of 90 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 1 paper in Molecular Biology and 1 paper in Signal Processing. Recurrent topics in Lukas Schott's work include Adversarial Robustness in Machine Learning (3 papers), Anomaly Detection Techniques and Applications (2 papers) and Time Series Analysis and Forecasting (1 paper). Lukas Schott is often cited by papers focused on Adversarial Robustness in Machine Learning (3 papers), Anomaly Detection Techniques and Applications (2 papers) and Time Series Analysis and Forecasting (1 paper). Lukas Schott collaborates with scholars based in Germany and United States. Lukas Schott's co-authors include Matthias Bethge, Wieland Brendel, Jonas Rauber, Mohak Shah, Naveen Ramakrishnan, Shengdong Zhang, R. Zimmermann, Evgenia Rusak and Oliver Bringmann and has published in prestigious journals such as arXiv (Cornell University) and MPG.PuRe (Max Planck Society).

In The Last Decade

Lukas Schott

4 papers receiving 88 citations

Peers

Countries citing papers authored by Lukas Schott

Since Specialization

Citations

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

Fields of papers citing papers by Lukas Schott

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lukas Schott

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

All Works

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4 of 4 papers shown

#	Work	Indexed citations
1	Increasing the robustness of DNNs against image corruptions by playing the Game of Noise 2020 ·arXiv (Cornell University) ·Evgenia Rusak, Lukas Schott, R. Zimmermann, (unknown), Oliver Bringmann, Matthias Bethge, Wieland Brendel	16
2	Towards the First Adversarially Robust Neural Network Model on MNIST 2019 ·MPG.PuRe (Max Planck Society) ·Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel	53
3	Robust Perception through Analysis by Synthesis. 2018 ·arXiv (Cornell University) ·Lukas Schott, Jonas Rauber, Wieland Brendel, Matthias Bethge	6
4	Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction 2017 ·Shengdong Zhang, (unknown), Naveen Ramakrishnan, Lukas Schott, Mohak Shah	15

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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