Countries citing papers authored by Michael Tschannen
Since
Specialization
Citations
This map shows the geographic impact of Michael Tschannen'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 Michael Tschannen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Tschannen more than expected).
Fields of papers citing papers by Michael Tschannen
This network shows the impact of papers produced by Michael Tschannen. 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 Michael Tschannen. The network helps show where Michael Tschannen may publish in the future.
Co-authorship network of co-authors of Michael Tschannen
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Tschannen.
A scholar is included among the top collaborators of Michael Tschannen 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 Michael Tschannen. Michael Tschannen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tschannen, Michael, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, & Mario Lučić. (2020). On Mutual Information Maximization for Representation Learning. arXiv (Cornell University).15 indexed citations
5.
Locatello, Francesco, Michael Tschannen, Stefan Bauer, et al.. (2020). Disentangling Factors of Variations Using Few Labels. arXiv (Cornell University).21 indexed citations
6.
Minderer, Matthias, Olivier Bachem, Neil Houlsby, & Michael Tschannen. (2020). Automatic Shortcut Removal for Self-Supervised Representation Learning. International Conference on Machine Learning. 1. 6927–6937.2 indexed citations
7.
Locatello, Francesco, et al.. (2020). Weakly-Supervised Disentanglement Without Compromises. 1. 6348–6359.4 indexed citations
8.
Lučić, Mario, Michael Tschannen, Marvin Ritter, et al.. (2019). High-Fidelity Image Generation With Fewer Labels. International Conference on Machine Learning. 4183–4192.16 indexed citations
9.
Zhai, Xiaohua, Joan Puigcerver, Alexander Kolesnikov, et al.. (2019). The Visual Task Adaptation Benchmark. arXiv (Cornell University).22 indexed citations
10.
Furlanello, Tommaso, Zachary C. Lipton, Michael Tschannen, Laurent Itti, & Anima Anandkumar. (2018). Born Again Neural Networks. CaltechAUTHORS (California Institute of Technology). 1607–1616.109 indexed citations
11.
Mentzer, Fabian, et al.. (2018). Towards Image Understanding from Deep Compression without Decoding. Lirias (KU Leuven).4 indexed citations
12.
Agustsson, Eirikur, Michael Tschannen, Fabian Mentzer, Radu Timofte, & Luc Van Gool. (2018). Extreme Learned Image Compression with GANs. Computer Vision and Pattern Recognition. 2587–2590.7 indexed citations
13.
Tschannen, Michael, et al.. (2018). StrassenNets: Deep Learning with a Multiplication Budget.. CaltechAUTHORS (California Institute of Technology). 4985–4994.2 indexed citations
14.
Agustsson, Eirikur, Fabian Mentzer, Michael Tschannen, et al.. (2017). Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks.. arXiv (Cornell University).10 indexed citations
15.
Agustsson, Eirikur, Fabian Mentzer, Michael Tschannen, et al.. (2017). Soft-to-hard vector quantization for end-to-end learning compressible representations. Lirias (KU Leuven). 30. 1141–1151.77 indexed citations
Tschannen, Michael, Grzegorz Toporek, Daphné Wallach, Matthias Peterhans, & Stefan Weber. (2012). Single Marker Localization for Automatic Patient Registration in Interventional Radiology. Bern Open Repository and Information System (University of Bern). 31–34.2 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.