This map shows the geographic impact of Taco Cohen'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 Taco Cohen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taco Cohen more than expected).
This network shows the impact of papers produced by Taco Cohen. 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 Taco Cohen. The network helps show where Taco Cohen may publish in the future.
Co-authorship network of co-authors of Taco Cohen
This figure shows the co-authorship network connecting the top 25 collaborators of Taco Cohen.
A scholar is included among the top collaborators of Taco Cohen 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 Taco Cohen. Taco Cohen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cohen, Taco, et al.. (2019). Gauge Equivariant Convolutional Networks and the Icosahedral CNN. UvA-DARE (University of Amsterdam). 97. 1321–1330.22 indexed citations
9.
Weiler, Maurice, Mario Geiger, Max Welling, Wouter Boomsma, & Taco Cohen. (2018). 3D steerable CNNs: learning rotationally equivariant features in volumetric data. UvA-DARE (University of Amsterdam). 31. 10402–10413.78 indexed citations
10.
Winkens, Jim, Jasper Linmans, Bastiaan S. Veeling, Taco Cohen, & Max Welling. (2018). Improved Semantic Segmentation for Histopathology using Rotation Equivariant Convolutional Networks.9 indexed citations
11.
Zintgraf, Luisa, Taco Cohen, Tameem Adel, & Max Welling. (2017). Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam).43 indexed citations
Kim, Jae-Soo, et al.. (1998). A fuzzy neural network model for the estimation of the feeding rate to an anaerobic waste water treatment process. Otago University Research Archive (University of Otago).1 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.