18.3k total citations 10 papers, 184 citations indexed
About
Thomas Kipf is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing.
According to data from OpenAlex, Thomas Kipf has authored 10 papers receiving a total of 184 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 4 papers in Signal Processing. Recurrent topics in Thomas Kipf's work include Multimodal Machine Learning Applications (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Advanced Image and Video Retrieval Techniques (2 papers). Thomas Kipf is often cited by papers focused on Multimodal Machine Learning Applications (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Advanced Image and Video Retrieval Techniques (2 papers). Thomas Kipf collaborates with scholars based in Netherlands, Germany and United States. Thomas Kipf's co-authors include Max Welling, Ethan Fetaya, Richard S. Zemel, Kuan-Chieh Wang, Alfons Kemper, Elise van der Pol, Andreas Kipf, Viktor Leis, Bernhard Radke and Peter Boncz and has published in prestigious journals such as Data Archiving and Networked Services (DANS), UvA-DARE (University of Amsterdam) and Neural Information Processing Systems.
In The Last Decade
Thomas Kipf
10 papers
receiving
174 citations
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Thomas Kipf'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 Thomas Kipf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Kipf more than expected).
This network shows the impact of papers produced by Thomas Kipf. 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 Thomas Kipf. The network helps show where Thomas Kipf may publish in the future.
Co-authorship network of co-authors of Thomas Kipf
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Kipf.
A scholar is included among the top collaborators of Thomas Kipf 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 Thomas Kipf. Thomas Kipf is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kipf, Thomas, Elise van der Pol, & Max Welling. (2020). Contrastive Learning of Structured World Models. Data Archiving and Networked Services (DANS).15 indexed citations
5.
Locatello, Francesco, Dirk Weissenborn, Thomas Unterthiner, et al.. (2020). Object-Centric Learning with Slot Attention. Neural Information Processing Systems. 33. 11525–11538.13 indexed citations
6.
Kipf, Thomas. (2020). Deep learning with graph-structured representations. UvA-DARE (University of Amsterdam).16 indexed citations
7.
Kipf, Andreas, Jonas Müller, Thomas Kipf, et al.. (2019). Estimating Cardinalities with Deep Sketches. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands. 1937–1940.21 indexed citations
8.
Kipf, Andreas, Thomas Kipf, Bernhard Radke, et al.. (2018). Learned Cardinalities: Estimating Correlated Joins with Deep Learning. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands.14 indexed citations
9.
Kipf, Thomas, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, & Richard S. Zemel. (2018). Neural Relational Inference for Interacting Systems. UvA-DARE (University of Amsterdam). 80. 2688–2697.92 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.