Wasserstein Auto-Encoders

262 indexed citations

Abstract

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About

This paper, published in 2018, received 262 indexed citations. Written by Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly and Bernhard Schölkopf covering the research area of Statistical and Nonlinear Physics. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (157 citations), Artificial Intelligence (144 citations) and Signal Processing (31 citations). Published in MPG.PuRe (Max Planck Society).

In The Last Decade

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Countries where authors are citing Wasserstein Auto-Encoders

Specialization
Citations

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

Fields of papers citing Wasserstein Auto-Encoders

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Wasserstein Auto-Encoders. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Wasserstein Auto-Encoders.

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.

This paper is also available at doi.org/w5874456.

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