International Conference on Learning Representations (ICLR 2013)
Impact in
- Authors
- 豊 松尾
In The Last Decade
doi.org/w4653157 →Countries where authors are citing International Conference on Learning Representations (ICLR 2013)
This map shows the geographic impact of International Conference on Learning Representations (ICLR 2013). 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 International Conference on Learning Representations (ICLR 2013) with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites International Conference on Learning Representations (ICLR 2013) more than expected).
Fields of papers citing International Conference on Learning Representations (ICLR 2013)
This network shows the impact of International Conference on Learning Representations (ICLR 2013). Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the International Conference on Learning Representations (ICLR 2013).
About International Conference on Learning Representations (ICLR 2013)
This paper, published in 2013, received 3.6k indexed citations . Written by 豊 松尾. It is primarily cited by scholars working on Artificial Intelligence (1.7k citations), Computer Vision and Pattern Recognition (1.5k citations), Signal Processing (296 citations), Electrical and Electronic Engineering (277 citations) and Information Systems (229 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.
This paper is also available at doi.org/w4653157.