Sequence Level Training with Recurrent Neural Networks
Impact in
Classified as
- Journal
- International Conference on Learning Representations
In The Last Decade
doi.org/w18065313 →Countries where authors are citing Sequence Level Training with Recurrent Neural Networks
This map shows the geographic impact of Sequence Level Training with Recurrent Neural Networks. 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 Sequence Level Training with Recurrent Neural Networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sequence Level Training with Recurrent Neural Networks more than expected).
Fields of papers citing Sequence Level Training with Recurrent Neural Networks
This network shows the impact of Sequence Level Training with Recurrent Neural Networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Sequence Level Training with Recurrent Neural Networks.
About Sequence Level Training with Recurrent Neural Networks
This paper, published in 2016, received 308 indexed citations . Written by Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli and Wojciech Zaremba covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (257 citations), Computer Vision and Pattern Recognition (191 citations), Information Systems (17 citations), Signal Processing (13 citations) and Molecular Biology (7 citations). Published in International Conference on Learning Representations.
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/w18065313.