How to Construct Deep Recurrent Neural Networks
Impact in
Classified as
- Journal
- International Conference on Learning Representations
In The Last Decade
doi.org/w4298807 →Countries where authors are citing How to Construct Deep Recurrent Neural Networks
This map shows the geographic impact of How to Construct Deep 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 How to Construct Deep 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 How to Construct Deep Recurrent Neural Networks more than expected).
Fields of papers citing How to Construct Deep Recurrent Neural Networks
This network shows the impact of How to Construct Deep 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 How to Construct Deep Recurrent Neural Networks.
About How to Construct Deep Recurrent Neural Networks
This paper, published in 2014, received 405 indexed citations . Written by Razvan Pascanu, Çağlar Gülçehre, Kyunghyun Cho and Yoshua Bengio covering the research area of Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. It is primarily cited by scholars working on Artificial Intelligence (228 citations), Computer Vision and Pattern Recognition (78 citations), Signal Processing (68 citations), Electrical and Electronic Engineering (66 citations) and Control and Systems Engineering (27 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/w4298807.