A State-of-the-Art Survey on Deep Learning Theory and Architectures
- Journal
- Electronics
In The Last Decade
doi.org/10.3390/electronics8030292 →Countries where authors are citing A State-of-the-Art Survey on Deep Learning Theory and Architectures
This map shows the geographic impact of A State-of-the-Art Survey on Deep Learning Theory and Architectures. 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 A State-of-the-Art Survey on Deep Learning Theory and Architectures with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A State-of-the-Art Survey on Deep Learning Theory and Architectures more than expected).
Fields of papers citing A State-of-the-Art Survey on Deep Learning Theory and Architectures
This network shows the impact of A State-of-the-Art Survey on Deep Learning Theory and Architectures. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A State-of-the-Art Survey on Deep Learning Theory and Architectures.
About A State-of-the-Art Survey on Deep Learning Theory and Architectures
This paper, published in 2019, received 1.1k indexed citations . Written by Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul Ahad S. Awwal and Vijayan K. Asari covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (328 citations), Computer Vision and Pattern Recognition (280 citations) and Radiology, Nuclear Medicine and Imaging (130 citations). Published in Electronics.
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/10.3390/electronics8030292.