Deep Learning with Limited Numerical Precision
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w3294705 →Countries where authors are citing Deep Learning with Limited Numerical Precision
This map shows the geographic impact of Deep Learning with Limited Numerical Precision. 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 Deep Learning with Limited Numerical Precision with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep Learning with Limited Numerical Precision more than expected).
Fields of papers citing Deep Learning with Limited Numerical Precision
This network shows the impact of Deep Learning with Limited Numerical Precision. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep Learning with Limited Numerical Precision.
About Deep Learning with Limited Numerical Precision
This paper, published in 2015, received 435 indexed citations . Written by Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan and Pritish Narayanan covering the research area of Statistical and Nonlinear Physics, Computational Theory and Mathematics and Artificial Intelligence. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (279 citations), Artificial Intelligence (211 citations) and Electrical and Electronic Engineering (182 citations). Published in International Conference on Machine Learning.
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/w3294705.