Automatic differentiation in PyTorch
In The Last Decade
doi.org/w74330037 →Countries where authors are citing Automatic differentiation in PyTorch
This map shows the geographic impact of Automatic differentiation in PyTorch. 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 Automatic differentiation in PyTorch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Automatic differentiation in PyTorch more than expected).
Fields of papers citing Automatic differentiation in PyTorch
This network shows the impact of Automatic differentiation in PyTorch. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Automatic differentiation in PyTorch.
About Automatic differentiation in PyTorch
This paper, published in 2017, received 5.7k indexed citations . Written by Adam Paszke, Sam Gross, Soumith Chintala, Edward Z. Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga and Adam Lerer covering the research area of Statistical and Nonlinear Physics and Artificial Intelligence. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (2.9k citations), Artificial Intelligence (2.4k citations) and Signal Processing (409 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/w74330037.