Torch7: A Matlab-like Environment for Machine Learning
Impact in
Classified as
- Journal
- Infoscience (Ecole Polytechnique Fédérale de Lausanne)
In The Last Decade
doi.org/w12964737 →Countries where authors are citing Torch7: A Matlab-like Environment for Machine Learning
This map shows the geographic impact of Torch7: A Matlab-like Environment for Machine Learning. 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 Torch7: A Matlab-like Environment for Machine Learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Torch7: A Matlab-like Environment for Machine Learning more than expected).
Fields of papers citing Torch7: A Matlab-like Environment for Machine Learning
This network shows the impact of Torch7: A Matlab-like Environment for Machine Learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Torch7: A Matlab-like Environment for Machine Learning.
About Torch7: A Matlab-like Environment for Machine Learning
This paper, published in 2011, received 713 indexed citations . Written by Ronan Collobert, Koray Kavukcuoglu and Clément Farabet covering the research area of Hardware and Architecture, Artificial Intelligence and Computational Theory and Mathematics. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (446 citations), Artificial Intelligence (317 citations), Electrical and Electronic Engineering (77 citations), Signal Processing (66 citations) and Hardware and Architecture (54 citations). Published in Infoscience (Ecole Polytechnique Fédérale de Lausanne).
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/w12964737.