Sparse Feature Learning for Deep Belief Networks
Impact in
Classified as
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w9456761 →Countries where authors are citing Sparse Feature Learning for Deep Belief Networks
This map shows the geographic impact of Sparse Feature Learning for Deep Belief 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 Sparse Feature Learning for Deep Belief Networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sparse Feature Learning for Deep Belief Networks more than expected).
Fields of papers citing Sparse Feature Learning for Deep Belief Networks
This network shows the impact of Sparse Feature Learning for Deep Belief Networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Sparse Feature Learning for Deep Belief Networks.
About Sparse Feature Learning for Deep Belief Networks
This paper, published in 2007, received 457 indexed citations . Written by Marc’Aurelio Ranzato, Y-Lan Boureau and Y. Le Cun covering the research area of Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (267 citations), Artificial Intelligence (230 citations), Signal Processing (83 citations), Computational Mechanics (32 citations) and Media Technology (32 citations). Published in Neural Information Processing Systems.
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/w9456761.