3D Object Recognition with Deep Belief Nets

209 indexed citations
published 2009
Authors
Vinod NairGeoffrey E. Hinton
Journal
Neural Information Processing Systems

In The Last Decade

doi.org/w17873582 →

Countries where authors are citing 3D Object Recognition with Deep Belief Nets

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This map shows the geographic impact of 3D Object Recognition with Deep Belief Nets. 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 3D Object Recognition with Deep Belief Nets with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites 3D Object Recognition with Deep Belief Nets more than expected).

Fields of papers citing 3D Object Recognition with Deep Belief Nets

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of 3D Object Recognition with Deep Belief Nets. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the 3D Object Recognition with Deep Belief Nets.

About 3D Object Recognition with Deep Belief Nets

This paper, published in 2009, received 209 indexed citations . Written by Vinod Nair and Geoffrey E. Hinton covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (114 citations), Artificial Intelligence (103 citations) and Signal Processing (53 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/w17873582.

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