Multimodal Learning with Deep Boltzmann Machines
Impact in
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doi.org/w3820358 →Countries where authors are citing Multimodal Learning with Deep Boltzmann Machines
This map shows the geographic impact of Multimodal Learning with Deep Boltzmann Machines. 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 Multimodal Learning with Deep Boltzmann Machines with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Multimodal Learning with Deep Boltzmann Machines more than expected).
Fields of papers citing Multimodal Learning with Deep Boltzmann Machines
This network shows the impact of Multimodal Learning with Deep Boltzmann Machines. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Multimodal Learning with Deep Boltzmann Machines.
About Multimodal Learning with Deep Boltzmann Machines
This paper, published in 2012, received 419 indexed citations . Written by Nitish Srivastava and Ruslan Salakhutdinov covering the research area of Signal Processing and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (269 citations), Artificial Intelligence (181 citations), Signal Processing (54 citations), Information Systems (33 citations) and Molecular Biology (20 citations). Published in Journal of Machine Learning Research.
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/w3820358.