Replicated Softmax: an Undirected Topic Model
Impact in
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- Neural Information Processing Systems
In The Last Decade
doi.org/w4133358 →Countries where authors are citing Replicated Softmax: an Undirected Topic Model
This map shows the geographic impact of Replicated Softmax: an Undirected Topic Model. 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 Replicated Softmax: an Undirected Topic Model with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Replicated Softmax: an Undirected Topic Model more than expected).
Fields of papers citing Replicated Softmax: an Undirected Topic Model
This network shows the impact of Replicated Softmax: an Undirected Topic Model. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Replicated Softmax: an Undirected Topic Model.
About Replicated Softmax: an Undirected Topic Model
This paper, published in 2009, received 306 indexed citations . Written by Geoffrey E. Hinton and Ruslan Salakhutdinov covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (194 citations), Computer Vision and Pattern Recognition (145 citations), Signal Processing (37 citations), Information Systems (34 citations) and Statistical and Nonlinear Physics (18 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/w4133358.