Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Impact in
Classified as
- Authors
- Matthew D. ZeilerRob Fergus
- Journal
- arXiv (Cornell University)
In The Last Decade
doi.org/w9258281 →Countries where authors are citing Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
This map shows the geographic impact of Stochastic Pooling for Regularization of Deep Convolutional Neural 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 Stochastic Pooling for Regularization of Deep Convolutional Neural Networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stochastic Pooling for Regularization of Deep Convolutional Neural Networks more than expected).
Fields of papers citing Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
This network shows the impact of Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Stochastic Pooling for Regularization of Deep Convolutional Neural Networks.
About Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
This paper, published in 2013, received 326 indexed citations . Written by Matthew D. Zeiler and Rob Fergus covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (214 citations), Artificial Intelligence (150 citations), Media Technology (43 citations), Signal Processing (25 citations) and Electrical and Electronic Engineering (21 citations). Published in arXiv (Cornell University).
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/w9258281.