Mish: A Self Regularized Non-Monotonic Neural Activation Function
Impact in
Classified as
- Authors
- Diganta Misra
- Journal
- arXiv (Cornell University)
In The Last Decade
doi.org/w16806797 →Countries where authors are citing Mish: A Self Regularized Non-Monotonic Neural Activation Function
This map shows the geographic impact of Mish: A Self Regularized Non-Monotonic Neural Activation Function. 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 Mish: A Self Regularized Non-Monotonic Neural Activation Function with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mish: A Self Regularized Non-Monotonic Neural Activation Function more than expected).
Fields of papers citing Mish: A Self Regularized Non-Monotonic Neural Activation Function
This network shows the impact of Mish: A Self Regularized Non-Monotonic Neural Activation Function. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Mish: A Self Regularized Non-Monotonic Neural Activation Function.
About Mish: A Self Regularized Non-Monotonic Neural Activation Function
This paper, published in 2019, received 317 indexed citations . Written by Diganta Misra 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 (169 citations), Artificial Intelligence (79 citations), Media Technology (33 citations), Radiology, Nuclear Medicine and Imaging (26 citations) and Industrial and Manufacturing Engineering (25 citations). Published in arXiv (Cornell University).
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This paper is also available at doi.org/w16806797.