Sensitivity analysis applied to the construction of radial basis function networks
By Daming Shi, Daniel S. Yeung, Junbin Gao. 2005
66 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Raja Giryes
- Guillermo Sapiro
- Hoang-Dung Tran
- Taylor T. Johnson
- Weiming Xiang
- Jure Sokolić
- Miguel Raul Dias Rodrigues
- Edouard Oyallon
In Computer Vision and Pattern Recognition
- Raja Giryes
- Guillermo Sapiro
- Jure Sokolić
- Miguel Raul Dias Rodrigues
- Lei Zhang
- Luc Van Gool
- Dacheng Tao
- Julien Mairal
Citations in the corpus
- Robust Large Margin Deep Neural Networks (2016, background): “neural networks [38], [39], and for the construction of radial basis function networks [40], since the spectral norm of the Jacobian matrix quantifies the sensitivity of DNN output with”
- Output Reachable Set Estimation and Verification for Multilayer Neural Networks (2017, background): “The sensitivity in the average point of view works well for learning algorithm improvement [29], weight selection [30], architecture construction [31], for instance.”