What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle
By G. Huang. Cognitive Computation, 2015
475 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/9dc64d487a73c4493dd6e846783fdbfdd06f6ad6
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
- Roberto Prevete
- Sk. Md. Mizanur Rahman
- Francesco Donnarumma
- Andrea Apicella
- Abdulhameed Alelaiwi
- Atif M. Alamri
- Francesco Isgrò
- Muhammad Ghulam
In Electrical and Electronic Engineering
- Roberto Prevete
- Sk. Md. Mizanur Rahman
- Francesco Donnarumma
- Andrea Apicella
- Abdulhameed Alelaiwi
- Atif M. Alamri
- Francesco Isgrò
- Muhammad Ghulam
Citations in the corpus
- Smart Health Solution Integrating IoT and Cloud: A Case Study of Voice Pathology Monitoring (2017, methodology): “[11] G.”
- A survey on modern trainable activation functions (2020, methodology): “(Ertuğrul, 2018) proposes two trainable activation functions using linear regression, but using network architectures different from the classic Feed-Forward Neural Networks (see for example (Chen et al., 2018; Huang, 2015)).”