Automatic Design of Deep Networks with Neural Blocks
By Guoqiang Zhong, Wencong Jiao, Wei Gao, Kaizhu Huang. 2019
25 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neural Architecture Search & AutoML
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
- Simone Scardapane
- Michele Scarpiniti
- Aurelio Uncini
- Enzo Baccarelli
- Jiangpeng He
- Feng Zhu
- Laura Palagi
In Computer Vision and Pattern Recognition
- Michele Scarpiniti
- Enzo Baccarelli
- Simone Scardapane
- Aurelio Uncini
- Feng Zhu
- Jiangpeng He
- Laura Palagi
Citations in the corpus
- Why Should We Add Early Exits to Neural Networks? (2020, background): “uccess of deep networks can be attributed in large part to their extreme modularity and compositionality, coupled with the power of automatic optimization routines such as stochastic gradient descent [78]. While a number of innovative components have been proposed recently (such as attention layers ”