Foundations and Advances in Deep Learning

By Kyunghyun Cho. 2014

15 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mercedes Eugenia Paoletti
2. Juan Mario Haut
3. Antonio J. Plaza
4. Javier Plaza
5. Swalpa Kumar Roy
6. J. Urgen Schmidhuber
7. David R Ha
8. Yoshua Bengio

Citations in the corpus

• Deep learning in neural networks: An overview (2014, background): “15) can be improved through gradient enhancements and automatic learning rate adjustments during stochastic gradient descent (Cho, 2014; Cho, Raiko, & Ilin, 2013), and through Tikhonov-type (Tikhonov, Arsenin, & John, 1977) regularization of RBMs (Cho, Ilin, & Raiko, 2012).”
• A new deep convolutional neural network for fast hyperspectral image classification (2017, background): “…hidden layer that works like a principal component analysis (PCA) if its weights between the encoder and decoder are tied, and non-linear or deep autoencoders, which have more modeling power by employing multiple nonlinear intermediate layers symmetrically in the encoder and decoder (Cho, 2014).”