Guest Editorial: Special Issue on Deep Representation and Transfer Learning for Smart and Connected Health
IEEE Transactions on Neural Networks and Learning Systems,
Volume 32,
Number 2,
pages 464--465,
doi: 10.1109/TNNLS.2021.3049931
- Feb 2021
Associated documents :
DEEP neural networks (NNs) have been proved to be efficient learning systems for supervised and unsupervised
tasks. However, learning complex data representations using
deep NNs can be difficult due to problems such as lack of data,
exploding or vanishing gradients, high computational cost, or
incorrect parameter initialization, among others. Deep representation and transfer learning (RTL) can facilitate the learning
of data representations by taking advantage of transferable
features learned by an NN model in a source domain, and
adapting the model to a new domain.
@Article{PWRDT21,
author = {Palade, Vasile and Wermter, Stefan and Ruiz-Garcia, Ariel and De Pádua Braga, Antônio and Took, Clive Cheong},
title = {Guest Editorial: Special Issue on Deep Representation and Transfer Learning for Smart and Connected Health},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
number = {2},
volume = {32},
pages = {464--465},
year = {2021},
month = {Feb},
publisher = {IEEE},
doi = {10.1109/TNNLS.2021.3049931},
}