Multilayer networks have seen a resurgence under the umbrella of deep learning. Current deep learning algorithms train the layers of the network sequentially, improving algorithmic performance as well as providing some regularization. We present a new training algorithm for deep networks which trains \emph{each node in the network} sequentially. Our algorithm is orders of magnitude faster, creates more interpretable internal representations at the node level, while not sacrificing on the ultimate out-of-sample performance.
@article{arxiv.1602.06183,
title = {Node-By-Node Greedy Deep Learning for Interpretable Features},
author = {Ke Wu and Malik Magdon-Ismail},
journal= {arXiv preprint arXiv:1602.06183},
year = {2016}
}