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arXiv · 1602.06183

Node-By-Node Greedy Deep Learning for Interpretable Features

Ke Wu·Malik Magdon-Ismail
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Abstract

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.

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Ke Wu, Malik Magdon-Ismail. 2016-02-19. Node-By-Node Greedy Deep Learning for Interpretable Features. https://arxiv.org/abs/1602.06183

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