• DOI:10.1007/978-3-662-44848-9_34
Corpus ID: 11684667

Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks

@inproceedings{Glehre2014LearnedNormPF,
  title={Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks},
  author={Çaglar G{\"u}lçehre and Kyunghyun Cho and Razvan Pascanu and Yoshua Bengio},
  booktitle={ECML/PKDD},
  year={2014}
}

Çaglar Gülçehre, Kyunghyun Cho, +1 author Yoshua Bengio

Published in ECML/PKDD 2014

Computer Science, Mathematics

In this paper we propose and investigate a novel nonlinear unit, called L p unit, for deep neural networks. The proposed L p unit receives signals from several projections of a subset of units in the layer below and computes a normalized L p norm. We notice two interesting interpretations of the L p unit. First, the proposed unit can be understood as a generalization of a number of conventional pooling operators such as average, root-mean-square and max pooling widely used in, for instance… CONTINUE READING

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed