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Efficient approximate representations for computationally expensive features
2018
The European Symposium on Artificial Neural Networks
High computational complexity is often a barrier to achieving desired representations in resource-constrained settings. This paper introduces a simple and computationally cheap method of approximating complex features. We do so by carefully constraining the architecture of Neural Networks (NNs) and regress from raw data to the intended feature representation. Our analysis focuses on spectral features, and demonstrates how low-capacity networks can capture the end-to-end dynamics of cascaded
dblp:conf/esann/Santos-Rodriguez18
fatcat:bf56pimr45eoncyy62dfdjzrrm