On the stability of deep networks

© 2015 International Conference on Learning Representations, ICLR. All rights reserved. In this work we study the properties of deep neural networks (DNN) with random weights. We formally prove that these networks perform a distance-preserving embedding of the data. Based on this we then draw conclusions on the size of the training data and the networks’ structure. A longer version of this paper with more results and details can be found in (Giryes et al., 2015). In particular, we formally prove in (Giryes et al., 2015) that DNN with random Gaussian weights perform a distance-preserving embedding of the data, with a special treatment for in-class and out-of-class data.

Duke Scholars

Author Guillermo Sapiro Electrical and Computer Engineering