# Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search

@article{Szen2019PeriodicSE, title={Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search}, author={M. S{\"u}zen and Joan J. Cerd{\`a} and Cornelius Weber}, journal={ArXiv}, year={2019}, volume={abs/1911.07831} }

Establishing associations between the structure and the generalisation ability of deep neural networks (DNNs) is a challenging task in modern machine learning. Producing solutions to this challenge will bring progress both in the theoretical understanding of DNNs and in building new architectures efficiently. In this work, we address this challenge by developing a new complexity measure based on the concept of {Periodic Spectral Ergodicity} (PSE) originating from quantum statistical mechanics…

## One Citation

### Automated Deep Learning: Neural Architecture Search Is Not the End

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