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ICML 2018oral160 citations

A Spline Theory of Deep Learning

Randall Balestriero, baraniuk

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Abstract

We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of

BibTeX
@InProceedings{pmlr-v80-balestriero18b,
  title = 	 {A Spline Theory of Deep Learning},
  author =       {Balestriero, Randall and richard baraniuk},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {374--383},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf},
  url = 	 {https://proceedings.mlr.press/v80/balestriero18b.html},
  abstract = 	 {We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of
A Spline Theory of Deep Learning · ICML 2018