# Constructive lower bounds on model complexity of shallow perceptron networks

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## Abstract

Limitations of shallow (one-hidden-layer) perceptron networks are investigated with respect to computing multivariable functions on finite domains. Lower bounds are derived on growth of the number of network units or sizes of output weights in terms of variations of functions to be computed. A concrete construction is presented with a class of functions which cannot be computed by signum or Heaviside perceptron networks with considerably smaller numbers of units and smaller output weights than the sizes of the function’s domains. A subclass of these functions is described whose elements can be computed by two-hidden-layer perceptron networks with the number of units depending on logarithm of the size of the domain linearly.

## Keywords

Shallow and deep networks Model complexity and sparsity Signum perceptron networks Finite mappings Variational norms Hadamard matrices## Notes

### Acknowledgments

This work was partially supported by the Czech Grant Agency grant GA15-18108S and institutional support of the Institute of Computer Science RVO 67985807.

## Compliance with ethical standards

## Conflict of interest

The author declares that she has no conflict of interests.

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