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arXiv 2012.03351math.FAcs.LGstat.ML

The universal approximation theorem for complex-valued neural networks

Technical University of Munich(慕尼黑工业大学);University of Vienna(维也纳大学);Catholic University of Eichstätt-Ingolstadt(艾希施泰特-因戈尔施塔特天主教大学)

Felix Voigtlaender

英文摘要:

We generalize the classical universal approximation theorem for neural networks to the case of complex-valued neural networks. Precisely, we consider feedforward networks with a complex activation function $σ: \mathbb{C} \to \mathbb{C}$ in which each neuron performs the operation $\mathbb{C}^N \to \mathbb{C}, z \mapsto σ(b + w^T z)$ with weights $w \in \mathbb{C}^N$ and a bias $b \in \mathbb{C}$, and with $σ$ applied componentwise. We completely characterize those activation functions $σ$ for which the associated complex networks have the universal approximation property, meaning that they can uniformly approximate any continuous function on any compact subset of $\mathbb{C}^d$ arbitrarily well. Unlike the classical case of real networks, the set of "good activation functions" which give rise to networks with the universal approximation property differs significantly depending on whether one considers deep networks or shallow networks: For deep networks with at least two hidden layers, the universal approximation property holds as long as $σ$ is neither a polynomial, a holomorphic function, or an antiholomorphic function. Shallow networks, on the other hand, are universal if and only if the real part or the imaginary part of $σ$ is not a polyharmonic function.

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