Approximation capabilities of folding networks
نویسنده
چکیده
In this paper we show several approximation results for folding networks { a generalization of partial recurrent neural networks such that not only time sequences but arbitrary trees can serve as input: Any measurable function can be approximated in probability. Any continuous function can be approximated in the maximum norm on inputs with restricted height, but the resources necessarily increase at least exponentially in the input height. In general, approximation on arbitrary inputs is not possible in the maximum norm.
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