Functions are rich in meaning and can be interpreted in a variety of ways. Neural networks were proven to be capable of approximating a large class of functions[1]. In this paper, we propose a new class of neural networks called "Neural Network Processing Neural Networks" (NNPNNs), which inputs neural networks and numerical values, instead of just numerical values. Thus enabling neural networks to represent and process rich structures.

中文翻译：

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神经网络处理神经网络：学习高阶函数的有效方法

函数具有丰富的含义，可以通过多种方式进行解释。神经网络被证明能够逼近一大类函数[1]。在本文中，我们提出了一类新的神经网络，称为“神经网络处理神经网络”（NNPNNs），它输入神经网络和数值，而不仅仅是数值。从而使神经网络能够表示和处理丰富的结构。