Neural Network Processing Neural Networks: An efficient way to learn higher order functions
This addresses the problem of enabling neural networks to handle complex functional structures, which could benefit AI researchers and practitioners, but it appears incremental as it builds on existing neural network capabilities.
The paper introduces Neural Network Processing Neural Networks (NNPNNs), a new class of neural networks that can input neural networks and numerical values to represent and process rich structures, enabling the learning of higher-order functions.
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.