Network Morphism based Neural Architecture Search (NAS) is one of the most
efficient methods, however, knowing where and when to add new neurons or remove
dis-functional ones is generally left to black-box Reinforcement Learning
models. In this paper, we present a new Network Morphism based NAS called Noisy
Heuristics NAS which uses heuristics learned from manually developing neural
network models and inspired by biological neuronal dynamics. Firstly, we add
new neurons randomly and prune away some to select only the best fitting
neurons. Secondly, we control the number of layers in the network using the
relationship of hidden units to the number of input-output connections. Our
method can increase or decrease the capacity or non-linearity of models online
which is specified with a few meta-parameters by the user. Our method
generalizes both on toy datasets and on real-world data sets such as MNIST,
CIFAR-10, and CIFAR-100. The performance is comparable to the hand-engineered
architecture ResNet-18 with the similar parameters.

本文介绍了一种基于网络态势的神经结构搜索方法 - Noisy Heuristics NAS，该方法利用人工智能动态学中所学得的启发式算法和生物神经元的动态特性，通过添加和修剪神经元以及控制网络中隐含层数量的关系等元参数的方式，可以在线增加或减少神经网络模型的容量或非线性，进而实现对神经结构的扩展和优化。实验结果在 MNIST、CIFAR-10 和 CIFAR-100 等数据集上表现良好，并与手工构建的 ResNet-18 结构相当。