  Neural Networks are function approximators that have achieved
state-of-the-art accuracy in numerous machine learning tasks. In spite of their
great success in terms of accuracy, their large training time makes it
difficult to use them for various tasks. In this paper, we explore the idea of
learning weight evolution pattern from a simple network for accelerating
training of novel neural networks. We use a neural network to learn the
training pattern from MNIST classification and utilize it to accelerate
training of neural networks used for CIFAR-10 and ImageNet classification. Our
method has a low memory footprint and is computationally efficient. This method
can also be used with other optimizers to give faster convergence. The results
indicate a general trend in the weight evolution during training of neural
networks.
