Neural Architecture Search Survey

It is no surprise that following the massive success of deep learning technology in solving complicated tasks, there is a growing demand for automated deep learning. Even though deep learning is a highly effective technology, there is a tremendous amount of human effort that goes into designing a deep learning algorithm (Figure 1). The field of automated deep learning is concerned with automating this process by finding suitable preprocessing techniques and architecture designs along with training routines and configurations required to obtain a well performing deep learning model. This drive for automation has led to a lot of interesting research work. Recently IBM launched their service of NeuNetS to automatically synthesize deep neural network models for various business applications.