Nasil : Neural Architecture Search With Imitation Learning

Automated machine learning (AML) refers to a class of techniques that, given a problem, can find an optimal set of model architectures, properties, and parameters. In recent years, AML has shown great success in finding neural network structures that are as good as the hand-designed structures and even better. AML is usually a time consuming process that needs to search a big search space. All available AML solutions start the search from scratch. Here we introduce neural architecture search with imitation learning (NASIL) method that starts the search by learning from hand designed structures by experts. NASIL is an actor-critic structure that uses hindsight experience replay(HER) and starts by training on these hand-designed structures stored in memory. We tested NASIL found structures on publicly available Google speech command dataset as well as in-house dataset. Our results show that NASIL is able to find a better structure than hand-designed structures in terms of accuracy and number of parameters. More importantly it is faster that similar approaches.