A greedy constructive algorithm for the optimization of neural network architectures

In this work we propose a new method to optimize the architecture of an artificial neural network. The algorithm proposed, called Greedy Search for Neural Network Architecture, aims to minimize the complexity of the architecture search and the complexity of the final model selected without compromising the predictive performance. The reduction of the computational cost makes this approach appealing for two reasons. Firstly, there is a need from domain scientists to easily interpret predictions returned by a deep learning model and this tends to be cumbersome when neural networks have complex structures. Secondly, the use of neural networks is challenging in situations with compute/memory limitations. Promising numerical results show that our method is competitive against other hyperparameter optimization algorithms for attainable performance and computational cost. We also generalize the definition of adjusted score from linear regression models to neural networks. Numerical experiments are presented to show that the adjusted score can boost the greedy search to favor smaller architectures over larger ones without compromising the predictive performance.