On the performance of deep learning for numerical optimization: An application to protein structure prediction

This study revisits the common application of deep learning and reformulates it for numerical, and protein structure prediction problems. The proposed approach adopts the idea of the neural architecture search to solve the problem at hand. Our contribution achieved competitive performances compared to the hand-crafted algorithms. The transfer and ensemble learnings are used to show how the optimization process can be accelerated. Deep neural networks have recently drawn considerable attention to build and evaluate artificial learning models for perceptual tasks.