DESCRIPTION
The project team had to conduct a full review of the scientific literature on methods for effective training of neural network models, evaluating their quality, and ranking models according to their quality within the framework of the NAS task, which allow reducing the number of computing resources used several times.
RELEVANCE
Methods of ranking models according to their quality significantly speed up the selection of architecture for automatic search, reducing the full search procedure to several GPU hours. Methods of effective training and quality assessment allow you to quickly get the final quality of the model, which leads to faster development of the final model and reduces the amount of computing resources needed to solve the problem.