The observer assisted method for adjusting hyper parameters in deep learning algorithms
This paper presents a concept of a novel method for adjusting hyper parameters in Deep Learning (DL) algorithms. An external agent observer monitors a performance of a selected Deep Learning algorithm. The observer learns to model the DL algorithm using a series of random experiments. Consequently, it may be used for predicting a response of the DL algorithm in terms of a selected quality measurement to a set of hyper parameters. This allows to construct an ensemble composed of a series of evaluators which constitute an observer assisted architecture. The architecture may be used to gradually iterate towards to the best achievable quality score in tiny steps governed by a unit of progress. The algorithm is stopped when the maximum number of steps is reached or no further progress is made.
ArXiv