Developing an interval method for training denoising autoencoders by bounding the noise

Abstract

This paper discusses prospects of using interval methods to training denoising autoencoders. Advantages and disadvantages of using the interval approach are discussed. It is proposed to formulate the problem of training the proper neural network as a constraint-satisfaction, and not optimization, problem. Pros and cons of this approach are considered. Preliminary numerical experiments are also presented.