Implicit curriculum learning

Abstract

Systems and techniques for facilitating implicit curriculum learning are provided. These allow for improved machine learning systems that can automatically execute curriculum learning without drawbacks such as pre-sorting data into different epochs which may have varying degrees of difficulty (e.g. easiest first, then harder epochs). Applicant's techniques can be executed more efficiently by automatically iterating over a data set, which need not be manually separated into different epochs. Thus, a system can access a neural network and a set of labeled data candidates. In various aspects, the system can perform a plurality of training epochs on the neural network based on the set of labeled data candidates. In various instances, the system can iteratively update the set of labeled data candidates as the plurality of training epochs are performed, by removing, after each training epoch, a dropout percentage of those labeled data candidates which the neural network correctly classified during the training epoch.