Computer scientists simplify deep learning

Computer scientists at Rice University have developed a new technique for minimizing the amount of computations required for deep learning. The simplification technique is similar to methods commonly used to minimize the amount of math required for data analysis. "This applies to any deep-learning architecture, and the technique scales sublinearly, which means that the larger the deep neural network to which this is applied, the more the savings in computations there will be," lead researcher Anshumali Shrivastava, an assistant professor of computer science, said in a news release. Deep-learning networks hold tremendous potential in a variety of fields, from healthcare to communications. The networks are still cumbersome, requiring significant amounts of computing power.