Rice, Amazon report breakthrough in ’distributed deep learning’

MACH slashes time and resources needed to train computers for product searches . Online shoppers typically string together a few words to search for the product they want, but in a world with millions of products and shoppers, the task of matching those unspecific words to the right product is one of the biggest challenges in information retrieval. Using a divide-and-conquer approach that leverages the power of compressed sensing , computer scientists from Rice University and Amazon have shown they can slash the amount of time and computational resources it takes to train computers for product search and similar " extreme classification problems ” like speech translation and answering general questions. The research will be presented this week at the 2019 Conference on Neural Information Processing Systems ( NeurIPS 2019 ) in Vancouver. The results include tests performed in 2018 when lead researcher Anshumali Shrivastava and lead author Tharun Medini , both of Rice, were visiting Amazon Search in Palo Alto, California. In tests on an Amazon search dataset that included some 70 million queries and more than 49 million products, Shrivastava, Medini and colleagues showed their approach of using "merged-average classifiers via hashing,” (MACH) required a fraction of the training resources of some state-of-the-art commercial systems. "Our training times are about 7-10 times faster, and our memory footprints are 2-4 times smaller than the best baseline performances of previously reported large-scale, distributed deep-learning systems,” said Shrivastava, an assistant professor of computer science at Rice.