Neural Graph Learning

Abstract: Machine learning has become ubiquitous in solving complex problems, and recent advances have leapfrogged the ability to build intelligent systems that can read text, see images, or hear sounds from the real world and understand semantics. While this has led to the development of many tools and accelerated progress in several fields, designing machine learning approaches “from scratch” remains a daunting challenge for many applications. On the other hand, graphs offer a simple, elegant way to express different types of relationships observed in data or to concisely encode structure underlying a problem. Thus, how can we combine the flexibility of graphs with the power of machine learning? This problem motivates new approaches that employ graph-based machine learning as a computing mechanism for solving real-world prediction tasks. So, the question remains: how do we design efficient learning algorithms for such scenarios and deal with the difficulty of hard-to-optimize machine learning functions, massively-sized graphs, and complex prediction tasks involving large (exponential) output spaces?

In his talk, Dr. Sujith Ravi, of Google, will describe how to address these challenges and design efficient distributed algorithms using Expander, Google’s large-scale graph-based machine learning framework. This work was motivated by the need to design robust methods that learn to generalize from data (and underlying relationships) with minimal supervision—the way humans do. The graph learning framework can easily handle massive graphs (containing billions of vertices and trillions of edges) and make predictions over billions of output labels while achieving O(1) space complexity per vertex. The framework is used to power a number of machine intelligence applications, including Smart Reply, image recognition, and video summarization, and can be combined with deep neural networks to tackle complex language understanding and computer vision problems. Dr. Ravi’s talk also will highlight some of Google’s latest research and results on “neural graph learning,” a new joint optimization framework for combing graph learning with deep neural network models.

Biography: Sujith Ravi, a Staff Research Scientist and Technical Lead Manager at Google, leads the company’s large-scale graph-based machine learning platform that powers natural language understanding and image recognition for products used by millions of people everyday in Search, Gmail, Photos, Android, YouTube, and Allo. The machine learning technology enables features such as Smart Reply that automatically suggests replies to incoming e-mails or chat messages in Inbox and Allo; Photos that searches for anything, from “hugs” to “dogs,” with the latest image recognition system; and smart messaging directly from Android Wear smartwatches powered by on-device machine learning. Dr. Ravi has authored more than 50 scientific publications and patents in top-tier machine learning and natural language processing conferences, and his work won the ACM SIGKDD Best Research Paper Award in 2014. He organizes machine learning symposia/workshops and regularly serves as Area Chair and PC of top-tier machine learning and natural language processing conferences, including NIPS, ICML, ACL, COLING, KDD, and WSDM.