: The scientific publication outputs have been exponentially growing in all domains, making document retrieval like finding a needle in a haystack. The main challenge roots in the fast-expanding attributed network with rich information of paper contents on nodes and citation relations on edges. This talk will introduce deep neural network models and deep generative models that are designed for addressing the problems of recommendation of top-k relevant papers to read/cite; identification of top-k possible authors of an anonymous paper; search of datasets and related papers, and identification top-k popular datasets in different domains.
Building 9 - Lecture hall 2
09:50 - 10:10