175 Tay Son, Dong Da, Hanoi.
Title: Discovering Interpretable Hidden Semantics from Massive Data
Speaker: THAN Khoat, Hanoi University of Science and Technology.
Abstract: Humans are excellent at learning from texts, and making inference on new observations based on his/her knowledge. The ability to reason correctly from little and noisy information is one of the long-standing mysteries that neuroscientists have been trying to understand. It motivates researchers in Artificial Intelligence to make an intelligent computer that can mimic human abilities and behaviors. In particular, the development of algorithms that enables computers to automatically process texts and natural languages has always been one of the most challenges.
Such algorithms are even more necessary in the era of "Big Data". A large amount of valuable information are put on the web everyday, which are in various forms including news, blogs, images, music, videos, opinions, links, etc. The needs of intelligent algorithms to discover the hidden knowledge/semantics from those huge data sources are increasingly arising. Hence a significant progress in the development of intelligent algorithms promises to have a strong impact on various applications ranging from information retrieval, recommendation systems, to computational social science, bioinformatics, forensics, history, and politics.
In this talk, I will discuss one of the most efficient ways to fulfill those needs, which is probabilistic topic modeling. I will discuss from the very basic concepts, basic models, to some challenges and recent advances. Some applications are also discussed.
Short Bio: Khoat Than is currently the Head of Data Science Laboratory, School of Information and Communication Technology, Hanoi University of Science and Technology. He received Ph.D. (2013) from Japan Advanced Institute of Science and Technology. His recent research interests include topic modeling, semantic embeddings, stochastic optimization, machine learning, big data, representation learning. His recent research has been receiving funds from various sources, including NAFOSTED (VN), AFOSR (US), ONRG (US).