Abstract: Recent advances in deep neural networks (DNNs) have revolutionized fields such as natural language processing, computer vision, and robotics, while also recently impacting other fields such as biology, computational mechanics, and health care.
Many problems in these domains require a joint model to describe the correlation among a set of variables, such as the correlation of words in a translated sentence. Classically, these correlations were modeled using graphical models (such as Markov random fields) defined over the variables. This talk will show how we can leverage DNNs to describe these correlations using energy-based models (EBMs). I will also show how we can use EBMs for different problem settings and discuss their promises and challenges.
Bio: Pedram Rooshenas is an assistant professor in the Department of Computer Science at UNC Charlotte. Prior to joining UNC Charlotte, he was a post-doctoral research fellow at the College of Information and Computer Sciences, University of Massachusetts, Amherst working with Prof. Andrew McCallum. He received his Ph.D. from the Department of Computer Science at the University of Oregon in 2017 with a dissertation on “Learning Tractable Graphical Models”. He is also the author of the LIBRA toolkit for learning and inference in graphical models. Pedram’s research interests are focused on the practical use of machine learning in complex problems, including innovations in graphical models, structured prediction, and deep learning.