Structured Prediction and Deep Learning
Speaker: Andrew McCallum, University of Massachusetts Amherst
Location: Warren Weaver Hall 1302
Date: October 21, 2016, 11:30 a.m.
Host: Sam Bowman
Deep neural networks have revolutionized speech recognition, computer vision, natural language processing, and other areas. Dramatic empirical results have been enabled by the networks' ability to learn rich representations of their inputs, where automatic differentiation allows practitioners to explore complex architectures easily. Structured prediction, however, must capture dependencies among the output variables. In this talk I will survey intersections between deep learning and structured prediction, explore the relationships between inference in graphical models and prediction in feed-forward neural networks, and finally introduce our recent work in "structured prediction energy networks" (Belanger and McCallum 2016), which uses a deep architecture to learn rich representations of output dependencies---essentially replacing the factors in the factor graph with a neural network yielding a scalar energy.
Andrew McCallum is a Professor and Director of the Information Extraction and Synthesis Laboratory, as well as Director of Center for Data Science in the College of Information and Computer Science at University of Massachusetts Amherst. He has published over 250 papers in many areas of AI, including natural language processing, machine learning and reinforcement learning; his work has received over 45,000 citations. He obtained his PhD from University of Rochester in 1995 with Dana Ballard and a postdoctoral fellowship from CMU with Tom Mitchell and Sebastian Thrun. In the early 2000's he was Vice President of Research and Development at at WhizBang Labs, a 170-person start-up company that used machine learning for information extraction from the Web. He is a AAAI Fellow, the recipient of the UMass Chancellor's Award for Research and Creative Activity, the UMass NSM Distinguished Research Award, the UMass Lilly Teaching Fellowship, and research awards from Google, IBM, Microsoft, and Yahoo. He was the General Chair for the International Conference on Machine Learning (ICML) 2012, and is the current President of the International Machine Learning Society, as well as member of the editorial board of the Journal of Machine Learning Research. For the past ten years, McCallum has been active in research on statistical machine learning applied to text, especially information extraction, entity resolution, social network analysis, structured prediction, semi-supervised learning, and deep neural networks for knowledge representation. His work on open peer review can be found at http://openreview.net. McCallum's web page is http://www.cs.umass.edu/~mccallum.
In-person attendance only available to those with active NYU ID cards.