AI/ML Seminar Series

Standard

Weekly Seminar in AI & Machine Learning
Sponsored by Cylance



Apr 2
No Seminar
Apr 9
Bren Hall 4011
1 pm
Sabino Miranda, Ph.D
CONACyT Researcher
Center for Research and Innovation in Information and Communication Technologies


Sentiment Analysis is a research area concerned with the computational analysis of people’s feelings or beliefs expressed in texts such as emotions, opinions, attitudes, appraisals, etc. At the same time, with the growth of social media data (review websites, microblogging sites, etc.) on the Web, Twitter has received particular attention because it is a huge source of opinionated information with potential applications to decision-making tasks from business applications to the analysis of social and political events. In this context, I will present the multilingual and error-robust approaches developed in our group to tackle sentiment analysis as a classification problem, mainly for informal written text such as Twitter. Our approaches have been tested in several benchmark contests such as SemEval (International Workshop on Semantic Evaluation), TASS (Workshop for Sentiment Analysis Focused on Spanish), and PAN (Workshop on Digital Text Forensics).
Apr 16
Bren Hall 4011
1 pm
Professor of Mathematics
University of California, Irvine

A simple way to generate a Boolean function in n variables is to take the sign of some polynomial. Such functions are called polynomial threshold functions. How many low-degree polynomial threshold functions are there? This problem was solved for degree d=1 by Zuev in 1989 and has remained open for any higher degrees, including d=2, since then. In a joint work with Pierre Baldi (UCI), we settled the problem for all degrees d>1. The solution explores connections of Boolean functions to additive combinatorics and high-dimensional probability. This leads to a program of extending random matrix theory to random tensors, which is mostly an uncharted territory at present.
Apr 23
Bren Hall 4011
1 pm
PhD Candidate, Computer Science
Brown University

We develop new representations and algorithms for three-dimensional (3D) scene understanding from images and videos. In cluttered indoor scenes, RGB-D images are typically described by local geometric features of the 3D point cloud. We introduce descriptors that account for 3D camera viewpoint, and use structured learning to perform 3D object detection and room layout prediction. We also extend this work by using latent support surfaces to capture style variations of 3D objects and help detect small objects. Contextual relationships among categories and layout are captured via a cascade of classifiers, leading to holistic scene hypotheses with improved accuracy. In outdoor autonomous driving applications, given two consecutive frames from a pair of stereo cameras, 3D scene flow methods simultaneously estimate the 3D geometry and motion of the observed scene. We incorporate semantic segmentation in a cascaded prediction framework to more accurately model moving objects by iteratively refining segmentation masks, stereo correspondences, 3D rigid motion estimates, and optical flow fields.
Apr 30
Bren Hall 4011
1 pm
PhD Candidate, Computer Science
University of California, Los Angeles

May 7
Bren Hall 4011
1 pm
Assistant Professor
University of Utah

May 14
Bren Hall 4011
1 pm
PhD Candidate, Computer Science
University of California, Irvine

May 21
Bren Hall 4011
1 pm
Senior Researcher
Microsoft Research

May 28
Memorial Day
Jun 4
Bren Hall 4011
1 pm

Jun 11
No Seminar (finals week)