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Geometric transformations in deep learning and Bayesian nonparamteric mixture models (by Oren Freifeld, Ben-Gurion University)
June 2 @ 12:00 pm - 1:00 pm IDT
June. 2nd 2019, Sun. 12:00 , Oren Freifeld (webpage).
Location: Nano Building (206), Room B991.
Geometric transformations in deep learning and Bayesian nonparamteric mixture models.
This talk will focus on two of the main research directions at our Vision, Inference, and Learning group: 1) Geometric transformations in deep learning and 2) Bayesian nonparamteric mixture models.
During the talk, which is based on both published and under-review works, I will touch upon applications in computer vision, machine learning, and time-series analysis.
About the speaker:
Oren Freifeld is a faculty member at Ben-Gurion University Computer Science Department. Previously, he was at MIT CSAIL (postdoc), Brown University Applied Math (PhD, ScM), Stanford Electrical Engineering (Visiting PhD Student), and Max Planck Institute for Intelligent Systems (Visiting PhD Student). His research focuses on practical and mathematically-principled tools for high-dimensional data analysis, particularly those that scale gracefully with the data’s size, and that adapt model complexity to the data. He is mostly interested in Bayesian and/or geometric methods, and in problems such as unsupervised learning, motion analysis, segmentation, statistical image models, signal alignment, and deep learning.