[Colloquium] Talk Reminder: Tomorrow at 11a.m. at TTIC

[Dawn Ellis]

When:  Friday, April 26th at 11a.m.

Where:  TTIC-6045 S. Kenwood Ave. Room 526

Speaker: Paul Vernaza (CMU)

Title:

Efficient analysis and synthesis of high-dimensional motion sequences via
continuous symmetries

Abstract:

Many important problems in fields as diverse as robotics, optimal control,
machine learning, computer vision, and graphics can be framed in terms of
reasoning about motion in high-dimensional spaces; yet these problems are
most often approached by very disparate methods. In this talk, I will share
some of my recent work on developing unified tools for reasoning about
high-dimensional motion that advance the state-of-the-art both in motion
planning and machine learning applications.  The key to these methods is
finding continuous symmetries that can be exploited to efficiently solve
the dynamic programming (DP) problems that arise in these different
applications.

In motion planning, this idea leads to the Learning Dimensional Descent
(LDD) algorithm, which performs sequential DP to explore
intelligently-chosen variations of a path; these variations are informed by
an initial learning step that automatically finds symmetries in a given
problem domain.  Similarly, in machine learning applications, symmetries
can be exploited to efficiently solve DP for the partition function of a
MaxEnt distribution over continuous paths.  This enables MaxEnt learning
for high-dimensional path distributions, which is intractable in the
general case.  Finally, I will present ongoing work on applying powerful
techniques from computational physics to solve scalability and slow
convergence issues in these continuous MaxEnt inference problems.

Host:  Raquel Urtasun, rurtasun at ttic.edu
-- 
*Dawn Ellis*
Administrative Assistant
773-834-1757
dellis at ttic.edu

TTIC
6045 S. Kenwood Ave.
Chicago, IL. 60637
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