Colloquium Details

Progress in Object-Centric Machine Learning

Speaker: Leonidas Guibas, Stanford University

Location: 60 Fifth Avenue 150

Date: October 21, 2019, 2 p.m.

Host: Dennis Shasha


Deep knowledge of the world is necessary if we are to augment real
scenes with virtual entities, or to have autonomous and intelligent
agents and artifacts that can assist us in everyday activities -- or
even carry out tasks entirely independently. One way to factorize the
complexity of the world is to associate information and knowledge with
stable entities, animate or inanimate, such as a person or a vehicle,
etc -- things we can generically call objects. In this talk I'll survey
a number of recent efforts whose aim is to create and annotate reference
representations for objects based on 3D models, with the aim of
delivering information to new observations, as needed. The information
may relate to object geometry, appearance, articulation, materials,
physical properties, affordances, or functionality.

One challenge of the 3D world is that 3D data typically come as point
clouds or meshes, which do not have the regular grid structure of image
or video data. This makes it challenging to apply the highly successful
convolutional deep architectures (CNNs) to 3D data, as CNNs heavily
depend on neighborhood regularity for weight sharing and other
optimizations. The talk will illustrate deep architectures capable of
processing irregular 3D geometry  for tasks such as object extraction
from scenes, geometric primitive fitting, inferring object function from
observations, and learning to differentiate objects through language.
Tools towards these goals include canonical spaces for objects and
representations of their compositional structure, as well as
multi-objective training and learned communication patterns in

Speaker Bio:

Leonidas Guibas is the Paul Pigott Professor of Computer Science (and by
courtesy, Electrical Engineering) at Stanford University, where he heads
the Geometric Computation group.  Dr. Guibas obtained his Ph.D. from
Stanford University  under the supervision of Donald Knuth. His main
subsequent employers were Xerox PARC, DEC/SRC, MIT, and Stanford. He is
a member and past acting director of the Stanford Artificial
Intelligence Laboratory and a member of the Computer Graphics
Laboratory, the Institute for Computational and Mathematical Engineering
(iCME) and the Bio-X program. Dr. Guibas has been elected to the US
National Academy of Engineering and the American Academy of Arts and
Sciences, and is an ACM Fellow, an IEEE Fellow and winner of the ACM
Allen Newell award and the ICCV Helmholtz prize. He is also a recent
recipient of a five-year DoD Vannevar Bush Faculty Fellowship.


In-person attendance only available to those with active NYU ID cards.

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