Class and Thursday seminar
Adam Kalai
There will be class today on noise-tolerant learning, statistical
queries, and learning parities.
I also wanted to point out to those students who don't get such
emails, that there is an interesting-looking machine learning/vision
lecture on Thursday. It looks like a chance to get exposure to a
different style of machine learning, more like Eran Segal's
Probabilistic Graphical Models course.
---------- Forwarded message ----------
From: Ronen Basri <ronen...@weizmann.ac.il>
Subject: Seminar: Gal Elidan, Thursday, Jan 11 12:00 at Room 1
Dear All,
I wanted to turn your attention to the following talk by Gal Elidan this
Thursday. Gal's research deals with machine learning and particularly with
probabilistic graphical models. He has done PhD with Nir Friedman at the HU
and postdoc with Daphne Keller at Stanford.
Ronen
The Weizmann Institute of Science
Faculty of Mathematics and Computer Science
Vision and Robotics Seminar
Gal Elidan
Computer Science Department
Stanford University
will speak on
Probabilistic shape for outlining objects
Abstract:
High-level object class recognition involves both the task of classification
and that of semantic localization (identifying an object's outline). While
many recent works focus on classification, the task of localization has
received much less attention. In this talk I will present a unified approach
for addressing these two tasks in concert, and focus on generic machine
learning tasks that arise in this context.
I will first pose object outlining as probabilistic inference based on an
explicit shape model. To cope with the computational complexity of this
problem, I will develop a template algorithm that significantly improves the
performance of an important and popular family of approximate inference
methods. I will show how our inference-based approach achieves
classification rates that are competitive with state-of-the-art methods
while at the same time provides us with accurate outlines that are
semantically meaningful.
I will then consider the challenges of learning with weaker supervision and
with few samples. I will present an approach that builds on the tools used
for object outlining to learn semantic shape models from simple outlines,
and show that the models learned are competitive with those learned with
full supervision. Finally, I will present a general purpose hierarchical
framework for transfer learning between joint distribution representations
of related classes. I will show how this approach allows us to learn better
class-specific shape models from few samples by learning jointly from
several related quadruped classes.
The lecture will take place in the
Lecture Hall, Room 1, Ziskind Building
on Thursday, January 11, 2007
12:00 - 13:00