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Events for December 11, 2007
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Multi-scale Adaptive Image Representations
Tue, Dec 11, 2007 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Doru C. Balcan,
Carnegie Mellon UniversityAbstract:
Multi-scale representations, such as those based on wavelets, have been successful in efficiently
describing intrinsic structure in images, and consequently have lead to the emergence of excellent
image coders. Nevertheless, using any fixed representation limits the best achievable performance
when encoding images of a given class, because all statistical information about that class is simply
ignored. An adaptive representation would then be more appropriate in this setting. One such
example is independent component analysis (ICA), a statistical method that computes a linear
representation whose coefficients have minimum entropy.
In this talk, I will introduce a hybrid image representation method called Multi-scale ICA, which
derives an adaptive basis for each of the wavelet decomposition sub-bands. A direct comparison of
the rate-distortion curves obtained by coefficient scalar quantization to various levels of precision
shows the improvement in terms of efficiency over the wavelet representation. One other merit of this
approach is its potential use to derive adaptive representations for large-size images, where existing
methods fail because of computational and sample complexity limitations. We can therefore interpret
the proposed method both as a nonparametric adaptive extension of wavelet representations, and as
a multi-scale generalization of ICA. This is joint work with Michael Lewicki.Speaker Bio:
Doru C. Balcan received the B.S. degree in computer science, in 2000, and the M.S. degree in
applied computer science, in 2002, from the Faculty of Mathematics, University of Bucharest,
Romania. He is currently pursuing Ph.D. studies in computer science at Carnegie Mellon University,
Pittsburgh, PA. His research is focused on developing algorithms for efficient and robust signal
processing and coding. More exactly, he is interested in techniques that exploit the mathematical
structure of problems commonly occurring in image and audio encoding, to produce representations
that are compact, resilient to noise, and fast to compute.Hosted by: Prof: C-C Jay KuoLocation: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia Veal
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CS Colloquia: Interactive and Intuitive Appearance Design
Tue, Dec 11, 2007 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Title: Interactive and Intuitive Appearance Design Speaker : Professor Fabio Pellacini - Dartmouth CollegeABSTRACT:
The appearance of objects comes from the interaction of scene lighting and
surface materials, whose careful definition is necessary to achieve the
remarkable sophistication of today's synthetic imagery.
Currently, appearance design is one of the remaining roadblocks for a
ubiquitous use of computer-generated imagery, since slow user feedback and
cumbersome user interfaces make the process significantly time consuming for
expert designers, and beyond the reach of novices.In this talk, I will present our recent results in rendering accurate
lighting for complex environments where we achieve interactivity by
developing new approximation algorithms that can take advantage of inherent
properties of lighting and of today's commodity hardware architectures.
These algorithms completely change the workflow of artists from an offline
to a fully interactive process.
I will also show results from algorithms that build on this interactivity to
support intuitive user interfaces for appearance design that drastically
simplify the time require for designing appearance.BIO:
Fabio Pellacini is an assistant professor in computer science at Dartmouth
College. His research focuses on algorithms for interactive, high-quality
rendering of complex environments and for artist-friendly material and
lighting design to support more effective content creation.Prior to joining academia, Pellacini worked at Pixar Animation Studios on
lighting algorithms, where he received credits on various movie productions.Pellacini received his Laurea degree in physics from the University of Parma
(Italy), and his M.S. and Ph.D. in computer science from Cornell University.Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Colloquia
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Tutor Days
Tue, Dec 11, 2007 @ 02:00 PM - 05:00 PM
Viterbi School of Engineering Student Affairs
Student Activity
Tutor Days are study sessions hold on stop days in the RTH Classrooms! Come and study with your peers, classmates and upper division study partners. There will be care packages for all participants!Monday:
AME 101, 201; BISC 120; BME 101, CE 205, MATH 125, 126, 226Tuesday:
CHEM 105a; CSCI 101, 102; ISE 220, PHYS 151, 152No RSVP Necessary! Feel free to email viterbi.varc@usc.edu with any questions!
Location: Ronald Tutor Hall of Engineering (RTH) - Classrooms
Audiences: Undergraduate Students
Contact: VARC, CED & WIE
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CS Colloquia: Mechanism Design, Machine Learning, and Pricing Problems
Tue, Dec 11, 2007 @ 04:00 PM - 05:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Title: Mechanism Design, Machine Learning, and Pricing ProblemsSpeaker: Maria-Florina Balcan(CMU)ABSTRACT:
In this work, we make an explicit connection between machine learning and
mechanism design. In doing so, we obtain a unified approach for considering a
variety of profit maximizing mechanism design problems, including many that
have been previously considered in the literature. In particular, we use
techniques from sample complexity in machine learning theory to reduce
problems of incentive compatible mechanism design to standard algorithmic
questions. We apply these results to a wide variety of revenue-maximizing
pricing problems, including the problem of auctioning a digital good, the
attribute auction problem, and the problem of item pricing in unlimited supply
combinatorial auctions. From a learning perspective, these settings present
several unique challenges: the loss function is discontinuous and asymmetric,
and the range of bidders' valuations may be large. This talk is based on joint work with Avrim Blum, Jason Hartline, and Yishay
Mansour.BIO:
Maria-Florina Balcan is a Ph.D. candidate at Carnegie Mellon University under
the supervision of Avrim Blum. She received B.S. and M.S. degrees from the
Faculty of Mathematics, University of Bucharest, Romania. Her main research
interests are Computational and Statistical Machine Learning, Computational
Aspects in Economics and Game Theory, and Algorithms. She is a recipient of
the IBM PhD Fellowship.Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Colloquia
