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CVPR
2012
IEEE
13 years 9 months ago
From Pictorial Structures to deformable structures
Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connecte...
Silvia Zuffi, Oren Freifeld, Michael J. Black
CVPR
2012
IEEE
13 years 9 months ago
From pixels to physics: Probabilistic color de-rendering
Consumer digital cameras use tone-mapping to produce compact, narrow-gamut images that are nonetheless visually pleasing. In doing so, they discard or distort substantial radiomet...
Ying Xiong, Kate Saenko, Trevor Darrell, Todd Zick...
ICML
2009
IEEE
16 years 7 months ago
PAC-Bayesian learning of linear classifiers
We present a general PAC-Bayes theorem from which all known PAC-Bayes risk bounds are obtained as particular cases. We also propose different learning algorithms for finding linea...
Alexandre Lacasse, François Laviolette, Mar...
KR
2004
Springer
16 years 1 days ago
Learning Probabilistic Relational Planning Rules
To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilist...
Hanna Pasula, Luke S. Zettlemoyer, Leslie Pack Kae...
132
Voted
PG
2003
IEEE
15 years 12 months ago
Machine Learning for Computer Graphics: A Manifesto and Tutorial
I argue that computer graphics can benefit from a deeper use of machine learning techniques. I give an overview of what learning has to offer the graphics community, with an emph...
Aaron Hertzmann