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» Learning Generative Models with the Up-Propagation Algorithm
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ICML
2003
IEEE
14 years 9 months ago
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning
We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by i...
Yaakov Engel, Shie Mannor, Ron Meir
CVPR
2004
IEEE
14 years 22 days ago
Modeling Complex Motion by Tracking and Editing Hidden Markov Graphs
In this paper, we propose a generative model for representing complex motion, such as wavy river, dancing fire and dangling cloth. Our generative method consists of four component...
Yizhou Wang, Song Chun Zhu
AAAI
2008
13 years 11 months ago
Multi-HDP: A Non Parametric Bayesian Model for Tensor Factorization
Matrix factorization algorithms are frequently used in the machine learning community to find low dimensional representations of data. We introduce a novel generative Bayesian pro...
Ian Porteous, Evgeniy Bart, Max Welling
MM
2004
ACM
170views Multimedia» more  MM 2004»
14 years 2 months ago
Effective automatic image annotation via a coherent language model and active learning
Image annotations allow users to access a large image database with textual queries. There have been several studies on automatic image annotation utilizing machine learning techn...
Rong Jin, Joyce Y. Chai, Luo Si
GRAPHITE
2007
ACM
14 years 26 days ago
Compact and efficient generation of radiance transfer for dynamically articulated characters
We present a data-driven technique for generating the precomputed radiance transfer vectors of an animated character as a function of its joint angles. We learn a linear model for...
Derek Nowrouzezahrai, Patricio D. Simari, Evangelo...