81 search results - page 6 / 17 » Learning, Generation and Recognition of Motions by Reference... |
CVPR
2011
IEEE
13 years 3 months ago
2011
IEEE
The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to...
COLT
1994
Springer
13 years 11 months ago
1994
Springer
We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic nite automata...
ICCV
2007
IEEE
14 years 9 months ago
2007
IEEE
We consider the problem of predicting a sequence of real-valued multivariate states from a given measurement sequence. Its typical application in computer vision is the task of mo...
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images re...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common vari...