15 search results - page 1 / 3 » Learning Gradients: Predictive Models that Infer Geometry an... |
JMLR
2010
12 years 11 months ago
2010
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
ICONIP
2007
13 years 6 months ago
2007
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
AAAI
2008
13 years 7 months ago
2008
Research has shown promise in the design of large scale common sense probabilistic models to infer human state from environmental sensor data. These models have made use of mined ...
ISIPTA
2005
IEEE
13 years 10 months ago
2005
IEEE
This paper addresses the following question: how should we update our beliefs after observing some incomplete data, in order to make credible predictions about new, and possibly i...
JMLR
2010
12 years 11 months ago
2010
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...