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UAI
2004
15 years 4 months ago
Exponential Families for Conditional Random Fields
In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decompo...
Yasemin Altun, Alexander J. Smola, Thomas Hofmann
93
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NIPS
1998
15 years 4 months ago
Learning a Continuous Hidden Variable Model for Binary Data
A directed generative model for binary data using a small number of hidden continuous units is investigated. A clipping nonlinearity distinguishes the model from conventional prin...
Daniel D. Lee, Haim Sompolinsky
NIPS
1996
15 years 4 months ago
Continuous Sigmoidal Belief Networks Trained using Slice Sampling
Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean G...
Brendan J. Frey
ICASSP
2010
IEEE
15 years 2 months ago
3D shape estimation from silhouettes using mean-shift
In this article, a novel method to accurately estimate 3D surface of objects of interest is proposed. Each ray projected from 2D image plane to 3D space is modelled with the Gauss...
Donghoon Kim, Jonathan Ruttle, Rozenn Dahyot
ADCM
2007
119views more  ADCM 2007»
15 years 2 months ago
Causality properties of refinable functions and sequences
We show that the scale-space operators defined by a class of refinable kernels satisfy a version of the causality property, and a sequence of such operators converges to the cor...
Say Song Goh, Tim N. T. Goodman, S. L. Lee