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IJON
2010
109views more  IJON 2010»
13 years 2 months ago
Variational inference for Student-t MLP models
This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for ...
Hang T. Nguyen, Ian T. Nabney
NIPS
2004
13 years 9 months ago
Unsupervised Variational Bayesian Learning of Nonlinear Models
In this paper we present a framework for using multi-layer perceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is h...
Antti Honkela, Harri Valpola
VLSM
2005
Springer
14 years 1 months ago
Advances in Variational Image Segmentation Using AM-FM Models: Regularized Demodulation and Probabilistic Cue Integration
Current state-of-the-art methods in variational image segmentation using level set methods are able to robustly segment complex textured images in an unsupervised manner. In recent...
Georgios Evangelopoulos, Iasonas Kokkinos, Petros ...
ICPR
2008
IEEE
14 years 9 months ago
Variational Maximum A Posteriori model similarity and dissimilarity matching
A new variational Maximum A Posteriori (MAP) contextual modeling approach is presented that minimizes the product of two ratios: (a) the ratio of the model distribution to the dis...
John Chiverton, Majid Mirmehdi, Xianghua Xie
ICCV
2009
IEEE
15 years 24 days ago
Optimizing Parametric Total Variation Models
One of the key factors for the success of recent energy minimization methods is that they seek to compute global solutions. Even for non-convex energy functionals, optimization ...
Petter Strandmark, Fredrik Kahl, Niels Chr. Overga...