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IJCV
2000
133views more  IJCV 2000»
15 years 6 months ago
Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint
We present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise, i.e., each data point having a different covariance matrix. The algor...
Yoram Leedan, Peter Meer
NN
2010
Springer
125views Neural Networks» more  NN 2010»
15 years 5 months ago
Parameter-exploring policy gradients
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
Frank Sehnke, Christian Osendorfer, Thomas Rü...
TIP
2010
123views more  TIP 2010»
15 years 5 months ago
Optimizing Motion Compensated Prediction for Error Resilient Video Coding
—This paper is concerned with optimization of the motion compensated prediction framework to improve the error resilience of video coding for transmission over lossy networks. Fi...
Hua Yang, Kenneth Rose
TIP
2011
84views more  TIP 2011»
15 years 2 months ago
Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising
—The removal of Poisson noise is often performed through the following three-step procedure. First, the noise variance is stabilized by applying the Anscombe root transformation ...
Markku Makitalo, Alessandro Foi
JMLR
2010
184views more  JMLR 2010»
15 years 1 months ago
Sequential Monte Carlo Samplers for Dirichlet Process Mixtures
In this paper, we develop a novel online algorithm based on the Sequential Monte Carlo (SMC) samplers framework for posterior inference in Dirichlet Process Mixtures (DPM) (DelMor...
Yener Ülker, Bilge Günsel, Ali Taylan Ce...