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ECCV
2008
Springer
15 years 2 days ago
Learning for Optical Flow Using Stochastic Optimization
Abstract. We present a technique for learning the parameters of a continuousstate Markov random field (MRF) model of optical flow, by minimizing the training loss for a set of grou...
Yunpeng Li, Daniel P. Huttenlocher
QRE
2008
140views more  QRE 2008»
13 years 9 months ago
Discrete mixtures of kernels for Kriging-based optimization
: Kriging-based exploration strategies often rely on a single Ordinary Kriging model which parametric covariance kernel is selected a priori or on the basis of an initial data set....
David Ginsbourger, Céline Helbert, Laurent ...
DAC
2009
ACM
14 years 5 months ago
Accurate temperature estimation using noisy thermal sensors
Multicore SOCs rely on runtime thermal measurements using on-chip sensors for DTM. In this paper we address the problem of estimating the actual temperature of on-chip thermal sen...
Yufu Zhang, Ankur Srivastava
NIPS
1998
13 years 11 months ago
Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm
We present the CEM (Conditional Expectation Maximization) algorithm as an extension of the EM (Expectation Maximization) algorithm to conditional density estimation under missing ...
Tony Jebara, Alex Pentland
BTW
2005
Springer
113views Database» more  BTW 2005»
14 years 3 months ago
A Learning Optimizer for a Federated Database Management System
: Optimizers in modern DBMSs utilize a cost model to choose an efficient query execution plan (QEP) among all possible ones for a given query. The accuracy of the cost estimates de...
Stephan Ewen, Michael Ortega-Binderberger, Volker ...