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» Sampling Bounds for Stochastic Optimization
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TIT
2002
86views more  TIT 2002»
13 years 9 months ago
Lagrangian empirical design of variable-rate vector quantizers: consistency and convergence rates
Abstract--The Lagrangian formulation of variable-rate vector quantization is known to yield useful necessary conditions for quantizer optimality and generalized Lloyd algorithms fo...
Tamás Linder
ECCV
2010
Springer
13 years 7 months ago
Aligning Spatio-Temporal Signals on a Special Manifold
We investigate the spatio-temporal alignment of videos or features/signals extracted from them. Specifically, we formally define an alignment manifold and formulate the alignment p...
Ruonan Li, Rama Chellappa
JMLR
2010
165views more  JMLR 2010»
13 years 4 months ago
Learning with Blocks: Composite Likelihood and Contrastive Divergence
Composite likelihood methods provide a wide spectrum of computationally efficient techniques for statistical tasks such as parameter estimation and model selection. In this paper,...
Arthur Asuncion, Qiang Liu, Alexander T. Ihler, Pa...
ICML
2007
IEEE
14 years 10 months ago
Asymptotic Bayesian generalization error when training and test distributions are different
In supervised learning, we commonly assume that training and test data are sampled from the same distribution. However, this assumption can be violated in practice and then standa...
Keisuke Yamazaki, Klaus-Robert Müller, Masash...
PLDI
1997
ACM
14 years 2 months ago
Dynamic Feedback: An Effective Technique for Adaptive Computing
This paper presents dynamic feedback, a technique that enables computations to adapt dynamically to different execution environments. A compiler that uses dynamic feedback produce...
Pedro C. Diniz, Martin C. Rinard