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IROS
2007
IEEE
157views Robotics» more  IROS 2007»
14 years 4 months ago
A learning framework for generic sensory-motor maps
— We present a new approach to cope with unknown redundant systems. For this we present i) an online algorithm that learns general input-output restrictions and, ii) a method tha...
Manuel Lopes, Bruno D. Damas
DATE
2008
IEEE
121views Hardware» more  DATE 2008»
14 years 4 months ago
A Bridging Fault Model Where Undetectable Faults Imply Logic Redundancy
We define a robust fault model as a model where the existence of an undetectable fault implies the existence of logic redundancy, or more generally, a suboptimality in the synthe...
Irith Pomeranz, Sudhakar M. Reddy
CORR
2010
Springer
53views Education» more  CORR 2010»
13 years 10 months ago
Prequential Plug-In Codes that Achieve Optimal Redundancy Rates even if the Model is Wrong
We analyse the prequential plug-in codes relative to one-parameter exponential families M. We show that if data are sampled i.i.d. from some distribution outside M, then the redund...
Peter Grünwald, Wojciech Kotlowski
SIAMIS
2008
211views more  SIAMIS 2008»
13 years 9 months ago
Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary
Modeling signals by sparse and redundant representations has been drawing considerable attention in recent years. Coupled with the ability to train the dictionary using signal exam...
Michal Aharon, Michael Elad
ICDM
2006
IEEE
183views Data Mining» more  ICDM 2006»
14 years 3 months ago
Accelerating Newton Optimization for Log-Linear Models through Feature Redundancy
— Log-linear models are widely used for labeling feature vectors and graphical models, typically to estimate robust conditional distributions in presence of a large number of pot...
Arpit Mathur, Soumen Chakrabarti