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NECO
2002
104views more  NECO 2002»
15 years 3 months ago
An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models
A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
Harri Valpola, Juha Karhunen
COGSR
2011
99views more  COGSR 2011»
14 years 11 months ago
A biologically realistic cleanup memory: Autoassociation in spiking neurons
Methods for cleaning up (or recognizing) states of a neural network are crucial for the functioning of many neural cognitive models. For example, Vector Symbolic Architectures pro...
Terrence C. Stewart, Yichuan Tang, Chris Eliasmith
122
Voted
CDC
2010
IEEE
103views Control Systems» more  CDC 2010»
14 years 11 months ago
Structural characterization of optimal event-based controllers for linear stochastic systems
Abstract-- Recent results in networked control systems indicate substantial benefits of event-based control compared to conventional designs. This paper identifies structural prope...
Adam Molin, Sandra Hirche
INTERSPEECH
2010
14 years 10 months ago
Using a DBN to integrate sparse classification and GMM-based ASR
The performance of an HMM-based speech recognizer using MFCCs as input is known to degrade dramatically in noisy conditions. Recently, an exemplar-based noise robust ASR approach,...
Yang Sun, Jort F. Gemmeke, Bert Cranen, Louis ten ...
INFOCOM
2012
IEEE
14 years 2 months ago
Expected loss bounds for authentication in constrained channels
We derive bounds on the expected loss for authentication protocols in channels which are constrained due to noisy conditions and communication costs. This is motivated by a numbe...
Christos Dimitrakakis, Aikaterini Mitrokotsa, Serg...