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...
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
Abstract-- Recent results in networked control systems indicate substantial benefits of event-based control compared to conventional designs. This paper identifies structural prope...
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 ...
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...