A self-organizing neural network for learning and recall of complex temporal sequences is proposed. we consider a single sequence with repeated items, or several sequences with a c...
Recent work on background subtraction has shown developments on two major fronts. In one, there has been increasing sophistication of probabilistic models, from mixtures of Gaussi...
Manjunath Narayana, Allen R. Hanson, Erik G. Learn...
If all features causing heterogeneity were observed, a mixture of experts approach (Jacobs et al., 1991) is likely to be superior to using a single model. When unobserved or very n...
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
Abstract. We propose a purely implicit solution to the contextual assumption generation problem in assume-guarantee reasoning. Instead of improving the L∗ algorithm — a learnin...
Yu-Fang Chen, Edmund M. Clarke, Azadeh Farzan, Min...