In this paper we present a framework for using multi-layer perceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is handled by linearizing it using a Gauss–Hermite quadrature at the hidden neurons. This yields an accurate approximation for cases of large posterior variance. The method can be used to derive nonlinear counterparts for ...

Each year perhaps millions of young people face the following dilemma: should I continue my education or rather start working with already acquired skills. Right decision must take into account somebody’s own abilities, accessibility to education institutions, competition, and potential benefits. A multi-agent, evolutionary model of this dilemma predicts a transition between stratified and homo...

This paper summarizes recent advances in PRLM language recognition within the context of the NIST 2007 LR evaluations (LRE). We present a comparison of binary decision tree (BT) vs. N -gram models when adaptation from a universal (background) model (UBM) is used, we introduce multi-models— anchor-model-like approach to scoring, and we adopt the framework of intersession variation using factor a...