Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...
Abstract. Our work is concerned with finding optimum connection strategies in highperformance associative memory models. Taking inspiration from axonal branching in biological neur...
In a previous work, we developed a quasi-efficient maximum likelihood approach for blindly separating stationary, temporally correlated sources modeled by Markov processes. In this...
Abstract. We study Winner-Takes-All and rank based Vector Quantization along the lines of the statistical physics of off-line learning. Typical behavior of the system is obtained w...
Abstract. We present a method to perform model selection based on predictive density in a class of spatio-temporal dynamic generalized linear models for areal data. These models as...