Modelling Program's Performance with Gaussian Mixtures for Parametric Statistics

Modelling facial colour and identity with Gaussian mixtures

An integrated system for the acquisition, normalisation and recognition of moving faces in dynamic scenes is introduced. Four face recognition tasks are deened and it is argued that modelling person-speciic probability densities in a generic face space using mixture models provides a technique applicable to all four tasks. The use of Gaussian colour mixtures for face detection and tracking is a...

Optimal Non-Parametric Prediction Intervals for Order Statistics with Random Sample Size

‎ In many experiments, such as biology and quality control problems, sample size cannot always be considered as a constant value. Therefore, the problem of predicting future data when the sample size is an integer-valued random variable can be an important issue. This paper describes the prediction problem of future order statistics based on upper and lower records. Two different cases for the ...

Performance Modelling for Task-parallel Programs

Many applications from scientific computing and physical simulations can benefit from a mixed task and data parallel implementation on parallel machines with a distributed memory organization, but it may also be the case that a pure data parallel implementation leads to faster execution times. Since the effort for writing a mixed task and data parallel implementation is large, it would be usefu...

Parametric trajectory mixtures for LVCSR

Parametric trajectory models explicitly represent the temporal evolution of the speech features as a Gaussian process with time-varying parameters. HMMs are a special case of such models, one in which the trajectory constraints in the speech segment are ignored by the assumption of conditional independence across frames within the segment. In this paper, we investigate in detail some extensions...

Hierarchical Gaussian Process Mixtures for