We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
The Student’s-t hidden Markov model (SHMM) has been recently proposed as a robust to outliers form of conventional continuous density hidden Markov models, trained by means of t...
We propose an unsupervised inference procedure for audio source separation. Components in nonnegative matrix factorization (NMF) are grouped automatically in audio sources via a p...
Despite the surge of interest in data reduction techniques over the past years, no method has been proposed to date that can always achieve approximation quality preferable to that...
We present an automatic error-detection approach that combines static checking and concrete test-case generation. Our approach consists of taking the abstract error conditions inf...