  We propose a robust framework for interpretable, few-shot analysis of
non-stationary sequential data based on flexible graphical models to express
the structured distribution of sequential events, using prototype radial basis
function (RBF) neural network emissions. A motivational link is demonstrated
between prototypical neural network architectures for few-shot learning and the
proposed RBF network infinite hidden Markov model (RBF-iHMM). We show that RBF
networks can be efficiently specified via prototypes allowing us to express
complex nonstationary patterns, while hidden Markov models are used to infer
principled high-level Markov dynamics. The utility of the framework is
demonstrated on biomedical signal processing applications such as automated
seizure detection from EEG data where RBF networks achieve state-of-the-art
performance using a fraction of the data needed to train long-short-term memory
variational autoencoders.
