Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
Imitation in artificial systems involves a number of important aspects, such as extracting the relevant features of the demonstrated behaviour, inverse mapping observations, and e...
We propose dynamical systems trees (DSTs) as a flexible model for describing multiple processes that interact via a hierarchy of aggregating processes. DSTs extend nonlinear dynam...
Background: Translational research requires taking basic science observations and developing them into clinically useful tests and therapeutics. We have developed a process to dev...
Robert Kim, Francesca Demichelis, Jeffery Tang, Al...