Abstract. In this paper, we review the task of inductive process modeling, which uses domain knowledge to compose explanatory models of continuous dynamic systems. Next we discuss ...
Will Bridewell, Pat Langley, Steve Racunas, Stuart...
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
A dynamical model for retinal processing is presented. The model describes the output of retinal ganglion cells whose receptive field is composed of a center and a surround combi...
Bridging the gap between model-based design and platformbased implementation is one of the critical challenges for embedded software systems. In the context of embedded control sy...
Truong Nghiem, George J. Pappas, Rajeev Alur, Anto...
The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techn...