Continuous Time Recurrent Neural Networks (CTRNNs) have previously been proposed as an enabling paradigm for evolving analog electrical circuits to serve as controllers for physica...
— This paper deals with stabilization of networked control systems (NCS) affected by uncertain time-varying delays and data packet dropouts. We point out that such network effect...
Both inherently sequential code and limitations of analysis techniques prevent full parallelization of many applications by parallelizing compilers. Amdahl's Law tells us tha...
— In this article, we investigate a new class of control problems called Ensemble Control, a notion coming from the study of complex spin dynamics in Nuclear Magnetic Resonance (...
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...