We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. ...
A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a ...
We consider the problem of broadcasting a large message in a large scale distributed platform. The message must be sent from a source node, with the help of the receiving peers whi...
We present an approach for large-scale modeling of parametric surfaces using spherical harmonics (SHs). A standard least square fitting (LSF) method for SH expansion is not scala...
A methodology and its associated algorithms are presented for mapping a novel, field-based vehicular mobility model onto graphical processing unit computational platform for simul...
Kalyan S. Perumalla, Brandon G. Aaby, Srikanth B. ...