This paper studies a variational Bayesian unmixing algorithm for hyperspectral images based on the standard linear mixing model. Each pixel of the image is modeled as a linear com...
Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourner...
In this paper, we propose a novel statistical capacitance extraction method for interconnects considering process variations. The new method, called statCap, is based on the spect...
In this paper we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. By following...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian mod...
Process variations have become a critical issue in performance verification of high-performance designs. We present a new, statistical timing analysis method that accounts for int...