This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for ...
Abstract. This work addresses a class of total-variation based multilabeling problems over a spatially continuous image domain, where the data fidelity term can be any bounded fun...
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
The clock signal and clock skew become more and more important for the circuit performance. Since there are salient shortcomings in the conventional topology construction algorith...
This paper discusses an extended adaptive supply network simulation model that explicitly captures growth (in terms of change in size over time, and birth and death) based on Utte...