In this paper, we propose fast and efficient techniques to analyze the power grid with accurate modeling of the transistor network. The solution techniques currently available for...
Anand Ramalingam, Giri Devarayanadurg, David Z. Pa...
Lifted inference, handling whole sets of indistinguishable objects together, is critical to the effective application of probabilistic relational models to realistic real world ta...
Kristian Kersting, Youssef El Massaoudi, Fabian Ha...
Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. The aim of this paper is to motivate ...
Component failure in large-scale IT installations is becoming an ever larger problem as the number of components in a single cluster approaches a million. In this paper, we presen...
In this paper, we show the feasibility of real-time flow monitoring with controllable accuracy in today’s IP networks. Our approach is based on Netflow and A-GAP. A-GAP is a prot...