We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
Instead of relying completely on machine intelligence in anomaly event analysis and correlation, in this paper, we take one step back and investigate the possibility of a human-int...
Soon Tee Teoh, Kwan-Liu Ma, Shyhtsun Felix Wu, Dan...
In cases involving computer related crime, event oriented evidence such as computer event logs, and telephone call records are coming under increased scrutiny. The amount of techn...
We present a new framework based on walks in a graph for analysis and inference in Gaussian graphical models. The key idea is to decompose the correlation between each pair of var...
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Wil...
We propose a Markov process model for spike-frequency adapting neural ensembles which synthesizes existing mean-adaptation approaches, population density methods, and inhomogeneou...
Eilif Mueller, Lars Buesing, Johannes Schemmel, Ka...