We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
The analysis of discrete stochastic models such as generally distributed stochastic Petri nets can be done using state space-based methods. The behavior of the model is described ...
In this paper, we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. Within the h...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
Abstract. We consider the distributed construction of a minimum weight 2edge-connected spanning subgraph (2-ECSS) of a given weighted or unweighted graph. A 2-ECSS of a graph is a ...
Sven Oliver Krumke, Peter Merz, Tim Nonner, Kathar...