This poster shows an artificial neural network capable of learning a temporal sequence. Directly inspired from a hippocampus model [Banquet et al, 1998], this architecture allows ...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
In social networks, nodes correspond to entities and edges to links between them. In most of the cases, nodes are also associated with a set of features. Noise, missing values or ...
The utility of simulations and analysis heavily relies on good models of network traffic. While network traffic constantly changing over time, existing approaches typically take y...
In an attempt to cope with time-varying workload, traditional adaptive Time Warp protocols are designed to react in response to performance changes by altering control parameter c...