We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motio...
: We present a business process performance evaluation approach based on a hierarchy of interacting analytical performance models from semanticoriented key performance indicator mo...
Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic fac...
: This paper presents an approach for the automated extraction of process patterns from Event-driven Process Chain (EPC) models in engineering domains. The manually extraction of p...
As order dependencies between process tasks can get complex, it is easy to make mistakes in process model design, especially behavioral ones such as deadlocks. Notions such as soun...
Mauro Gambini, Marcello La Rosa, Sara Migliorini, ...