—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
This paper addresses the problem of efficient information theoretic, non-parametric data clustering. We develop a procedure for adapting the cluster memberships of the data pattern...
Robert Jenssen, Deniz Erdogmus, Kenneth E. Hild II...
Abstract—This paper presents a stochastic modelling framework for complex biochemical reaction networks from a component-based perspective. Our approach takes into account the di...
Mila E. Majster-Cederbaum, Nils Semmelrock, Verena...
Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal langu...
Noah Goodman, Vikash K. Mansinghka, Daniel M. Roy,...
In recent years, there has been increasing interest in computational models of biological systems based on various calculi of communicating processes, such as the stochastic pi-ca...