Identifiability becomes an essential requirement for learning machines when the models contain physically interpretable parameters. This paper presents two approaches to examining...
Reduced models have long been used as a tool for the analysis of the complex activity taking place in neurons and their coupled networks. Recent advanced in experimental and theore...
Normal fuzzy CMAC neural network performs well because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires hu...
Floriberto Ortiz Rodriguez, Wen Yu, Marco A. Moren...
Optical diffusion tomography attempts to reconstruct an object cross section from measurements of scattered and attenuated light. While Bayesian approaches are well suited to this...
Jong Chul Ye, Charles A. Bouman, Rick P. Millane, ...
Abstract. In the context of nonlinear regression, we consider the problem of explaining a variable y from a vector x of explanatory variables and from a vector t of conditionning v...