Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
Abstract Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. A...
In a seminal paper, Amari (1998) proved that learning can be made more efficient when one uses the intrinsic Riemannian structure of the algorithms' spaces of parameters to po...
Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alte...
Ivo Couckuyt, Dirk Gorissen, Hamed Rouhani, Eric L...
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging ...
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom D...