A series of evolutionary neural network simulations are presented which explore the hypothesis that learning factors can result in the evolution of long periods of parental protec...
The kernel function plays a central role in kernel methods. Most existing methods can only adapt the kernel parameters or the kernel matrix based on empirical data. Recently, Ong e...
Abstract. We are interested in the relationship between learning efficiency and representation in the case of supervised neural networks for pattern classification trained by conti...
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...
In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from th...