Krylov-subspace based methods for generating low-order models of complicated interconnect are extremely effective, but there is no optimality theory for the resulting models. Alte...
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In ou...
Although tabular reinforcement learning (RL) methods have been proved to converge to an optimal policy, the combination of particular conventional reinforcement learning techniques...
The representation of moving geometry entities is an important issue in the fields of CAD/CAM and robotics motion design. We present a method to interpolate the moving frame homog...
: In this work we introduce an iterative method that deforms brain models built from tomographic images. The deformation is used for normalization purposes: individual models are d...