Supervised learning of a parts-based model can be for-
mulated as an optimization problem with a large (exponen-
tial in the number of parts) set of constraints. We show how
thi...
M. Pawan Kumar, Andrew Zisserman, Philip H.S. Torr
The optimization method is one of key issues in discriminative learning of pattern classifiers. This paper proposes a hybrid approach of the Covariance Matrix Adaptation Evolution...
We describe a discriminatively trained sequence alignment model based on the averaged perceptron. In common with other approaches to sequence modeling using perceptrons, and in co...
In this paper we propose a generative statistical approach for the three dimensional (3D) extraction of the branching structure of unfoliaged deciduous trees from urban image seque...
In this paper we present a new algorithm for computing reduced-order models of interconnect which utilizes the dominant controllable subspace of the system. The dominant controlla...