In this paper, we consider the multi-task sparse learning problem under the assumption that the dimensionality diverges with the sample size. The traditional l1/l2 multi-task lass...
Xi Chen, Jingrui He, Rick Lawrence, Jaime G. Carbo...
We propose a simple generative, syntactic language model that conditions on overlapping windows of tree context (or treelets) in the same way that n-gram language models condition...
Abstract Computer vision is full of problems elegantly expressed in terms of energy minimization. We characterize a class of energies with hierarchical costs and propose a novel hi...
Andrew Delong, Lena Gorelick, Olga Veksler, Yuri B...
Controlling a tendon-driven robot like the humanoid Ecce is a difficult task, even more so when its kinematics and its pose are not known precisely. In this paper, we present a vis...
Opportunistic routing aims to improve wireless performance by exploiting communication opportunities arising by chance. A key challenge in opportunistic routing is how to achieve ...
A situated approach to Markovian image segmentation is proposed based on a distributed, decentralized and cooperative strategy for model estimation. According to this approach, th...
We address optimal model estimation for model-based vector quantization for both the constrained resolution (CR) and constrained entropy (CE) cases. To this purpose we derive unde...