This site uses cookies to deliver our services and to ensure you get the best experience. By continuing to use this site, you consent to our use of cookies and acknowledge that you have read and understand our Privacy Policy, Cookie Policy, and Terms
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...