We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradie...
Abstract. This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidat...
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. ...
Detecting instances of unknown categories is an important task for a multitude of problems such as object recognition, event detection, and defect localization. This paper investig...
The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisatio...
Image annotation has been an active research topic in recent years due to its potentially large impact on both image understanding and Web image search. In this paper, we target a...
Xirong Li, Le Chen, Lei Zhang, Fuzong Lin, Wei-Yin...