In this paper we propose a novel general framework for unsupervised model adaptation. Our method is based on entropy which has been used previously as a regularizer in semi-superv...
Ariya Rastrow, Frederick Jelinek, Abhinav Sethy, B...
- This paper demonstrates how methods borrowed from information fusion can improve the performance of a classifier by constructing (i.e., fusing) new features that are combinations...
There have been several recent advancements in Machine Learning community on the Entity Matching (EM) problem. However, their lack of scalability has prevented them from being app...
Vibhor Rastogi, Nilesh N. Dalvi, Minos N. Garofala...
Abstract People typically move and act under the constraints of an environment, making human behavior strongly place-dependent. Motion patterns, the places and the rates at which p...
Matthias Luber, Gian Diego Tipaldi, Kai Oliver Arr...
We study the problem of image denoising where images are assumed to be samples from low dimensional (sub)manifolds. We propose the algorithm of locally linear denoising. The algor...