This paper presents a framework for directly addressing issues arising from self-occlusions and ambiguities due to the lack of depth information in vector-based representations. V...
Dyadic data refers to a domain with two nite sets of objects in which observations are made for dyads, i.e., pairs with one element from either set. This type of data arises natur...
For almost a decade we have been working at developing and using template-based models for coarse-grained parallel computing. Our initial system, FrameWorks, was positively receiv...
For many types of machine learning algorithms, one can compute the statistically optimal" way to select training data. In this paper, we review how optimal data selection tec...
David A. Cohn, Zoubin Ghahramani, Michael I. Jorda...
This paper presents a computational model ofverb acquisitionwhich uses what we willcallthe principle of structured overeommitment to eliminate the need for negative evidence. The ...