Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including...
We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov Random Fields (MRFs) which support ef...
Dragomir Anguelov, Benjamin Taskar, Vassil Chatalb...
We propose a new framework for multi-object segmentation of deep brain structures, which have significant shape variations and relatively small sizes in medical brain images. In th...
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...