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ICML
2005
IEEE
16 years 6 months ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
ICML
2004
IEEE
16 years 6 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...
WWW
2008
ACM
16 years 6 months ago
Video suggestion and discovery for youtube: taking random walks through the view graph
The rapid growth of the number of videos in YouTube provides enormous potential for users to find content of interest to them. Unfortunately, given the difficulty of searching vid...
Shumeet Baluja, Rohan Seth, D. Sivakumar, Yushi Ji...
ICSE
2007
IEEE-ACM
16 years 5 months ago
Parallel Randomized State-Space Search
Model checkers search the space of possible program behaviors to detect errors and to demonstrate their absence. Despite major advances in reduction and optimization techniques, s...
Matthew B. Dwyer, Sebastian G. Elbaum, Suzette Per...
CVPR
2010
IEEE
16 years 1 months ago
Efficient Piecewise Learning for Conditional Random Fields
Conditional Random Field models have proved effective for several low-level computer vision problems. Inference in these models involves solving a combinatorial optimization probl...
Karteek Alahari, Phil Torr