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ECCV
2006
Springer
16 years 1 months ago
Efficient Belief Propagation with Learned Higher-Order Markov Random Fields
Belief propagation (BP) has become widely used for low-level vision problems and various inference techniques have been proposed for loopy graphs. These methods typically rely on a...
Xiangyang Lan, Stefan Roth, Daniel P. Huttenlocher...
CIKM
2009
Springer
15 years 6 months ago
A social recommendation framework based on multi-scale continuous conditional random fields
This paper addresses the issue of social recommendation based on collaborative filtering (CF) algorithms. Social recommendation emphasizes utilizing various attributes informatio...
Xin Xin, Irwin King, Hongbo Deng, Michael R. Lyu
KDD
2010
ACM
274views Data Mining» more  KDD 2010»
15 years 3 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
BMCBI
2007
233views more  BMCBI 2007»
14 years 12 months ago
160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA)
Background: To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching s...
Isaac T. S. Li, Warren Shum, Kevin Truong
PAMI
2010
215views more  PAMI 2010»
14 years 10 months ago
Fusion Moves for Markov Random Field Optimization
—The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one p...
Victor S. Lempitsky, Carsten Rother, Stefan Roth, ...