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120
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ACL
2010
14 years 10 months ago
Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition
Graph-based semi-supervised learning (SSL) algorithms have been successfully used to extract class-instance pairs from large unstructured and structured text collections. However,...
Partha Pratim Talukdar, Fernando Pereira
ENDM
2006
70views more  ENDM 2006»
15 years 22 days ago
Quasirandomness in Graphs
Jim Propp's rotor router model is a simple deterministic analogue of a random walk. Instead of distributing chips randomly, it serves the neighbors in a fixed order. We analy...
Benjamin Doerr, Tobias Friedrich
88
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IJVR
2008
166views more  IJVR 2008»
15 years 22 days ago
Context Sensitive Stylesheets for Scene Graphs
Augmented Reality (AR) enables users to visualize synthetic information overlaid on a real video stream. Such visualization is achieved by tools that vary depending on the underlyi...
Erick Méndez, Dieter Schmalstieg
123
Voted
JMLR
2010
158views more  JMLR 2010»
14 years 7 months ago
Topology Selection in Graphical Models of Autoregressive Processes
An algorithm is presented for topology selection in graphical models of autoregressive Gaussian time series. The graph topology of the model represents the sparsity pattern of the...
Jitkomut Songsiri, Lieven Vandenberghe
105
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APPROX
2008
Springer
119views Algorithms» more  APPROX 2008»
15 years 2 months ago
The Complexity of Distinguishing Markov Random Fields
Abstract. Markov random fields are often used to model high dimensional distributions in a number of applied areas. A number of recent papers have studied the problem of reconstruc...
Andrej Bogdanov, Elchanan Mossel, Salil P. Vadhan