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» Learning the structure of manifolds using random projections
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ICML
2005
IEEE
14 years 9 months ago
Predicting protein folds with structural repeats using a chain graph model
Protein fold recognition is a key step towards inferring the tertiary structures from amino-acid sequences. Complex folds such as those consisting of interacting structural repeat...
Yan Liu, Eric P. Xing, Jaime G. Carbonell
SODA
2012
ACM
268views Algorithms» more  SODA 2012»
11 years 11 months ago
Analyzing graph structure via linear measurements
We initiate the study of graph sketching, i.e., algorithms that use a limited number of linear measurements of a graph to determine the properties of the graph. While a graph on n...
Kook Jin Ahn, Sudipto Guha, Andrew McGregor
ICCV
2007
IEEE
14 years 10 months ago
Steerable Random Fields
In contrast to traditional Markov random field (MRF) models, we develop a Steerable Random Field (SRF) in which the field potentials are defined in terms of filter responses that ...
Stefan Roth, Michael J. Black
COBUILD
1998
Springer
14 years 21 days ago
The Metaphor of Virtual Rooms in the Cooperative Learning Environment CLear
In the CLear project we develop a cooperative learning system for supporting learning and training processes of co-located and distributed groups. One of the fundamental concepts o...
Hans-Rüdiger Pfister, Christian Schuckmann, J...
ICMCS
2009
IEEE
415views Multimedia» more  ICMCS 2009»
13 years 6 months ago
A new localized superpixel Markov random field for image segmentation
In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel label...
Xiaofeng Wang, Xiao-Ping Zhang