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JMIV
2010
131views more  JMIV 2010»
15 years 8 days ago
An SL(2) Invariant Shape Median
Median averaging is a powerful averaging concept on sets of vector data in finite dimensions. A generalization of the median for shapes in the plane is introduced. The underlying ...
Benjamin Berkels, Gina Linkmann, Martin Rumpf
121
Voted
SDM
2009
SIAM
192views Data Mining» more  SDM 2009»
15 years 11 months ago
Mining Cohesive Patterns from Graphs with Feature Vectors.
The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to g...
Arash Rafiey, Flavia Moser, Martin Ester, Recep Co...
CORR
2012
Springer
170views Education» more  CORR 2012»
13 years 9 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
107
Voted
MICCAI
2008
Springer
16 years 3 months ago
A Distributed Spatio-temporal EEG/MEG Inverse Solver
We propose a novel 1 2-norm inverse solver for estimating the sources of EEG/MEG signals. Based on the standard 1-norm inverse solver, the proposed sparse distributed inverse solve...
Wanmei Ou, Polina Golland, Matti Hämäl&a...
108
Voted
CVPR
2010
IEEE
15 years 9 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
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