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SIAMIS
2011
14 years 7 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch
CORR
2012
Springer
170views Education» more  CORR 2012»
13 years 8 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
92
Voted
ICCV
2009
IEEE
16 years 5 months ago
Optimizing Parametric Total Variation Models
One of the key factors for the success of recent energy minimization methods is that they seek to compute global solutions. Even for non-convex energy functionals, optimization ...
Petter Strandmark, Fredrik Kahl, Niels Chr. Overga...
COMPGEOM
2009
ACM
15 years 7 months ago
On grids in topological graphs
A topological graph is a graph drawn in the plane with vertices represented by points and edges as arcs connecting its vertices. A k-grid in a topological graph is a pair of edge ...
Eyal Ackerman, Jacob Fox, János Pach, Andre...
CDC
2008
IEEE
134views Control Systems» more  CDC 2008»
15 years 7 months ago
Dynamic vehicle routing with heterogeneous demands
— In this paper we study a variation of the Dynamic Traveling Repairperson Problem (DTRP) in which there are two classes of demands; high priority, and low priority. In the probl...
Stephen L. Smith, Marco Pavone, Francesco Bullo, E...