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UAI
1996
13 years 8 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
ANOR
2007
80views more  ANOR 2007»
13 years 7 months ago
The minimum shift design problem
The min-SHIFT DESIGN problem (MSD) is an important scheduling problem that needs to be solved in many industrial contexts. The issue is to find a minimum number of shifts and the...
Luca Di Gaspero, Johannes Gärtner, Guy Kortsa...
CGF
2010
147views more  CGF 2010»
13 years 7 months ago
Resampling Strategies for Deforming MLS Surfaces
Moving-Least-Squares (MLS) Surfaces undergoing large deformations need periodic regeneration of the point set (point-set resampling) so as to keep the point-set density quasi-unif...
Joao Paulo Gois, Gustavo C. Buscaglia
JMLR
2008
209views more  JMLR 2008»
13 years 7 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
TSP
2008
140views more  TSP 2008»
13 years 7 months ago
On the Dual-Tree Complex Wavelet Packet and M-Band Transforms
The two-band discrete wavelet transform (DWT) provides an octave-band analysis in the frequency domain, but this might not be "optimal" for a given signal. The discrete w...
I. Bayram, Ivan W. Selesnick