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AAAI
2006
13 years 10 months ago
Performing Incremental Bayesian Inference by Dynamic Model Counting
The ability to update the structure of a Bayesian network when new data becomes available is crucial for building adaptive systems. Recent work by Sang, Beame, and Kautz (AAAI 200...
Wei Li 0002, Peter van Beek, Pascal Poupart
BMVC
1998
13 years 10 months ago
Autocalibration in the Presence of Critical Motions
Autocalibration is a difficult problem. Not only is its computation very noisesensitive, but there also exist many critical motions that prevent the estimation of some of the came...
David Demirdjian, Gabriela Csurka, Radu Horaud
NPL
1998
175views more  NPL 1998»
13 years 8 months ago
Prediction of Chaotic Time-Series with a Resource-Allocating RBF Network
Abstract. One of the main problems associated with arti cial neural networks online learning methods is the estimation of model order. In this paper, we report about a new approach...
Roman Rosipal, Milos Koska, Igor Farkas
ICCV
2011
IEEE
12 years 9 months ago
Learning to Cluster Using High Order Graphical Models with Latent Variables
This paper proposes a very general max-margin learning framework for distance-based clustering. To this end, it formulates clustering as a high order energy minimization problem w...
Nikos Komodakis
ISPD
2010
ACM
249views Hardware» more  ISPD 2010»
14 years 4 months ago
A matching based decomposer for double patterning lithography
Double Patterning Lithography (DPL) is one of the few hopeful candidate solutions for the lithography for CMOS process beyond 45nm. DPL assigns the patterns less than a certain di...
Yue Xu, Chris Chu