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JCNS
2010
104views more  JCNS 2010»
13 years 7 months ago
A new look at state-space models for neural data
State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely o...
Liam Paninski, Yashar Ahmadian, Daniel Gil Ferreir...
CVPR
2009
IEEE
15 years 3 months ago
Efficient Representation of Local Geometry for Large Scale Object Retrieval
State of the art methods for image and object re- trieval exploit both appearance (via visual words) and local geometry (spatial extent, relative pose). In large scale problems,...
Michal Perdoch (Czech Technical University), Ondre...
CVPR
2008
IEEE
14 years 10 months ago
Fast image search for learned metrics
We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis d...
Prateek Jain, Brian Kulis, Kristen Grauman
CVPR
2008
IEEE
14 years 10 months ago
Margin-based discriminant dimensionality reduction for visual recognition
Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...
Hakan Cevikalp, Bill Triggs, Frédéri...
IANDC
2006
121views more  IANDC 2006»
13 years 8 months ago
Comparing operational models of name-passing process calculi
We study three operational models of name-passing process calculi: coalgebras on (pre)sheaves, indexed labelled transition systems, and history dependent automata. The coalgebraic...
Marcelo P. Fiore, Sam Staton