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» Learning for Dynamic Subsumption
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137
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LCTRTS
2007
Springer
15 years 8 months ago
Integrated CPU and l2 cache voltage scaling using machine learning
Embedded systems serve an emerging and diverse set of applications. As a result, more computational and storage capabilities are added to accommodate ever more demanding applicati...
Nevine AbouGhazaleh, Alexandre Ferreira, Cosmin Ru...
136
Voted
ECCV
2006
Springer
16 years 4 months ago
Sparse Flexible Models of Local Features
Abstract. In recent years there has been growing interest in recognition models using local image features for applications ranging from long range motion matching to object class ...
Gustavo Carneiro, David Lowe
120
Voted
CE
2005
170views more  CE 2005»
15 years 2 months ago
Development of an environmental virtual field laboratory
Laboratory exercises, field observations and field trips are a fundamental part of many earth science and environmental science courses. Field observations and field trips can be ...
V. Ramasundaram, S. Grunwald, A. Mangeot, N. B. Co...
156
Voted
CEC
2005
IEEE
15 years 8 months ago
Sensorimotor experience and its metrics: informational geometry and the temporal horizon
Abstract- We introduce metrics on sensorimotor experience at various temporal scales based on informationtheory. Sensorimotor variables through which the experience of an agent fl...
Chrystopher L. Nehaniv
KDD
2009
ACM
207views Data Mining» more  KDD 2009»
16 years 3 months ago
DynaMMo: mining and summarization of coevolving sequences with missing values
Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn ...
Lei Li, James McCann, Nancy S. Pollard, Christos F...