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ICML
2010
IEEE
13 years 11 months ago
Learning Temporal Causal Graphs for Relational Time-Series Analysis
Learning temporal causal graph structures from multivariate time-series data reveals important dependency relationships between current observations and histories, and provides a ...
Yan Liu 0002, Alexandru Niculescu-Mizil, Aurelie C...
ICC
2008
IEEE
106views Communications» more  ICC 2008»
14 years 4 months ago
On the Capacity of OFDM Systems with Receiver I/Q Imbalance
—OFDM systems have gained outstanding popularity for high data rate wireless communications. In practice, however, the performance of OFDM systems is often limited due to hardwar...
Stefan Krone, Gerhard Fettweis
GFKL
2007
Springer
184views Data Mining» more  GFKL 2007»
14 years 4 months ago
A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems
Abstract. Artificial systems with a high degree of autonomy require reliable semantic information about the context they operate in. State interpretation, however, is a difficult ...
Daniel Meyer-Delius, Christian Plagemann, Georg vo...
ICDM
2005
IEEE
117views Data Mining» more  ICDM 2005»
14 years 3 months ago
On Learning Asymmetric Dissimilarity Measures
Many practical applications require that distance measures to be asymmetric and context-sensitive. We introduce Context-sensitive Learnable Asymmetric Dissimilarity (CLAD) measure...
Krishna Kummamuru, Raghu Krishnapuram, Rakesh Agra...
NIPS
2004
13 years 11 months ago
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes
We propose the hierarchical Dirichlet process (HDP), a nonparametric Bayesian model for clustering problems involving multiple groups of data. Each group of data is modeled with a...
Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, ...