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» Learning Overcomplete Representations
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ICML
2007
IEEE
14 years 11 months ago
Recovering temporally rewiring networks: a model-based approach
A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically r...
Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing
ICML
2006
IEEE
14 years 11 months ago
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
Inference in Markov Decision Processes has recently received interest as a means to infer goals of an observed action, policy recognition, and also as a tool to compute policies. ...
Marc Toussaint, Amos J. Storkey
ICML
2004
IEEE
14 years 11 months ago
Automated hierarchical mixtures of probabilistic principal component analyzers
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
Ting Su, Jennifer G. Dy
ICML
2004
IEEE
14 years 11 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
ICML
1999
IEEE
14 years 11 months ago
Abstracting from Robot Sensor Data using Hidden Markov Models
ing from Robot Sensor Data using Hidden Markov Models Laura Firoiu, Paul Cohen Computer Science Department, LGRC University of Massachusetts at Amherst, Box 34610 Amherst, MA 01003...
Laura Firoiu, Paul R. Cohen