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» Structured Learning with Approximate Inference
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ECML
2004
Springer
14 years 1 months ago
Model Approximation for HEXQ Hierarchical Reinforcement Learning
HEXQ is a reinforcement learning algorithm that discovers hierarchical structure automatically. The generated task hierarchy repthe problem at different levels of abstraction. In ...
Bernhard Hengst
CVPR
1999
IEEE
14 years 10 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
ECCV
2006
Springer
14 years 10 months ago
Efficient Belief Propagation with Learned Higher-Order Markov Random Fields
Belief propagation (BP) has become widely used for low-level vision problems and various inference techniques have been proposed for loopy graphs. These methods typically rely on a...
Xiangyang Lan, Stefan Roth, Daniel P. Huttenlocher...
CICLING
2008
Springer
13 years 10 months ago
Natural Language as the Basis for Meaning Representation and Inference
Abstract. Semantic inference is an important component in many natural language understanding applications. Classical approaches to semantic inference rely on logical representatio...
Ido Dagan, Roy Bar-Haim, Idan Szpektor, Iddo Green...
ECML
2007
Springer
14 years 2 months ago
Bayesian Inference for Sparse Generalized Linear Models
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The paramete...
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg...