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NIPS
2007
13 years 7 months ago
Variational inference for Markov jump processes
Markov jump processes play an important role in a large number of application domains. However, realistic systems are analytically intractable and they have traditionally been ana...
Manfred Opper, Guido Sanguinetti
UAI
2003
13 years 7 months ago
Bayesian Hierarchical Mixtures of Experts
The Hierarchical Mixture of Experts (HME) is a well-known tree-structured model for regression and classification, based on soft probabilistic splits of the input space. In its o...
Christopher M. Bishop, Markus Svensén
ICONIP
2007
13 years 7 months ago
Natural Conjugate Gradient in Variational Inference
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
Antti Honkela, Matti Tornio, Tapani Raiko, Juha Ka...
ISIPTA
2005
IEEE
168views Mathematics» more  ISIPTA 2005»
13 years 11 months ago
Approximate Inference in Credal Networks by Variational Mean Field Methods
Graph-theoretical representations for sets of probability measures (credal networks) generally display high complexity, and approximate inference seems to be a natural solution fo...
Jaime Shinsuke Ide, Fabio Gagliardi Cozman
ICCAD
2005
IEEE
97views Hardware» more  ICCAD 2005»
14 years 2 months ago
DiCER: distributed and cost-effective redundancy for variation tolerance
— Increasingly prominent variational effects impose imminent threat to the progress of VLSI technology. This work explores redundancy, which is a well-known fault tolerance techn...
Di Wu, Ganesh Venkataraman, Jiang Hu, Quiyang Li, ...