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abstract: ‘In this paper, we present the *GAMES (Globally Adaptive Markov Elementary Sequences)* family of learning algorithms for sequential and interactive models with environments showing local patterns (e.g. noisy Markov random fields, $\epsilon$-machines, Bayesian networks). The method exploits the locality of Markov models on the base of the *Markov tree* that encodes the conditional probability distribution. As a result, it can be executed in a distributed fashion without traversing the entire model. Interestingly, *GAMES* have an operational interpretation in the probabilistic viewpoint of sequence learning and correspond to the variant of the Expectation-Maximization algorithm which explicitly assumes that the observations follow a distribution matching the model. The main contribution of the paper is a generalization of the variational inference algorithm *belief propagation* which does not require any assumption on the observation model.’
author:
– |
Francesco Fascioli\
Cognitive Systems and Robotics Lab, CNR-IEIIT\
The Italian Institute for Intelligent Systems, Italian National Research Council\
`francesco.fascioli@cnr-ieiit.it`\
Marco Garro [^1]\
Cognitive Systems and Robotics Lab, CNR-IEIIT\
The Italian Institute for Intelligent Systems, Italian National Research Council\
`marco.garro@cnr-ieiit.it`\
Aldo Piantanida\
Cognitive Systems and Robotics Lab, CNR-IEIIT\
The Italian Institute for Intelligent Systems, Italian National Research Council\
`aldo.piantanida@cnr-ieiit.it`\
Laureano Ramponi\
37a470d65a

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