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In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Expectation Propagation for approximate Bayesian inference
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