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• W2111270167 abstract "Advances in statistical learning theory have resulted in a multitude of different designs of learning machines. But which ones are implemented by brains and other biological information processors? We analyze how various abstract Bayesian learners perform on different data and argue that it is difficult to determine which learning-theoretic computation is performed by a particular organism using just its performance in learning a stationary target (learning curve). Based on the fluctuation-dissipation relation in statistical physics, we then discuss a different experimental setup that might be able to solve the problem." @default.
• W2111270167 created "2016-06-24" @default.
• W2111270167 creator A5022198472 @default.
• W2111270167 date "2005-09-01" @default.
• W2111270167 modified "2023-09-28" @default.
• W2111270167 title "Fluctuation-Dissipation Theorem and Models of Learning" @default.
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• W2111270167 doi "https://doi.org/10.1162/0899766054322982" @default.
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