Predictive coding learns more sample-efficiently than backpropagation because its updates align better with output prediction errors in deep linear networks, with exact conditions for optimal alignment derived.
Local loss optimization in the infinite width: Stable parameterization of predictive coding networks and target propagation
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Understanding Sample Efficiency in Predictive Coding
Predictive coding learns more sample-efficiently than backpropagation because its updates align better with output prediction errors in deep linear networks, with exact conditions for optimal alignment derived.