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.
ePC: Fast and Deep Predictive Coding for Digital Hardware , publisher =
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Predictive coding is recast as deep hierarchical Gaussian filters to restore precision-weighted message passing, yielding closed-form inference and online precision learning that matches backpropagation speed on FashionMNIST while outperforming on online and concept-drift tasks.
Cross-entropy loss is empirically load-bearing for the K-way energy probe outperforming softmax margins in predictive coding on CIFAR-10, with roughly two-thirds of the gap due to logit scale.
citing papers explorer
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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.
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Closed-form predictive coding via hierarchical Gaussian filters
Predictive coding is recast as deep hierarchical Gaussian filters to restore precision-weighted message passing, yielding closed-form inference and online precision learning that matches backpropagation speed on FashionMNIST while outperforming on online and concept-drift tasks.
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Cross-Entropy Is Load-Bearing: A Pre-Registered Scope Test of the K-Way Energy Probe on Bidirectional Predictive Coding
Cross-entropy loss is empirically load-bearing for the K-way energy probe outperforming softmax margins in predictive coding on CIFAR-10, with roughly two-thirds of the gap due to logit scale.