Hugging Face Trending Papers
A Study of Hidden-State Optimization Order in Predictive Coding Networks
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arXiv AI
Sep 2
A Study of Hidden-State Optimization Order in Predictive Coding Networks
The paper investigates how the sequence of hidden-state optimization affects feature learning in local-learning models, specifically predictive coding networks (PCNs). It introduces a boundary-first inference schedule that first aligns hidden states at chunk boundaries before refining representations within each chunk. Experiments on CIFAR-10 show that this approach improves accuracy by 9.77% over standard PCNs and 5.51% under a different parametrization, with diagnostics indicating stronger feature learning.
Hugging Face Trending Papers
Jun 22
Error Highways: Scaling Predictive Coding to Very Deep Networks
Predictive coding networks (PCNs) offer a biologically-plausible, local-learning alternative to back-propagation of errors (backprop). Nevertheless, they have remained largely confined to shallow architectures and evaluated on simple machine intelligence benchmarks.
arXiv Machine Learning
Jun 26
Learning Long-Range Dependencies with Temporal Predictive Coding
arXiv:2602. 18131v2 Announce Type: replace Abstract: Temporal Predictive Coding provides a layer-local, parallelisable mechanism for learning in recurrent systems, making it an attractive candidate for online local learning on neuromorphic and edge hardware.
arXiv AI
Aug 11
On the Infinite Width and Depth Limits of Predictive Coding Networks
arXiv:2602. 07697v3 Announce Type: replace-cross Abstract: Predictive coding (PC) is a biologically plausible alternative to standard backpropagation (BP) that minimises an energy function with respect to network activities before updating weights.
arXiv Machine Learning
Sep 21
Predictive Suppression Layers for Communication-Efficient Spiking Neural Networks
arXiv:2609.21583v1 Announce Type: cross Abstract: Feedforward Spiking Neural Networks (SNNs) typically propagate every generated spike indiscriminately, disregarding whether the information is redund...
arXiv Machine Learning
Jun 30
Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization
arXiv:2606. 29951v1 Announce Type: new Abstract: Interpretable Mesomorphic Neural Networks (IMNs) offer a promising framework that combines the predictive power of deep neural networks with the interpretability of linear models.