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This work proposes sheaf regularization to reduce local inconsistencies in Decentralized SyncMap, a self-organizing system, and thereby stabilize its chunking dynamics, and introduces a radial sheaf structure that penalizes distance-dependent radial motion between pairs of variables.
A boundary-first inference schedule that partitions a model into chunks, first coordinates hidden states at chunk boundaries, and then refines representations within each chunk is proposed, which instantiate in predictive coding networks (PCNs), a local-learning framework in which hidden activities and prediction error...
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