SOURCE-LINKED INTELLIGENCE
Stable Unsupervised Continual Chunking with Sheaf SyncMap
Unsupervised Continual chunking is a fundamental problem in machine learning and neuroscience, where the goal is to identify groups of states that frequently co-occur in temporal sequences. A key challenge is to form accurate chunks while maintaining their stability over time. In this work, we propose sheaf regularization to reduce local inconsistencies in Decentralized SyncMap, a self-organizing system, and thereby stabilize its chunking dynamics. We introduce a radial sheaf structure that penalizes distance-dependent radial motion between pairs of variables. Experimental results show that th
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T06:59:32.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.