Stable Unsupervised Continual Chunking with Sheaf SyncMap

2026-09-21

AI Summary

研究解决了无监督持续分块中的稳定性问题，通过引入sheaf正则化方法改进了Decentralized SyncMap系统，提高了其在不同条件下的性能和适应性。

Abstract

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 the proposed method achieves the highest normalized mutual information (NMI) among the evaluated SyncMap variants on 12 of 18 probabilistic Continual General Chunking Problem (CGCP) graphs with two-state memory and on 17 of 18 graphs with dynamic memory. In the sequential adaptation experiment, Sheaf SyncMap also achieves high NMI after shifts in the input distribution, indicating that it can adapt to new knowledge while avoiding the negative transfer commonly observed in modern machine learning systems such as neural networks.

Problem

• Unsupervised Continual Chunking
• Temporal Sequences
• Stability

Innovation

• sheaf regularization
• radial sheaf structure
• normalized mutual information (NMI)
• continual chunking