基于层正则化SyncMap的稳定无监督持续分块
Stable Unsupervised Continual Chunking with Sheaf SyncMap
中文总结 AI 辅助
针对无监督持续分块中的稳定性挑战，提出层正则化稳定分散式SyncMap，在多数CGCP图上取得最高NMI，并能适应新知识避免负迁移。
中文摘要 AI 辅助
无监督持续分块是机器学习和神经科学中的一个基本问题，其目标是识别在时间序列中频繁共现的状态组。一个关键挑战是在形成准确分块的同时保持其随时间推移的稳定性。在这项工作中，我们提出了一种层（sheaf）正则化方法，以减少分散式SyncMap（一种自组织系统）中的局部不一致性，从而稳定其分块动态。我们引入了一种径向层结构，该结构惩罚变量对之间依赖于距离的径向运动。实验结果表明，在具有两状态记忆的18个概率持续通用分块问题（CGCP）图中的12个，以及具有动态记忆的18个图中的17个上，所提出的方法在评估的SyncMap变体中取得了最高的归一化互信息（NMI）。在顺序适应实验中，Sheaf SyncMap在输入分布变化后也取得了高NMI，表明它能够适应新知识，同时避免现代机器学习系统（如神经网络）中常见的负迁移。
英文摘要
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.
