Topological Simplification in Predictive Coding Networks
预测编码网络中的拓扑简化
机构 * University of Southern California(南加州大学)
AI总结 该研究用分层持续同调分析,发现预测编码网络(PCNs)的模型规模、简化深度与重构误差、架构类型影响其连通分量合并时机,揭示了PCNs压缩-重构权衡的相关规律。
Comments Accepted to the 2nd Annual Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS 2026); to appear in Proceedings of Machine Learning Research