English(EN) Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification

Looped SSMs 通过深度递归提升时间序列分类性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-15 15:18

研究人员推出了一种新颖的状态空间模型（SSM）方法——Looped SSMs，用于时间序列分类。该方法通过应用深度递归来提高性能，其中模型块跨层重用，类似于循环 Transformer。研究还强调了输入重塑技术（如连接或展平时间步）的显著优势，这些技术进一步提高了准确性。 AI

影响为时间序列分类模型引入了新颖的架构改进，有可能增强特定 AI 应用的性能。

排序理由该集群包含一篇介绍新模型架构和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Radu Grosu · 2026-05-15 15:18

Looped SSMs：深度递归和输入重塑用于时间序列分类

State Space Models (SSMs) are inherently recurrent along the sequence dimension, yet depth-recurrence - reusing the same block repeatedly across layers, as recently applied in looped transformers - has not been explored in this model family. We show that a looped SSM with $k$ par…