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Looped SSMs improve time series classification with depth-recurrence

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Looped SSMs, a novel approach to State Space Models for time series classification. This method enhances performance by applying depth-recurrence, where model blocks are reused across layers, similar to looped transformers. The study also highlights the significant benefits of input reshaping techniques, such as concatenating or flattening timesteps, which further boost accuracy. AI

IMPACT Introduces novel architectural improvements for time series classification models, potentially enhancing performance in specialized AI applications.

RANK_REASON The cluster contains a new academic paper detailing a novel model architecture and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Looped SSMs improve time series classification with depth-recurrence

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Radu Grosu ·

    Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification

    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…

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