English(EN) TIDES: Implicit Time-Awareness in Selective State Space Models

TIDES模型在状态空间模型中改进了不规则时间序列处理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 00:00

研究人员推出了一种新颖的选择性状态空间模型（SSM），名为TIDES，旨在有效处理不规则时间序列数据。与Mamba等现有模型不同，Mamba学习的步长偏离物理时间，而TIDES保持的步长等同于物理时间间隔。这是通过将输入依赖性转移到对角线状态矩阵来实现的，从而无需牺牲每个token的表达能力即可原生处理不规则时间戳。TIDES在Fading Flash实验基准、UEA时间序列分类和Physiome ODE回归等基准测试中表现出色，在原生不规则数据集上超越了当前最先进的架构。 AI

影响增强了AI模型处理和分析具有不规则时间戳的时间序列数据的能力，可能改进天文学和气候科学等领域的应用。

排序理由该集群描述了一篇介绍新模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-05 00:00

TIDES：选择性状态空间模型中的隐式时间感知

Selective state space models (SSMs), such as Mamba, achieve strong per-token expressivity by making the time discretization step TildeΔ a learned function of the input. However, in doing so, TildeΔ no longer equals the physical time gap Δ between consecutive observations, limitin…

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TIDES：选择性状态空间模型中的隐式时间感知

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