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English(EN) Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study

Tsetlin机获得压缩循环反馈,实现高效的序列推理

研究人员开发了一种压缩Tsetlin机中循环反馈的新方法,旨在提高小型设备上的序列推理能力。该方法使用异或(XOR)折叠来减小循环连接的宽度,并在两个时间尺度上保留折叠位,然后将它们阈值化为二元状态。在布尔有限状态机基准上的评估表明,与原始反馈相比,压缩模型实现了约61-63%的准确率,对性能的影响最小,但执行时间和循环宽度显著减少。 AI

影响 这项压缩技术可以实现资源受限设备上更高效的序列推理,有可能拓宽Tsetlin机的应用范围。

排序理由 该集群包含一篇关于Tsetlin机新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Tsetlin机获得压缩循环反馈,实现高效的序列推理

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该集群包含一篇关于Tsetlin机新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ankit Kumar, Utkarsh Raj, Rishad Shafik, Sudip Roy ·

    Tsetlin机器中的压缩循环反馈:一项可复现的布尔有限状态机研究

    arXiv:2609.06133v1 Announce Type: new Abstract: Sequential inference on small devices requires a model to retain useful history without repeatedly processing a long input record. A Recurrent Tsetlin Machine (RTM) provides this memory by returning Boolean clause outputs from one t…