English(EN) Beyond Fixed Points: Superpolynomial Capacity of Asymmetric Hopfield Networks

非对称 Hopfield 网络实现超多项式序列记忆

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员开发了一种新颖的非对称 Hopfield 网络构造，显著增强了其存储时间序列的能力。这些利用二元神经元和同步更新的网络，现在可以支持超多项式数量的独立极限环吸引子。这一突破使得长序列的鲁棒存储成为可能，克服了先前的限制，并展示了比以往理解的更大的序列记忆容量。 AI

影响为神经网络中的序列记忆引入了新的理论框架，可能影响未来的 AI 架构。

排序理由详细介绍神经网络新理论构造的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Aakash Kumar, Anatoly Khina, Frederik Mallmann-Trenn, Emanuele Natale · 2026-05-26 04:00

超越不动点：非对称Hopfield网络的超多项式容量

arXiv:2605.24611v1 Announce Type: new Abstract: Classical Hopfield networks are limited to static patterns due to symmetric weights, whereas asymmetric networks can encode temporal sequences via limit-cycle attractors. Achieving high-capacity storage of long sequences in classica…