English(EN) Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks

新型递归二次滤波器增强了深度循环网络的学习能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 17:30

研究人员开发了递归二次滤波器（RQFs），这是一种受生物机制启发的、新型的复值时间滤波器，旨在改进深度连续时间循环网络的学习。这些滤波器通过使每一层的自顶向下输入具有前瞻性，解决了深度网络堆栈中信号延迟和误差衰减等问题。在语音命令和Path-X等任务上的评估表明，RQFs、S5和ORGaNICs的前瞻性变体与非前瞻性变体相当或更优，将RQFs确定为一种高效的循环架构，并将前瞻性编码作为一种有价值的纠正方法。 AI

影响引入了一种新颖的滤波技术，可以提高循环神经网络在序列处理任务中的效率和性能。

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · David J. Heeger · 2026-09-03 17:30

前瞻性编码改进深度连续时间循环网络的学习

Temporal integration gives continuous-time recurrent networks memory, but in deep stacks it also delays bottom-up signals and attenuates top-down errors. We develop Recursive Quadrature Filters (RQFs), biologically motivated complex-valued temporal filters that are a special case…

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前瞻性编码改进深度连续时间循环网络的学习

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