通过残差连接增强记忆的新型无训练循环网络。
Residual Reservoir Memory Networks
- 用线性记忆池+非线性残差正交池构建新结构
- 在时间序列与一维像素分类任务上表现更优
- 适合需要长期依赖建模的时序任务研究者
我们提出一种新型无训练循环神经网络——残差水库记忆网络（ResRMN），属于水库计算（RC）范式。该网络结合线性记忆水库与基于时间维度残差正交连接的非线性水库，以增强输入的长期传播能力。通过线性稳定性分析研究了水库状态动力学，并探索了多种时间残差连接配置。实验在时间序列和一维像素级分类任务上验证了其有效性，结果表明该方法优于其他传统RC模型。
原文摘要 · Abstract (English)
We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservoir Memory Networks (ResRMNs). ResRMN combines a linear memory reservoir with a non-linear reservoir, where the latter is based on residual orthogonal connections along the temporal dimension for enhanced long-term propagation of the input. The resulting reservoir state dynamics are studied through the lens of linear stability analysis, and we investigate diverse configurations for the temporal residual connections. The proposed approach is empirically assessed on time-series and pixel-level 1-D classification tasks. Our experimental results highlight the advantages of the proposed approach over other conventional RC models.
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