AI 中文总结
该研究提出有序结构依赖假设与独立结构专家原则，构建结构演化循环神经网络，通过多个有序投影提升序列学习性能，为结构化学习提供通用计算视角。
AI 中文摘要
循环神经网络（RNN）广泛应用于序列学习，但其应用通常与时间数据相关联，尽管循环计算本质上是对有序序列进行操作，而非针对时间本身。基于这一观察，我们提出有序结构依赖假设（OSDH），该假设认为同一观测的多个可允许排序可能揭示出通过单一序列组织无法获取的互补结构依赖关系。为将这一假设付诸实践，我们提出独立结构专家原则（ISEP），即先独立训练针对特定投影的序列模型，再通过专用融合模型整合其学习到的表示。作为具体实现，我们提出结构演化循环神经网络（SE-RNN），其采用传统RNN作为特定投影的结构专家，同时保留底层循环计算不变。在三个结构复杂程度显著不同的合成数据集上开展的概念验证实验表明，当存在隐藏结构依赖时，所提出的架构能持续从多个有序投影中获益，而在更简单的数据集上仍保持竞争力。由于OSDH独立于底层序列处理模型，所提出的框架自然可扩展至循环网络之外，也可采用其他架构实现。研究结果为在各类结构化学习问题中利用互补有序表示提供了通用计算视角。
英文摘要
Recurrent neural networks (RNNs) are widely used for sequence learning, yet their application is commonly associated with temporal data, although recurrent computation fundamentally operates on ordered sequences rather than on time itself. Building on this observation, we introduce the Ordered Structural Dependency Hypothesis (OSDH), which proposes that multiple admissible orderings of the same observations may reveal complementary structural dependencies inaccessible through a single sequential organization. To operationalize this hypothesis, we propose the Independent Structural Expert Principle (ISEP), whereby projection-specific sequence models are trained independently before their learned representations are integrated through a dedicated fusion model. As a concrete realization, we present Structural Evolution RNNs (SE-RNNs), which employ conventional RNNs as projection-specific structural experts while preserving the underlying recurrent computation unchanged. Proof-of-concept experiments on three synthetic datasets with substantially different levels of structural complexity demonstrate that the proposed architecture consistently benefits from multiple ordered projections when hidden structural dependencies are present, while remaining competitive on simpler datasets. Since OSDH is independent of the underlying sequence-processing model, the proposed framework naturally extends beyond recurrent networks and may be instantiated using alternative architectures. The results suggest a general computational perspective for exploiting complementary ordered representations across diverse structured learning problems.
Comments28 pages; 3 figures
