论文标题
递归结合，用于序列的相似性的过度向量表示
Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences
论文作者
论文摘要
高维计算（HDC），也称为矢量符号体系结构（VSA），是人工智能和认知计算中使用的计算框架，它具有具有较大固定尺寸的分布式向量表示。设计HDC/VSA解决方案的关键步骤是从输入数据中获取此类表示。在这里，我们专注于序列，并提出它们转换为分布式表示，这些表示既保留附近位置上相同序列元素的相似性，又与序列移位相似。这些属性是通过使用递归结合和叠加操作形成序列位置的表示来启用的。通过用于建模人类对单词相似性的感知的符号字符串，对所提出的转化进行了实验研究。所获得的结果与文献中更复杂的方法相提并论。所提出的转换是为HDC/VSA模型设计的，称为傅立叶全息降低表示。但是，它可以适用于其他一些HDC/VSA模型。
Hyperdimensional computing (HDC), also known as vector symbolic architectures (VSA), is a computing framework used within artificial intelligence and cognitive computing that operates with distributed vector representations of large fixed dimensionality. A critical step for designing the HDC/VSA solutions is to obtain such representations from the input data. Here, we focus on sequences and propose their transformation to distributed representations that both preserve the similarity of identical sequence elements at nearby positions and are equivariant to the sequence shift. These properties are enabled by forming representations of sequence positions using recursive binding and superposition operations. The proposed transformation was experimentally investigated with symbolic strings used for modeling human perception of word similarity. The obtained results are on a par with more sophisticated approaches from the literature. The proposed transformation was designed for the HDC/VSA model known as Fourier Holographic Reduced Representations. However, it can be adapted to some other HDC/VSA models.
