论文标题
分析分布式向量表示编码空间信息的能力
Analyzing the Capacity of Distributed Vector Representations to Encode Spatial Information
论文作者
论文摘要
向量符号结构属于相关认知建模方法的家族，该方法编码高维矢量中的符号和结构。与人类受试者类似，其处理和存储信息或概念在短期内存中的能力受到数值限制，可以在此矢量表示中编码的信息能力是有限的，也是对认知的数值限制进行建模的一种方法。在本文中，我们分析了有关分布式表示的信息能力的这些限制。我们将分析重点放在简单的叠加和更复杂的结构化表示上，涉及卷积的力量编码空间信息。在两个实验中，我们发现可以有效存储在单个向量中的概念数量的上限。
Vector Symbolic Architectures belong to a family of related cognitive modeling approaches that encode symbols and structures in high-dimensional vectors. Similar to human subjects, whose capacity to process and store information or concepts in short-term memory is subject to numerical restrictions,the capacity of information that can be encoded in such vector representations is limited and one way of modeling the numerical restrictions to cognition. In this paper, we analyze these limits regarding information capacity of distributed representations. We focus our analysis on simple superposition and more complex, structured representations involving convolutive powers to encode spatial information. In two experiments, we find upper bounds for the number of concepts that can effectively be stored in a single vector.
