Representing sets as summed semantic vectors

被引：5
|
作者：
Summers-Stay, Douglas [1 ]
Sutor, Peter [2 ]
Li, Dandan [1 ]
机构：
[1] US Army Res Lab, Adeiphi, MD 20783 USA
[2] Univ Maryland, College Pk, MD 20742 USA
来源：
BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES | 2018年 / 25卷
关键词：
Hyperdimensional computing; Memory; Vector symbolic architectures; COMPETITION;
D O I：
10.1016/j.bica.2018.07.002
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Representing meaning in the form of high dimensional vectors is a common and powerful tool in biologically inspired architectures. While the meaning of a set of concepts can be summarized by taking a (possibly weighted) sum of their associated vectors, this has generally been treated as a one-way operation. In this paper we show how a technique built to aid sparse vector decomposition allows in many cases the exact recovery of the inputs and weights to such a sum, allowing a single vector to represent an entire set of vectors from a dictionary. We characterize the number of vectors that can be recovered under various conditions, and explore several ways such a tool can be used for vector-based reasoning.
引用
收藏
页码：113 / 118
页数：6
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