2021
DOI: 10.48550/arxiv.2109.02157
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Learning with Holographic Reduced Representations

Abstract: Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors [1] by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects. This method has seen little use outside of older symbolic AI work and cognitive science. Our goal is to revisit this approach to understand if it is viable for enabling a hybrid neural-symbolic approach to learning as a differentiable component… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 23 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?