# Symbolic Representation and Learning With Hyperdimensional Computing

@article{Mitrokhin2020SymbolicRA, title={Symbolic Representation and Learning With Hyperdimensional Computing}, author={Anton Mitrokhin and Peter Sutor and Douglas Summers-Stay and Cornelia Ferm{\"u}ller and Yiannis Aloimonos}, journal={Frontiers in Robotics and AI}, year={2020}, volume={7} }

It has been proposed that machine learning techniques can benefit from symbolic representations and reasoning systems. We describe a method in which the two can be combined in a natural and direct way by use of hyperdimensional vectors and hyperdimensional computing. By using hashing neural networks to produce binary vector representations of images, we show how hyperdimensional vectors can be constructed such that vector-symbolic inference arises naturally out of their output. We design the… Expand

#### 4 Citations

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations

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This two-part comprehensive survey is devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to… Expand

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