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Vector Symbolic Architectures (VSA) are methods designed to enable distributed representation and manipulation of semantically-structured information, such as natural languages. Recently, a new VSA based on multiplication of distributed vectors by random matrices was proposed, this is known as Matrix-Binding-of-Additive-Terms (MBAT). We propose an enhancement that introduces an important additional...
Vector Symbolic Architectures (VSA) are approaches to representing symbols and structured combinations of symbols as high-dimensional vectors. They have applications in machine learning and for understanding information processing in neurobiology. VSAs are typically described in an abstract mathematical form in terms of vectors and operations on vectors. In this work, we show that a machine learning...
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