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We present a distributed vector representation based on a simplification of the BEAGLE system, designed in the context of the Sigma cognitive architecture. Our method does not require gradient-based training of neural networks, matrix decompositions as with LSA, or convolutions as with BEAGLE. All that is involved is a sum of random vectors and their(More)
We demonstrate that distributed vector representations are capable of hierarchical reasoning by summing sets of vectors representing hyponyms (subordinate concepts) to yield a vector that resembles the associated hypernym (superordinate concept). These distributed vector representations constitute a potentially neurally plausible model while demonstrating a(More)
Previous computational models of jazz improvisation typically employ algorithms designed to " think " like an improvising jazz musician, each offering distinct advantages and disadvantages. Creating a model that successfully produces jazz improvisation would (1) offer insights into a unique cognitive expertise, (2) elucidate more general creative processes(More)
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