Cognitive modeling and learning with sparse binary hypervectors
Zhonghao Yang
Following the general theoretical framework of VSA (Vector Symbolic
Architecture), a cognitive model with the use of sparse binary hypervectors is
proposed. In addition, learning algorithms are introduced to bootstrap the
model from incoming data stream, with much improved transparency and
efficiency. Mimicking human cognitive process, the training can be performed
online while inference is in session. Word-level embedding is re-visited with
such hypervectors, and further applications in the field of NLP (Natural
Language Processing) are explored.