The blessing of dimensionality

Abstract

The paper surveys ongoing research on hyperdimensional computing and vector symbolic architectures which represent an alternative approach to neural computing with various advantages and interesting specific properties: transparency, error tolerance, sustainability. In particular, it can be demonstrated that hyperdimensional patterns are well-suited for the encoding of complex knowledge structures. Consequently, the related architectures offer perspectives for the development of innovative neurosymbolic models with a closer correspondence to cognitive processes in human brains. We revisit the fundamentals of hyperdimensional representations and examine some recent applications of related methods for analogical reasoning and learning tasks, with a particular focus on knowledge graphs. We then propose potential extensions and delineate prospective avenues for future investigations.