Connectionist hashed associative memory

摘要

This paper proposes the use of simple connectionist networks as hashing functions for sparse associative or content addressable memory. The robustness of such networks in the presence of noisy inputs, and the property that “similar inputs lead to similar outputs” permits (in a probabilistic sense) faster-than-linear retrieval of data which best fits the input. The input may be noisy or have partially specified feature vectors. Mathematical analysis is presented for the Boolean feature case using a network with randomly selected connection strengths.