Noise supplement learning for associative memories composed of multilayer perceptrons and sparsely interconnected neural networks

摘要

Recently, we have proposed associative memories composed of multilayer perceptrons (MLPs) and sparsely interconnected neural networks (SINNs), named MLP-SINN. Our proposed associative memories are suitable for hardware implementation and have better performance than both MLPs and SINNs. However, the capabilities of MLP and SINN are not effectively used in the conventional MLP-SINN, because they are synthesized independently. In this report, we propose the noise supplement learning for MLP-SINN associative memories to improve them.