papers AI Learner
The Github is limit! Click to go to the new site.

Associative Memories Based on Multiple-Valued Sparse Clustered Networks

2014-02-03
Hooman Jarollahi, Naoya Onizawa, Takahiro Hanyu, Warren J. Gross

Abstract

Associative memories are structures that store data patterns and retrieve them given partial inputs. Sparse Clustered Networks (SCNs) are recently-introduced binary-weighted associative memories that significantly improve the storage and retrieval capabilities over the prior state-of-the art. However, deleting or updating the data patterns result in a significant increase in the data retrieval error probability. In this paper, we propose an algorithm to address this problem by incorporating multiple-valued weights for the interconnections used in the network. The proposed algorithm lowers the error rate by an order of magnitude for our sample network with 60% deleted contents. We then investigate the advantages of the proposed algorithm for hardware implementations.

Abstract (translated by Google)
URL

https://arxiv.org/abs/1402.0808

PDF

https://arxiv.org/pdf/1402.0808


Similar Posts

Comments