IEEE Data Storage Best Student Paper Award

Our paper Noise-Enhanced Associative Memories received the IEEE Data Storage Best Student Paper Award.

IEEE Data Storage Best Student Paper Award for 2013

Awardees: Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi and Lar R. Varshney

Paper: “Noise-Enhanced Associative Memories,” Proceedings of Neural Information Processing Systems, pp. 1682–1690, 2013. NIPS Proceedings

Abstract: Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms have allowed reliable learning and recall of an exponential number of patterns. Although these designs correct external errors in recall, they assume neurons that compute noiselessly, in contrast to the highly variable neurons in hippocampus and olfactory cortex. Here we consider associative memories with noisy internal computations and analytically characterize performance. As long as the internal noise level is below a specified threshold, the error probability in the recall phase can be made exceedingly small. More surprisingly, we show that internal noise actually improves the performance of the recall phase. Computational experiments lend additional support to our theoretical analysis. This work suggests a functional benefit to noisy neurons in biological neuronal networks.

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