no code implementations • 24 Jul 2014 • Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi
The resulting network has a retrieval capacity that is exponential in the size of the network.
no code implementations • 13 Mar 2014 • Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney
More surprisingly, we show that internal noise actually improves the performance of the recall phase while the pattern retrieval capacity remains intact, i. e., the number of stored patterns does not reduce with noise (up to a threshold).
no code implementations • NeurIPS 2013 • Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney
More surprisingly, we show that internal noise actually improves the performance of the recall phase.
no code implementations • 26 Jan 2013 • Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav Varshney
Recent advances in associative memory design through strutured pattern sets and graph-based inference algorithms have allowed the reliable learning and retrieval of an exponential number of patterns.
no code implementations • 8 Jan 2013 • Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi
We propose a novel architecture to design a neural associative memory that is capable of learning a large number of patterns and recalling them later in presence of noise.