Background
In 1982, motivated by neural modeling work of [3] and the Ising spin glass model from statistical physics [2], Hopfield introduced a method for the storage and retrieval of binary patterns in an auto-associative neural-network [1]. However, existing techniques for training Little-Hopfield networks suffer either from limited pattern capacity or excessive training time, and they exhibit poor performance when trained on unlabeled, corrupted memories.