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Convolutional Neural Associative Memories: Massive Capacity with Noise Tolerance
The resulting network has a retrieval capacity that is exponential in the size of the network.
Noise Facilitation in Associative Memories of Exponential Capacity
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).
Noise-Enhanced Associative Memories
More surprisingly, we show that internal noise actually improves the performance of the recall phase.
Neural Networks Built from Unreliable Components
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.
Coupled Neural Associative Memories
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.
