Heretofore, neural networks with external memory are restricted to single memory with lossy
representations of memory interactions. A rich representation of relationships between …

Dense Associative Memories or Modern Hopfield Networks have many appealing properties
of associative memory. They can do pattern completion, store a large number of memories …

Associative memory plays an important role in human intelligence and its mechanisms have
been linked to attention in machine learning. While the machine learning community's …

Associative memories have been an active area of research over the last forty years
(Willshaw et al., 1969; Kohonen, 1972; Hopfield, 1982) because they form a central …

A large number of neural network models of associative memory have been proposed in the
literature. These include the classical Hopfield networks (HNs), sparse distributed memories …

This paper presents a deep associative neural network (DANN) based on unsupervised
representation learning for associative memory. In brain, the knowledge is learnt by …

Associative memories in the brain receive and store patterns of activity registered by the
sensory neurons, and are able to retrieve them when necessary. Due to their importance in …

Hebbian heteroassociative learning is inherently asymmetric. Storing a forward association,
from item A to item B, enables recall of B (given A), but does not permit recall of A (given B) …

Identifying computational mechanisms for memorization and retrieval of data is a long-
standing problem at the intersection of machine learning and neuroscience. Our main …

Bidirectional associative memories (BAMs) have been widely used for auto and
heteroassociative learning. However, few research efforts have addressed the issue of …