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cs.NE2025
Sequential Learning in the Dense Associative Memory
Hayden McAlister, Anthony Robins, Lech Szymanski
Sequential learning involves learning tasks in a sequence, and proves challenging for most neural networks. Biological neural networks regularly conquer the sequential learning cha…
cs.NE2024
Improved Robustness and Hyperparameter Selection in the Dense Associative Memory
Hayden McAlister, Anthony Robins, Lech Szymanski
The Dense Associative Memory generalizes the Hopfield network by allowing for sharper interaction functions. This increases the capacity of the network as an autoassociative memory…
cs.NE2024
Prototype Analysis in Hopfield Networks with Hebbian Learning
Hayden McAlister, Anthony Robins, Lech Szymanski
We discuss prototype formation in the Hopfield network. Typically, Hebbian learning with highly correlated states leads to degraded memory performance. We show this type of learnin…