5 papers
Joint Flow Matching: Consistent Generative and Discriminative Inference from a Shared Flow
Hayden McAlister, Lech Szymanski
We study Joint Flow Matching (JFM), a flow-based framework for learning a joint distribution over paired variables such as images and class labels. JFM transports the variables in…
Classifying States of the Hopfield Network with Improved Accuracy, Generalization, and Interpretability
Hayden McAlister, Anthony Robins, Lech Szymanski
We extend the existing work on Hopfield network state classification, employing more complex models that remain interpretable, such as densely-connected feed-forward deep neural ne…
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…
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…
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…