Hopfield networks as a model of prototype-based category learning: A method to distinguish trained, spurious, and prototypical attractors
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Geometric entropy and phase transitions analyzed in continuous thermal dense associative memory
This paper explores the theoretical memory capacity of modern Hopfield networks, specifically Dense Associative Memory models with continuous states. It derives thermodynamic phase boundaries for these networks, compari…
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Kernel Hopfield networks show high storage capacity, stability limits analyzed
Researchers have analyzed the geometric properties and storage capacity limits of kernel Hopfield networks trained with Kernel Logistic Regression (KLR). Their experiments, using random sequences and CIFAR-10 image embe…
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New methods boost medical image segmentation with minimal annotations
Researchers have developed new semi-supervised learning techniques to improve image segmentation with significantly reduced annotation requirements. One method, SemiGDA, aligns feature and semantic distributions using d…