3 papers
cs.IT2026
Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks
Mark Blümel, Andreas C. Schneider, Valentin Neuhaus +5
Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues. Yet, the local computational princip…
cs.LG2026
Learning Through Noise: Why Subliminal Learning Works and When It Fails
Vincent C. Brockers, Roman D. Ventzke, Valentin Neuhaus +2
In the context of artificial neural networks, subliminal learning refers to the transfer of task-relevant knowledge or unintended biases from teacher to student models through dist…
cs.IT2025
What should a neuron aim for? Designing local objective functions based on information theory
Andreas C. Schneider, Valentin Neuhaus, David A. Ehrlich +4
In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological system…