Showing hep-thShow all
2 papers · 1 filter
hep-th2026
Towards Worst-Case Guarantees with Scale-Aware Interpretability
Lauren Greenspan, David Berman, Aryeh Brill +9
Neural networks organize information according to the hierarchical, multi-scale structure of natural data. Methods to interpret model internals should be similarly scale-aware, exp…
hep-th2025
NCoder -- A Quantum Field Theory approach to encoding data
David S. Berman, Marc S. Klinger, Alexander G. Stapleton
In this paper we present a novel approach to interpretable AI inspired by Quantum Field Theory (QFT) which we call the NCoder. The NCoder is a modified autoencoder neural network w…