2 papers
stat.ML2026
Width-Robust Learnability in Mean-Field Bayesian Neural Networks
Dmitry Vaintrob, Kaarel Hänni
Infinite-width limits are a standard way to reason about neural networks, but it is not automatic that the limiting learner has the same complexity-theoretic inductive bias as larg…
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…