2026
Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets
ICML 2026poster
Scaling factors in residual branches have emerged as a prevalent method for boosting neural network performance, especially in normalization-free architectures. While prior work has primarily examined scaling effects from an optimization perspective, this paper investigates their role in residual ar…