ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks

In three linesChainzRule replaces standard activations with learnable polynomial layers governed by Differential Regularization (DREG), a Jacobian penalty computed analytically during forward pass. Tested across tabular, NLP, and vision: 85.71% on Pima Diabetes, 46.20% on SST-5 with frozen encoder (5% of RNTN training data), 55.79% on SST-5 fine-tuned BERT, +2.32% on CIFAR-10-C. Improves robustness and sample efficiency.

Summary generated by Claude — human-verified