The most compute-efficient training framework for ResNet architectures. 99.1% FLOPs reduction. 100% Local.

Code for "Matryoshka Plasticity: Exploiting Nested Transformer Structure for Zero‑Overhead Continual Learning"

Staged Embarrassment Learning (SEL) is a bio-inspired framework for efficient Deep Learning. Inspired by a child’s rapid correction after a mistake, SEL uses dynamic gradient sparsity to focus compute on high-loss "embarrassing" samples . It achieves up to 99% FLOPs reduction, making it ideal for Edge AI.