2 papers
cs.LG2026
SLORR: Simple and Efficient In-Training Low-Rank Regularization
David González-Martínez, Shiwei Liu
Low-rank factorization is widely used to compress neural networks, but modern models are often not naturally amenable to aggressive factorization without significant accuracy loss.…
cs.LG2025
BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression
David González-Martínez
Activation-aware low-rank factorization techniques yield strong compression results but are generally confined to linear layers, while existing whitening-based theory typically mak…