arXiv Machine Learning By Nico Harder, Daniel Becking, Karsten Mueller, Wojciech Samek

Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs

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arXiv:2606. 19993v1 Announce Type: new Abstract: We present Activation- and Influence-Aware Ranks (AIR), an SVD-based LLM compression framework that guides each weight matrix's low-rank approximation with a backward-signal influence metric.

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arXiv AI
Sep 10

Mind the Approximation: Fisher-Weighted SVD Compression for ViTs

The paper introduces FACTS, a structured Fisher Approximation for compressing Vision Transformers (ViTs) using Fisher-weighted SVD, which enforces token‑local aggregation while preserving within‑token activation‑gradient dependence. It also presents Constrained Rank Search (CoRS) to optimize layer‑wise rank allocation under a fixed FLOP budget. Experiments on ViTs and hybrid architectures show that FACTS improves accuracy‑efficiency trade‑offs, outperforming the strongest SVD baseline by up to +5.8 percentage points on Swin‑B without requiring finetuning.

By Moritz Thoma, Maximilian Groezinger, Maximilian Forstenh\"ausler, Emad Aghajanzadeh, Ryan Pegoud, Manoj Rohit Vemparala, Pierpaolo Mori, Alexander Frickenstein, Daniel Mueller-Gritschneder, Ulf Schlichtmann