arXiv Machine Learning July 3, 2026 By David Gonz\'alez-Mart\'inez

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression

Read the original on arXiv Machine Learning →

arXiv:2509. 25136v3 Announce Type: replace Abstract: Activation-aware low-rank factorization techniques yield strong compression results but are generally confined to linear layers, while existing whitening-based theory typically makes an implicit full-rank assumption on activations.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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