AI★★★★arXiv · 2026-07-09
SLORR: Simple and Efficient In-Training Low-Rank Regularization
SLORR is a novel framework for in-training low-rank regularization, addressing the limitations of existing methods. It provides a simple and stateless approach to compressing neural networks without requiring SVDs of large weight matrices or modifying the model architecture. SLORR is instantiated with two main methods, offering a promising solution for efficient neural network compression.
📌 Key points
- SLORR is a simple and stateless framework
- Enables in-training low-rank regularization without SVDs
- Preserves the original model architecture
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