FIELD NOTES TOPIC
low-rank regularization
2 notes, newest first.
SLORR: Simple and Efficient In-Training Low-Rank Regularization
The paper introduces SLORR, a simple and efficient method for in-training low-rank regularization of neural networks. It addresses the limitations of existing methods by being stateless and preserving the original…
SLORR: Simple and Efficient In-Training Low-Rank Regularization
The paper introduces SLORR, a simple and efficient in-training low-rank regularization framework for compressing neural networks without significant accuracy loss or relying on stateful cached quantities. The authors…
