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Notes on compressibility
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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 low-rank regularization method for compressing neural networks during training. It addresses the limitations of existing methods by being stateless…
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…
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