The content discusses the benefits of overparameterization in deep models for solving various tasks but highlights the challenges of increased computational costs. The author introduces a compression algorithm based on low-dimensional learning dynamics, demonstrating improved efficiency without compromising generalization.
The study focuses on deep linear models and their incremental fitting within low-dimensional subspaces, leading to a compression technique that accelerates convergence. By leveraging spectral initialization, the compressed network consistently achieves lower recovery errors than the original network across various problems.
The experiments showcase the effectiveness of the compression technique on matrix recovery problems, emphasizing faster convergence and reduced training time. Additionally, the application of compressed networks to deep nonlinear models demonstrates improved performance with significant reductions in training time and memory usage.
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by Soo Min Kwon... at arxiv.org 03-13-2024
https://arxiv.org/pdf/2311.05061.pdfDeeper Inquiries