Summary of Scaling Continuous Kernels with Sparse Fourier Domain Learning, by Clayton Harper et al.
Scaling Continuous Kernels with Sparse Fourier Domain Learning
by Clayton Harper, Luke Wood, Peter Gerstoft, Eric C. Larson
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach addresses three key challenges in learning continuous kernel representations: computational efficiency, parameter efficiency, and spectral bias. The method leverages sparse learning in the Fourier domain to efficiently scale continuous kernels, reducing computational and memory demands while mitigating spectral bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to learn continuous kernel representations that overcomes limitations like high computational and memory demands, and spectral bias. It does this by using a special type of learning called sparse learning in the Fourier domain. This helps make the method more efficient and accurate. |
