The Power and Limitations of Kernel Learning

The Power and Limitations of Kernel Learning

Simons Institute via YouTube Direct link

Intro

1 of 15

1 of 15

Intro

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The Power and Limitations of Kernel Learning

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  1. 1 Intro
  2. 2 The limits and power of kernels
  3. 3 "Shallow"/kernel architectures
  4. 4 Kernel learning for modern ML
  5. 5 Kernel methods for big data
  6. 6 The limits of kernels
  7. 7 Eigenvalue decay
  8. 8 Eigenpro: practical implementation
  9. 9 Comparison with state-of-the-art
  10. 10 Understanding SGD
  11. 11 Batch size for parallel computation
  12. 12 Overfitting with kernels
  13. 13 Kernel overfitting/interpolation
  14. 14 Accelerated methods for kernels
  15. 15 Parting Thoughts

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