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