Research map / Learning theory and algorithms / Kernels and regression theory
Nearly-tight Bounds for Deep Kernel Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase network depth l in deep kernel machines (generalization bound based on uniform covering number) | deep kernel machines (DKMs) · theoretical generalization bound | convergence rate of generalization bound | Mixed |
| use Rademacher chaos complexity bound (kernel pseudo-dimension) instead of uniform covering number for deep multiple kernel machines | deep multiple kernel machines (DMKMs) · theoretical generalization bound | convergence rate of generalization bound | Helped |
| restrict span-combined kernels to m base kernels so that kernel pseudo-dimension is at most m | deep multiple kernel machines (DMKMs) · theoretical generalization bound | Rademacher chaos complexity bound | No clear effect |
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