Research map / Learning theory and algorithms / Sample complexity
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase depth of the network added per conditional gradient iteration (1 to 4 layers) (ablation) | SPN-CG on 16-dim GMM data · Synthetic GMM density estimation | iterations to convergence | Helped |
| increase depth of the network added per conditional gradient iteration (ablation) | SPN-CG on 16-dim GMM data · Synthetic GMM density estimation | total running time | Helped |
| run three SPN-CG iterations instead of one (ablation) | SPN-CG · MNIST and NORB classification with missing pixels | accuracy | Helped |
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