Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adding DGP hidden layers and dimensions (DGP-3 with M=100) vs. shallow GP | FITC DGP regression · UCI regression (10 datasets) | average test MLL / rank | Helped |
| Using a one-dimensional hidden layer (warped GP, DGP-1) instead of multi-dimensional hidden layers (ablation) | DGP · UCI regression | average test MLL | Hurt |
| Increasing pseudo points/inducing outputs from M=50 to M=100 in DGP (ablation) | DGP · UCI regression | average test MLL | Helped |
| Adding a hidden layer to a GP on binary classification (DGP D-2-1 vs GP D-1) | DGP · Binary classification (6 datasets) | average test log-likelihood | Mixed |
| Adding a hidden layer on multiclass classification (DGP D-1-K vs GP D-K) | DGP · Multiclass classification (4 datasets) | average test log-likelihood | No clear effect |
| DGP with 2 hidden dims (DGP-2 100) on photovoltaic molecules vs shallow FITC GP | DGP · Photovoltaic molecules (HCEP) | test MLL | Helped |
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