Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SGHMC instead of DSVI for DGP inference | DGP · medium-large regression datasets | test log-likelihood | Helped |
| Use SGHMC instead of DSVI for DGP inference | DGP on protein · protein | training speed to target: 1.6 times faster | Helped |
| Use SGHMC DGP 5 instead of the reported DGP baseline | DGP · Harvard Clean Energy Project | test MLL: -1.25 -> -0.83 | Helped |
| Use Dec DGP 3 instead of SGHMC DGP 3 | DGP on MNIST · MNIST | accuracy: 98.0 % -> 98.1 % | Helped |
| Decouple inducing points for the mean and variance (ablation) | DSVI DGP · molecules, protein, year | test log-likelihood | Helped |
| Decouple inducing points for the mean and variance (ablation) | DSVI DGP · other datasets | test log-likelihood | No clear effect |
| Use fewer inducing points for the variance in the decoupled SGHMC DGP (ablation) | SGHMC DGP · energy and wine_red | test log-likelihood stability | Helped |
| Use fewer inducing points for the variance in the decoupled SGHMC DGP (ablation) | SGHMC DGP · large datasets | test log-likelihood | No clear effect |
| Intertwine sample generation and hyperparameter updates with Moving Window MCEM (ablation) | MCEM for DGP hyperparameter optimization · kin8nm | predictive log-likelihood | Helped |
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