Research map / Probabilistic and scientific modeling / Variational and generative sampling
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use NGVI instead of BBVI with reparameterization gradients | Bayesian logistic regression with Gaussian mixture approximation · Breast-Cancer and Sonar | KL divergence convergence | Helped |
| Increase the number of Gaussian mixture components | Bayesian logistic regression posterior approximation · Breast-Cancer and Sonar | KL divergence | Helped |
| Use a Gaussian mixture instead of a single Gaussian | Bayesian logistic regression posterior approximation · Breast-Cancer and Sonar | KL divergence | Helped |
| Use NGVI instead of BBVI | Skew-Gaussian or Student's t approximation · covtype-binary-scale | convergence | Helped |
| Use NGVI with a skew-Gaussian approximation instead of BBVI | Bayesian neural network · BNN test set | test RMSE convergence | Helped |
| Use a skew-Gaussian instead of a Gaussian approximation | Bayesian neural network · BNN test set | test RMSE | No clear effect |
| Use a skew-Gaussian instead of a Gaussian approximation | Bayesian logistic regression posterior approximation · 2-D Bayesian logistic regression | posterior approximation quality | Helped |
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