Research map / Probabilistic and scientific modeling / Variational and generative sampling
A Spectral Approach to Gradient Estimation for Implicit Distributions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SSGE instead of Stein+ for gradient estimates | Gradient-free HMC · UCI Glass hyperparameter posterior | average acceptance ratio | Helped |
| Use SSGE instead of KMC for gradient-free HMC | Gradient-free HMC · UCI Glass hyperparameter posterior | average acceptance ratio | Helped |
| Use SSGE for out-of-sample gradient estimation | Gradient estimation for a standard Gaussian · 1-D standard Gaussian | gradient estimation accuracy | Helped |
| Add the entropy term, estimated using SSGE (ablation) | Implicit VAE · CelebA | sample quality | Helped |
| Add SSGE-estimated entropy gradients to the Implicit VAE | Implicit VAE · CelebA | FID | Helped |
| Use SSGE with an implicit posterior instead of a factorized Gaussian posterior | Bayesian neural network · 1-D regression | predictive uncertainty | Mixed |
| Use SSGE with a factorized posterior instead of BBB | Bayesian neural network · 1-D regression | prediction | No clear effect |
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