Latent Variable Sampling
Data augmentation using Polya-Gamma latent variables.
Introduced by Polson et al. in Bayesian inference for logistic models using Polya-Gamma latent variablesThis method applies Polya-Gamma latent variables as a way to obtain closed form expressions for full-conditionals of posterior distributions in sampling algorithms like MCMC.
Source: Bayesian inference for logistic models using Polya-Gamma latent variablesPapers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Classification | 2 | 16.67% |
| General Classification | 2 | 16.67% |
| Few-Shot Learning | 1 | 8.33% |
| Incremental Learning | 1 | 8.33% |
| Uncertainty Quantification | 1 | 8.33% |
| Point Processes | 1 | 8.33% |
| Multi-Armed Bandits | 1 | 8.33% |
| Thompson Sampling | 1 | 8.33% |
| Computational Efficiency | 1 | 8.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
