Bayesian Neural Network Regression on Wine (test)
0.604RMSESVGD
Evaluation results
| Method | Links | |
|---|---|---|
| SVGDruns=20, architecture=Two-hidden-layer ReLU neural network, hidden_neurons=50, particles=102026.01 | 0.604 | |
| SVGD2025.09 | 0.604 | |
| AIGruns=20, architecture=Two-hidden-layer ReLU neural network, hidden_neurons=50, particles=102026.01 | 0.606 | |
| AIG2025.09 | 0.606 | |
| ARWPruns=20, architecture=Two-hidden-layer ReLU neural network, hidden_neurons=50, particles=10, optimization=Nesterov2026.01 | 0.608 | |
| PBRWP2025.09 | 0.612 | |
| WGFruns=20, architecture=Two-hidden-layer ReLU neural network, hidden_neurons=50, particles=102026.01 | 0.614 | |
| WGF2025.09 | 0.614 | |
| BRWPruns=20, architecture=Two-hidden-layer ReLU neural network, hidden_neurons=50, particles=102026.01 | 0.623 | |
| BRWP2025.09 | 0.623 | |
| Adamruns=20, architecture=Two-hidden-layer ReLU neural network, hidden_neurons=50, particles=102026.01 | 0.629 | |
| Adam2025.09 | 0.629 |