Regression on Power (RMSE)
3.72RMSEGP
Evaluation Results
| Method | Links | |
|---|---|---|
| GPLearning Rate Regime=Low2026.07 | 3.72 | |
| LDBLLLearning Rate Regime=Low2026.07 | 3.87 | |
| MFVILearning Rate Regime=Low2026.07 | 3.87 | |
| CARD2022.06 | 3.93 | |
| BDKNLearning Rate Regime=Low2026.07 | 3.93 | |
| GPExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=322026.07 | 3.95 | |
| DELearning Rate Regime=Low2026.07 | 3.98 | |
| Deep Ensembles2022.06 | 4.02 | |
| MC Dropout2022.06 | 4.04 | |
| BLLLearning Rate Regime=Low2026.07 | 4.05 | |
| PBP2022.06 | 4.1 | |
| GCDS2022.06 | 4.11 | |
| VBLLLearning Rate Regime=Low2026.07 | 4.22 | |
| DKLLearning Rate Regime=Low2026.07 | 4.36 | |
| DKLExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.07 | 4.44 | |
| BDKNExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons), Feature maps (H)=52026.07 | 4.79 | |
| BLLExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.07 | 6.33 | |
| VBLLExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.07 | 8.38 | |
| MFVIExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.07 | 9.46 | |
| LDBLLExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.07 | 14.93 | |
| DEExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.07 | 3,424.99 |