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ResearchFormerly WizWand

Regression on Power (RMSE)

3.72RMSEGP
-133.1308790.61211,714.3552,638.0979Jun 15, 2022Feb 17, 2023Oct 22, 2023Jun 25, 2024Feb 27, 2025Nov 1, 2025Jul 7, 2026
Updated 3mo ago

Evaluation results

MethodLinks
GPLearning Rate Regime=Low2026.073.72
LDBLLLearning Rate Regime=Low2026.073.87
MFVILearning Rate Regime=Low2026.073.87
CARD2022.063.93
BDKNLearning Rate Regime=Low2026.073.93
GPExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=322026.073.95
DELearning Rate Regime=Low2026.073.98
Deep Ensembles2022.064.02
MC Dropout2022.064.04
BLLLearning Rate Regime=Low2026.074.05
PBP2022.064.1
GCDS2022.064.11
VBLLLearning Rate Regime=Low2026.074.22
DKLLearning Rate Regime=Low2026.074.36
DKLExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.074.44
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)=52026.074.79
BLLExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.076.33
VBLLExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.078.38
MFVIExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.079.46
LDBLLExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.0714.93
DEExperimental setting=1 (high learning rate), Learning rate=0.1, Training epochs=40, Batch size=32, Architecture=2 hidden layers (50 neurons)2026.073,424.99