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ResearchFormerly WizWand

Regression on 3droad (test)

0.101RMSEMATÉRN (BBMM)
0.07860.22980.3810.5322Jun 28, 2022Jul 14, 2022Jul 30, 2022Aug 16, 2022Sep 1, 2022Sep 17, 2022Oct 4, 2022
Updated 8mo ago

Evaluation results

MethodLinks
MATÉRN (BBMM)n (Samples)=278319, d (Dimensions)=3, GPU Type=Tesla V100-SXM2-32GB, Feature Transformation=ECDF, Precision=Pnew2022.100.101—0.909126.37
BTEn (Samples)=278319, d (Dimensions)=3, GPU Type=Tesla V100-SXM2-16GB, Feature Transformation=ECDF, Precision=Pnew2022.100.104—-1.28514.2
BTn (Samples)=278319, d (Dimensions)=3, GPU Type=Tesla V100-SXM2-16GB, Feature Transformation=ECDF, Precision=Pnew2022.100.105—-1.267—
Iterative GPlengthscale=shared across dimensions, Time (hours)=7.06, GPUs=82022.060.106———
Iterative GPlengthscale=per dimension, Time (hours)=1, GPUs=82022.060.111.239——
SGPRInducing Points=10000, Time (hours)=41.83, GPUs=12022.060.17-0.322——
SGPRInducing Points=8000, Time (hours)=28.21, GPUs=12022.060.176-0.302——
SGPRInducing Points=5000, Time (hours)=11.33, GPUs=12022.060.19-0.228——
SGPRInducing Points=1000, Time (hours)=1.11, GPUs=12022.060.285-0.173——
SVGPn (Samples)=278319, d (Dimensions)=3, GPU Type=Tesla V100-SXM2-32GB, Feature Transformation=ECDF, Precision=Pnew2022.100.481—0.69734.09
SGPRn (Samples)=278319, d (Dimensions)=3, GPU Type=Tesla V100-SXM2-32GB, Feature Transformation=ECDF, Precision=Pnew2022.100.661—0.94312.01