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3D Chern-Simons prediction on Kühn 3-torus L=8 (train)

1R^2 ScoreFAKE-FLAT (κ=0)
-0.05040.22230.4950.7677Jun 11, 2026
Updated 4mo ago

Evaluation results

MethodLinks
FAKE-FLAT (κ=0)M (Training set size)=642026.061
FAKE-FLAT (κ=0)M (Training set size)=2562026.061
FAKE-FLAT (κ=0)M (Training set size)=10242026.061
CNN-3DM (Training set size)=642026.061
CNN-3DM (Training set size)=2562026.061
CNN-3DM (Training set size)=10242026.061
Adjusted cup-product layerM (Training set size)=642026.060.99
Adjusted cup-product layerM (Training set size)=2562026.060.99
Adjusted cup-product layerM (Training set size)=10242026.060.99
Adjusted cup-product layerM (Training set size)=40962026.060.99
Adjusted cup-product layerM (Training set size)=163842026.060.99
FAKE-FLAT (κ=0)M (Training set size)=40962026.060.85
CNN-3DM (Training set size)=40962026.060.45
CNN-3DM (Training set size)=163842026.060.34
FAKE-FLAT (κ=0)M (Training set size)=163842026.060.2
GNNM (Training set size)=2562026.060
GNNM (Training set size)=10242026.060
GNNM (Training set size)=40962026.060
GNNM (Training set size)=163842026.060
MPSN-SINM (Training set size)=642026.060
MPSN-SINM (Training set size)=2562026.060
MPSN-SINM (Training set size)=10242026.060
MPSN-SINM (Training set size)=40962026.060
MPSN-SINM (Training set size)=163842026.060
SNN-EbliM (Training set size)=642026.060
SNN-EbliM (Training set size)=2562026.060
SNN-EbliM (Training set size)=10242026.060
SNN-EbliM (Training set size)=40962026.060
SNN-EbliM (Training set size)=163842026.060
GNNM (Training set size)=642026.06-0.01