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ResearchFormerly WizWand

Accuracy Prediction on NAS-Bench-201 8 (whole dataset)

0.901Kendall's TauNAR-Former
0.389320.522160.6550.78784Jul 1, 2025
Updated 8mo ago

Evaluation results

MethodLinks
NAR-FormerBackbone=Transformer, Training Samples=10% (1563)2025.070.901
NN-FormerBackbone=Hybrid, Training Samples=10% (1563)2025.070.89
NAR-Former V2Backbone=Hybrid, Training Samples=10% (1563)2025.070.888
NN-FormerBackbone=Hybrid, Training Samples=5% (781)2025.070.879
NAR-Former V2Backbone=Hybrid, Training Samples=5% (781)2025.070.874
NN-FormerBackbone=Hybrid, Training Samples=3% (469)2025.070.86
NAR-FormerBackbone=Transformer, Training Samples=5% (781)2025.070.849
NAR-Former V2Backbone=Hybrid, Training Samples=3% (469)2025.070.846
NN-FormerBackbone=Hybrid, Training Samples=1% (156)2025.070.804
NAR-FormerBackbone=Transformer, Training Samples=3% (469)2025.070.79
PINATBackbone=Transformer, Training Samples=10% (1563)2025.070.784
GraphormerBackbone=Transformer, Training Samples=10% (1563)2025.070.776
PINATBackbone=Transformer, Training Samples=5% (781)2025.070.761
NAR-Former V2Backbone=Hybrid, Training Samples=1% (156)2025.070.752
TNASPBackbone=Transformer, Training Samples=10% (1563)2025.070.724
GraphormerBackbone=Transformer, Training Samples=5% (781)2025.070.719
PINATBackbone=Transformer, Training Samples=3% (469)2025.070.706
TNASPBackbone=Transformer, Training Samples=5% (781)2025.070.689
GraphormerBackbone=Transformer, Training Samples=3% (469)2025.070.68
GraphTransBackbone=Hybrid, Training Samples=10% (1563)2025.070.673
NAR-FormerBackbone=Transformer, Training Samples=1% (156)2025.070.66
NPBackbone=GNN, Training Samples=10% (1563)2025.070.646
TNASPBackbone=Transformer, Training Samples=3% (469)2025.070.64
NPBackbone=GNN, Training Samples=5% (781)2025.070.634
PINATBackbone=Transformer, Training Samples=1% (156)2025.070.631
GraphormerBackbone=Transformer, Training Samples=1% (156)2025.070.63
TNASPBackbone=Transformer, Training Samples=1% (156)2025.070.589
GraphTransBackbone=Hybrid, Training Samples=5% (781)2025.070.588
NPBackbone=GNN, Training Samples=3% (469)2025.070.584
GraphTransBackbone=Hybrid, Training Samples=3% (469)2025.070.55
NAOBackbone=LSTM, Training Samples=10% (1563)2025.070.526
NAOBackbone=LSTM, Training Samples=5% (781)2025.070.522
NAOBackbone=LSTM, Training Samples=1% (156)2025.070.493
NAOBackbone=LSTM, Training Samples=3% (469)2025.070.47
NPBackbone=GNN, Training Samples=1% (156)2025.070.413
GraphTransBackbone=Hybrid, Training Samples=1% (156)2025.070.409