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ResearchFormerly WizWand

Accuracy Prediction on NAS-Bench-101 (test)

0.871Kendall's TauNAR-Former
0.648440.706220.7640.82178Jun 19, 2023
Updated 8mo ago

Evaluation results

MethodLinks
NAR-FormerBackbone=Transformer, Training Samples=4236 (1%)2023.060.871
NAR-Former V2Backbone=Transformer, Training Samples=4236 (1%)2023.060.861
TNASPBackbone=Transformer, Training Samples=4236 (1%)2023.060.82
TNASP + SEBackbone=Transformer, Training Samples=4236 (1%)2023.060.82
ReNASBackbone=CNN, Training Samples=4236 (1%)2023.060.816
NAO+SEBackbone=LSTM, Training Samples=4236 (1%)2023.060.787
NAOBackbone=LSTM, Training Samples=4236 (1%)2023.060.775
NAR-Former V2Backbone=Transformer, Training Samples=424 (0.1%)2023.060.773
NP + SEBackbone=GNN, Training Samples=4236 (1%)2023.060.773
NPBackbone=GNN, Training Samples=4236 (1%)2023.060.769
NAR-FormerBackbone=Transformer, Training Samples=424 (0.1%)2023.060.765
TNASP + SEBackbone=Transformer, Training Samples=424 (0.1%)2023.060.722
TNASPBackbone=Transformer, Training Samples=424 (0.1%)2023.060.705
NP + SEBackbone=GNN, Training Samples=424 (0.1%)2023.060.684
NAO+SEBackbone=LSTM, Training Samples=424 (0.1%)2023.060.68
NPBackbone=GNN, Training Samples=424 (0.1%)2023.060.679
NAOBackbone=LSTM, Training Samples=424 (0.1%)2023.060.666
ReNASBackbone=CNN, Training Samples=424 (0.1%)2023.060.657