Accuracy Prediction on NAS-Bench-101 100 samples (test)
0.802Kendall's TauNAR-Former V2
Evaluation Results
| Method | Links | |
|---|---|---|
| NAR-Former V2Backbone=Transformer, Training Samples=424 (0.1%)2023.06 | 0.802 | |
| NAR-FormerBackbone=Transformer, Training Samples=424 (0.1%)2023.06 | 0.801 | |
| TNASP + SEBackbone=Transformer, Training Samples=424 (0.1%)2023.06 | 0.754 | |
| TNASPBackbone=Transformer, Training Samples=424 (0.1%)2023.06 | 0.752 | |
| CTNASBackbone=GNN, Training Samples=424 (0.1%)2023.06 | 0.751 | |
| NAO+SEBackbone=LSTM, Training Samples=424 (0.1%)2023.06 | 0.732 | |
| NP + SEBackbone=GNN, Training Samples=424 (0.1%)2023.06 | 0.713 | |
| NPBackbone=GNN, Training Samples=424 (0.1%)2023.06 | 0.71 | |
| NAOBackbone=LSTM, Training Samples=424 (0.1%)2023.06 | 0.704 | |
| ReNASBackbone=CNN, Training Samples=424 (0.1%)2023.06 | 0.634 |