Accuracy Prediction on NAS-Bench-101 (test)
0.871Kendall's TauNAR-Former
Evaluation Results
| Method | Links | |
|---|---|---|
| NAR-FormerBackbone=Transformer, Training Samples=4236 (1%)2023.06 | 0.871 | |
| NAR-Former V2Backbone=Transformer, Training Samples=4236 (1%)2023.06 | 0.861 | |
| TNASPBackbone=Transformer, Training Samples=4236 (1%)2023.06 | 0.82 | |
| TNASP + SEBackbone=Transformer, Training Samples=4236 (1%)2023.06 | 0.82 | |
| ReNASBackbone=CNN, Training Samples=4236 (1%)2023.06 | 0.816 | |
| NAO+SEBackbone=LSTM, Training Samples=4236 (1%)2023.06 | 0.787 | |
| NAOBackbone=LSTM, Training Samples=4236 (1%)2023.06 | 0.775 | |
| NAR-Former V2Backbone=Transformer, Training Samples=424 (0.1%)2023.06 | 0.773 | |
| NP + SEBackbone=GNN, Training Samples=4236 (1%)2023.06 | 0.773 | |
| NPBackbone=GNN, Training Samples=4236 (1%)2023.06 | 0.769 | |
| NAR-FormerBackbone=Transformer, Training Samples=424 (0.1%)2023.06 | 0.765 | |
| TNASP + SEBackbone=Transformer, Training Samples=424 (0.1%)2023.06 | 0.722 | |
| TNASPBackbone=Transformer, Training Samples=424 (0.1%)2023.06 | 0.705 | |
| NP + SEBackbone=GNN, Training Samples=424 (0.1%)2023.06 | 0.684 | |
| NAO+SEBackbone=LSTM, Training Samples=424 (0.1%)2023.06 | 0.68 | |
| NPBackbone=GNN, Training Samples=424 (0.1%)2023.06 | 0.679 | |
| NAOBackbone=LSTM, Training Samples=424 (0.1%)2023.06 | 0.666 | |
| ReNASBackbone=CNN, Training Samples=424 (0.1%)2023.06 | 0.657 |