Graph Regression On Lipophilicity
평가 지표
RMSE@80%Train
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | RMSE@80%Train |
|---|---|
| censnet-convolution-with-edge-node-switching | 1.16 |
| censnet-convolution-with-edge-node-switching | 0.93 |
| molecular-graph-convolutions-moving-beyond | - |
| how-powerful-are-graph-neural-networks | - |
| masked-attention-is-all-you-need-for-graphs | - |
| molecular-property-prediction-a-multilevel | - |
| simplifying-graph-convolutional-networks | - |
| optimal-transport-graph-neural-networks | - |
| molecular-property-prediction-a-multilevel | - |
| graph-attention-networks | - |
| attention-based-graph-neural-network-for-semi | - |
| dropgnn-random-dropouts-increase-the | - |
| graph-neural-networks-with-convolutional-arma | - |
| recipe-for-a-general-powerful-scalable-graph | - |
| molecule-property-prediction-based-on-spatial | - |
| principal-neighbourhood-aggregation-for-graph | - |
| neural-message-passing-for-quantum-chemistry | - |
| semi-supervised-classification-with-graph | - |
| pure-transformers-are-powerful-graph-learners | - |
| convolutional-networks-on-graphs-for-learning | - |
| censnet-convolution-with-edge-node-switching | 1.15 |
| how-attentive-are-graph-attention-networks | - |
| do-transformers-really-perform-bad-for-graph | - |