task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | ParaSurf | https://doi.org/10.1093/bioinformatics/btaf062 | AUC-PR | 0.781 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | ParaSurf | https://doi.org/10.1093/bioinformatics/btaf062 | AUC-ROC | 0.967 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | MIPE | https://arxiv.org/abs/2405.20668v1 | AUC-PR | 0.741 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | MIPE | https://arxiv.org/abs/2405.20668v1 | AUC-ROC | 0.927 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | Pesto | https://www.nature.com/articles/s41467-023-37701-8 | AUC-PR | 0.724 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | Pesto | https://www.nature.com/articles/s41467-023-37701-8 | AUC-ROC | 0.856 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | PECAN | https://academic.oup.com/bioinformatics/article/36/13/3996/5823885 | AUC-PR | 0.713 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | PECAN | https://academic.oup.com/bioinformatics/article/36/13/3996/5823885 | AUC-ROC | 0.915 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | Parapred | https://academic.oup.com/bioinformatics/article/34/17/2944/4972995 | AUC-PR | 0.652 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | Parapred | https://academic.oup.com/bioinformatics/article/34/17/2944/4972995 | AUC-ROC | 0.868 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | Paragraph | https://academic.oup.com/bioinformatics/article/39/1/btac732/6825310 | AUC-PR | 0.650 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | Paragraph | https://academic.oup.com/bioinformatics/article/39/1/btac732/6825310 | AUC-ROC | 0.927 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | AG-Fast-Parapred | http://arxiv.org/abs/1806.04398v1 | AUC-PR | 0.612 |
Binding Site Prediction > Antibody-antigen binding prediction | MIPE | AG-Fast-Parapred | http://arxiv.org/abs/1806.04398v1 | AUC-ROC | 0.883 |
Binding Site Prediction > Antibody-antigen binding prediction | Paragraph Expanded | ParaSurf | https://doi.org/10.1093/bioinformatics/btaf062 | AUC-PR | 0.793 |
Binding Site Prediction > Antibody-antigen binding prediction | Paragraph Expanded | ParaSurf | https://doi.org/10.1093/bioinformatics/btaf062 | AUC-ROC | 0.967 |
Binding Site Prediction > Antibody-antigen binding prediction | Paragraph Expanded | Paragraph | https://academic.oup.com/bioinformatics/article/39/1/btac732/6825310 | AUC-PR | 0.725 |
Binding Site Prediction > Antibody-antigen binding prediction | Paragraph Expanded | Paragraph | https://academic.oup.com/bioinformatics/article/39/1/btac732/6825310 | AUC-ROC | 0.934 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | ParaSurf | https://doi.org/10.1093/bioinformatics/btaf062 | AUC-PR | 0.733 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | ParaSurf | https://doi.org/10.1093/bioinformatics/btaf062 | AUC-ROC | 0.955 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | Paragraph | https://academic.oup.com/bioinformatics/article/39/1/btac732/6825310 | AUC-PR | 0.696 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | Paragraph | https://academic.oup.com/bioinformatics/article/39/1/btac732/6825310 | AUC-ROC | 0.934 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | PECAN | https://academic.oup.com/bioinformatics/article/36/13/3996/5823885 | AUC-PR | 0.675 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | PECAN | https://academic.oup.com/bioinformatics/article/36/13/3996/5823885 | AUC-ROC | 0.952 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | Parapred | https://academic.oup.com/bioinformatics/article/34/17/2944/4972995 | AUC-PR | 0.646 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | Parapred | https://academic.oup.com/bioinformatics/article/34/17/2944/4972995 | AUC-ROC | 0.930 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | Daberdaku | https://academic.oup.com/bioinformatics/article/35/11/1870/5161081 | AUC-PR | 0.545 |
Binding Site Prediction > Antibody-antigen binding prediction | PECAN | Daberdaku | https://academic.oup.com/bioinformatics/article/35/11/1870/5161081 | AUC-ROC | 0.923 |
BEV Segmentation | SimBEV | BEVFusion | https://arxiv.org/abs/2502.01894v2 | mIoU | 0.5 |
BEV Segmentation | SimBEV | BEVFusion | https://arxiv.org/abs/2502.01894v2 | pedestrian | 0.2 |
BEV Segmentation | SimBEV | BEVFusion | https://arxiv.org/abs/2502.01894v2 | road | 0.884 |
BEV Segmentation | SimBEV | BEVFusion | https://arxiv.org/abs/2502.01894v2 | car | 0.727 |
BEV Segmentation | SimBEV | BEVFusion | https://arxiv.org/abs/2502.01894v2 | truck | 0.745 |
BEV Segmentation | SimBEV | BEVFusion | https://arxiv.org/abs/2502.01894v2 | bus | 0.8 |
BEV Segmentation | SimBEV | BEVFusion | https://arxiv.org/abs/2502.01894v2 | motorcycle | 0.363 |
BEV Segmentation | SimBEV | BEVFusion | https://arxiv.org/abs/2502.01894v2 | bicycle | 0.036 |
BEV Segmentation | SimBEV | BEVFusion | https://arxiv.org/abs/2502.01894v2 | rider | 0.233 |
BEV Segmentation | SimBEV | UniTR | https://arxiv.org/abs/2502.01894v2 | mIoU | 0.497 |
BEV Segmentation | SimBEV | UniTR | https://arxiv.org/abs/2502.01894v2 | pedestrian | 0.275 |
BEV Segmentation | SimBEV | UniTR | https://arxiv.org/abs/2502.01894v2 | road | 0.928 |
BEV Segmentation | SimBEV | UniTR | https://arxiv.org/abs/2502.01894v2 | car | 0.738 |
BEV Segmentation | SimBEV | UniTR | https://arxiv.org/abs/2502.01894v2 | truck | 0.677 |
BEV Segmentation | SimBEV | UniTR | https://arxiv.org/abs/2502.01894v2 | bus | 0.517 |
BEV Segmentation | SimBEV | UniTR | https://arxiv.org/abs/2502.01894v2 | motorcycle | 0.365 |
BEV Segmentation | SimBEV | UniTR | https://arxiv.org/abs/2502.01894v2 | bicycle | 0.114 |
BEV Segmentation | SimBEV | UniTR | https://arxiv.org/abs/2502.01894v2 | rider | 0.362 |
BEV Segmentation | SimBEV | BEVFusion-L | https://arxiv.org/abs/2502.01894v2 | mIoU | 0.483 |
BEV Segmentation | SimBEV | BEVFusion-L | https://arxiv.org/abs/2502.01894v2 | pedestrian | 0.189 |
BEV Segmentation | SimBEV | BEVFusion-L | https://arxiv.org/abs/2502.01894v2 | road | 0.877 |
BEV Segmentation | SimBEV | BEVFusion-L | https://arxiv.org/abs/2502.01894v2 | car | 0.706 |
BEV Segmentation | SimBEV | BEVFusion-L | https://arxiv.org/abs/2502.01894v2 | truck | 0.735 |
BEV Segmentation | SimBEV | BEVFusion-L | https://arxiv.org/abs/2502.01894v2 | bus | 0.815 |
BEV Segmentation | SimBEV | BEVFusion-L | https://arxiv.org/abs/2502.01894v2 | motorcycle | 0.325 |
BEV Segmentation | SimBEV | BEVFusion-L | https://arxiv.org/abs/2502.01894v2 | bicycle | 0.036 |
BEV Segmentation | SimBEV | BEVFusion-L | https://arxiv.org/abs/2502.01894v2 | rider | 0.184 |
BEV Segmentation | SimBEV | UniTR+LSS | https://arxiv.org/abs/2502.01894v2 | mIoU | 0.476 |
BEV Segmentation | SimBEV | UniTR+LSS | https://arxiv.org/abs/2502.01894v2 | pedestrian | 0.129 |
BEV Segmentation | SimBEV | UniTR+LSS | https://arxiv.org/abs/2502.01894v2 | road | 0.933 |
BEV Segmentation | SimBEV | UniTR+LSS | https://arxiv.org/abs/2502.01894v2 | car | 0.728 |
BEV Segmentation | SimBEV | UniTR+LSS | https://arxiv.org/abs/2502.01894v2 | truck | 0.694 |
BEV Segmentation | SimBEV | UniTR+LSS | https://arxiv.org/abs/2502.01894v2 | bus | 0.585 |
BEV Segmentation | SimBEV | UniTR+LSS | https://arxiv.org/abs/2502.01894v2 | motorcycle | 0.359 |
BEV Segmentation | SimBEV | UniTR+LSS | https://arxiv.org/abs/2502.01894v2 | bicycle | 0.063 |
BEV Segmentation | SimBEV | UniTR+LSS | https://arxiv.org/abs/2502.01894v2 | rider | 0.316 |
BEV Segmentation | SimBEV | BEVFusion-C | https://arxiv.org/abs/2502.01894v2 | mIoU | 0.152 |
BEV Segmentation | SimBEV | BEVFusion-C | https://arxiv.org/abs/2502.01894v2 | pedestrian | 0 |
BEV Segmentation | SimBEV | BEVFusion-C | https://arxiv.org/abs/2502.01894v2 | road | 0.76 |
BEV Segmentation | SimBEV | BEVFusion-C | https://arxiv.org/abs/2502.01894v2 | car | 0.172 |
BEV Segmentation | SimBEV | BEVFusion-C | https://arxiv.org/abs/2502.01894v2 | truck | 0.051 |
BEV Segmentation | SimBEV | BEVFusion-C | https://arxiv.org/abs/2502.01894v2 | bus | 0.229 |
BEV Segmentation | SimBEV | BEVFusion-C | https://arxiv.org/abs/2502.01894v2 | motorcycle | 0 |
BEV Segmentation | SimBEV | BEVFusion-C | https://arxiv.org/abs/2502.01894v2 | bicycle | 0 |
BEV Segmentation | SimBEV | BEVFusion-C | https://arxiv.org/abs/2502.01894v2 | rider | 0 |
Active Speaker Detection | LRS3-TED | GestSync | https://arxiv.org/abs/2310.05304v1 | Accuracy | 87 % |
Active Speaker Detection > Fraud Detection | BAF – Base | LightGBM | https://arxiv.org/abs/2408.12989v1 | Recall @ 1% FPR | 25.2% |
Active Speaker Detection > Fraud Detection | BAF – Base | FIGS | https://arxiv.org/abs/2408.12989v1 | Recall @ 1% FPR | 21% |
Active Speaker Detection > Fraud Detection | BAF – Base | CART+RIFF | https://arxiv.org/abs/2408.12989v1 | Recall @ 1% FPR | 18.4% |
Active Speaker Detection > Fraud Detection | BAF – Base | CART | https://arxiv.org/abs/2408.12989v1 | Recall @ 1% FPR | 16% |
Active Speaker Detection > Fraud Detection | BAF – Base | FIGS+RIFF | https://arxiv.org/abs/2408.12989v1 | Recall @ 1% FPR | 15.8% |
Active Speaker Detection > Fraud Detection | BAF – Base | FIGU+RIFF | https://arxiv.org/abs/2408.12989v1 | Recall @ 1% FPR | 15.5% |
Active Speaker Detection > Fraud Detection | BAF – Base | LightGBM | https://arxiv.org/abs/2401.05240v2 | Recall @ 5% FPR | 54.3% |
Active Speaker Detection > Fraud Detection | BAF – Base | CatBoost | https://arxiv.org/abs/2401.05240v2 | Recall @ 5% FPR | 52.4% |
Active Speaker Detection > Fraud Detection | BAF – Base | LightGBM | https://link.springer.com/chapter/10.1007/978-3-031-76604-6_4 | Recall @ 5% FPR | 51.76% |
Active Speaker Detection > Fraud Detection | BAF – Base | 1D-CSNN | https://link.springer.com/chapter/10.1007/978-3-031-73503-5_11 | Recall @ 5% FPR | 50.35% |
Active Speaker Detection > Fraud Detection | BAF – Base | MLP–NN | https://arxiv.org/abs/2401.05240v2 | Recall @ 5% FPR | 49.6% |
Active Speaker Detection > Fraud Detection | BAF – Base | 1D-CSNN | https://link.springer.com/chapter/10.1007/978-3-031-76604-6_4 | Recall @ 5% FPR | 42.79% |
Active Speaker Detection > Fraud Detection | BAF – Variant IV | 1D-CSNN | https://link.springer.com/chapter/10.1007/978-3-031-76604-6_4 | Recall @ 5% FPR | 35.54% |
Active Speaker Detection > Fraud Detection | BAF – Variant II | 1D-CSNN | https://link.springer.com/chapter/10.1007/978-3-031-76604-6_4 | Recall @ 5% FPR | 47.08% |
Active Speaker Detection > Fraud Detection | BAF – Variant I | 1D-CSNN | https://link.springer.com/chapter/10.1007/978-3-031-76604-6_4 | Recall @ 5% FPR | 40.71% |
Active Speaker Detection > Fraud Detection | BAF – Variant V | 1D-CSNN | https://link.springer.com/chapter/10.1007/978-3-031-76604-6_4 | Recall @ 5% FPR | 34.96% |
Active Speaker Detection > Fraud Detection | Kaggle-Credit Card Fraud Dataset | DevNet | https://arxiv.org/abs/1911.08623v1 | AUC | 0.98 |
Active Speaker Detection > Fraud Detection | Kaggle-Credit Card Fraud Dataset | DevNet | https://arxiv.org/abs/1911.08623v1 | Average Precision | 0.69 |
Active Speaker Detection > Fraud Detection | Kaggle-Credit Card Fraud Dataset | XBNET | https://arxiv.org/abs/2106.05239v3 | Accuracy | 71.33 |
Active Speaker Detection > Fraud Detection | Healthcare Provider Fraud Detection Analysis | BiRank | https://doi.org/10.1007/s13385-024-00384-6 | AUC | 0.786 |
Active Speaker Detection > Fraud Detection | Healthcare Provider Fraud Detection Analysis | BiRank | https://doi.org/10.1007/s13385-024-00384-6 | AUPRC | 0.175 |
Active Speaker Detection > Fraud Detection | Healthcare Provider Fraud Detection Analysis | GraphSAGE | https://doi.org/10.1007/s13385-024-00384-6 | AUC | 0.668 |
Active Speaker Detection > Fraud Detection | Healthcare Provider Fraud Detection Analysis | GraphSAGE | https://doi.org/10.1007/s13385-024-00384-6 | AUPRC | 0.201 |
Active Speaker Detection > Fraud Detection | Healthcare Provider Fraud Detection Analysis | metapath2vec | https://doi.org/10.1007/s13385-024-00384-6 | AUC | 0.513 |
Active Speaker Detection > Fraud Detection | Healthcare Provider Fraud Detection Analysis | metapath2vec | https://doi.org/10.1007/s13385-024-00384-6 | AUPRC | 0.054 |
Active Speaker Detection > Fraud Detection | Yelp-Fraud | LEX-GNN | https://dl.acm.org/doi/10.1145/3627673.3679956 | AUC-ROC | 96.40 |
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