task_path
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β | dataset
stringlengths 1
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β | model_name
stringlengths 1
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β | paper_url
stringlengths 21
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β | metric_name
stringlengths 1
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β | metric_value
stringlengths 1
9.22k
β |
|---|---|---|---|---|---|
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 β Base
|
LightGBM
|
https://arxiv.org/abs/2408.12989v1
|
Recall @ 1% FPR
|
25.2%
|
Active Speaker Detection > Fraud Detection
|
BAF β Base
|
FIGS
|
https://arxiv.org/abs/2408.12989v1
|
Recall @ 1% FPR
|
21%
|
Active Speaker Detection > Fraud Detection
|
BAF β Base
|
CART+RIFF
|
https://arxiv.org/abs/2408.12989v1
|
Recall @ 1% FPR
|
18.4%
|
Active Speaker Detection > Fraud Detection
|
BAF β Base
|
CART
|
https://arxiv.org/abs/2408.12989v1
|
Recall @ 1% FPR
|
16%
|
Active Speaker Detection > Fraud Detection
|
BAF β Base
|
FIGS+RIFF
|
https://arxiv.org/abs/2408.12989v1
|
Recall @ 1% FPR
|
15.8%
|
Active Speaker Detection > Fraud Detection
|
BAF β Base
|
FIGU+RIFF
|
https://arxiv.org/abs/2408.12989v1
|
Recall @ 1% FPR
|
15.5%
|
Active Speaker Detection > Fraud Detection
|
BAF β Base
|
LightGBM
|
https://arxiv.org/abs/2401.05240v2
|
Recall @ 5% FPR
|
54.3%
|
Active Speaker Detection > Fraud Detection
|
BAF β Base
|
CatBoost
|
https://arxiv.org/abs/2401.05240v2
|
Recall @ 5% FPR
|
52.4%
|
Active Speaker Detection > Fraud Detection
|
BAF β 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 β 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 β Base
|
MLPβNN
|
https://arxiv.org/abs/2401.05240v2
|
Recall @ 5% FPR
|
49.6%
|
Active Speaker Detection > Fraud Detection
|
BAF β 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 β 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 β 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 β 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 β 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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