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9.22k
βŒ€
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