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ResearchFormerly WizWand

Multi-Instance Learning TPAUC Maximization on MUSK2 (test)

0.867Mean TPAUCAUC-M (att)
0.511320.603660.6960.78834Oct 5, 2023
Updated 8mo ago

Evaluation results

MethodLinks
AUC-M (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.100.867
SONT (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.100.867
MIDAM (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.100.819
SOTAS (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.100.819
MIDAM (smx)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=stochastic smoothed-max2023.100.8
SONT (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.100.8
AUC-M (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.100.783
MIDAM (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.100.717
SONT (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.100.7
SOTAS (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.100.683
AUC-M (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.100.675
MIDAM (smx)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=stochastic smoothed-max2023.100.667
MIDAM (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.100.6
SOTAS (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.100.6
MIDAM (smx)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=stochastic smoothed-max2023.100.525