AIM is a new saliency-guided adversarial feature replacement method to evaluate faithfulness of saliency maps and reliability of masking operators on image, audio, and EEG tasks.
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3 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
3
Pith papers citing it
7
external citations · external index
years
2026 3verdicts
UNVERDICTED 3representative citing papers
D-Shap reformulates dynamic Shapley valuation as structured matrix maintenance exploiting utility and coalition locality to support millisecond task updates and orders-of-magnitude cheaper player updates.
Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.
citing papers explorer
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AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Maps
AIM is a new saliency-guided adversarial feature replacement method to evaluate faithfulness of saliency maps and reliability of masking operators on image, audio, and EEG tasks.
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Dynamic Shapley Computation
D-Shap reformulates dynamic Shapley valuation as structured matrix maintenance exploiting utility and coalition locality to support millisecond task updates and orders-of-magnitude cheaper player updates.
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Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis
Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.