Fortress stabilizes query-to-app relevance models by pruning features that cause inconsistent predictions across time periods while retaining predictive power from engagement signals.
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AIMEN trains an ensemble of neural networks on CTGAN-augmented data to predict adverse labor outcomes at 0.784 F1 and produces sparse counterfactual explanations identifying changes in two to three attributes.
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Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning
Fortress stabilizes query-to-app relevance models by pruning features that cause inconsistent predictions across time periods while retaining predictive power from engagement signals.
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Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health
AIMEN trains an ensemble of neural networks on CTGAN-augmented data to predict adverse labor outcomes at 0.784 F1 and produces sparse counterfactual explanations identifying changes in two to three attributes.