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Found 3 papers, 1 papers with code
Contrastive Corpus Attribution for Explaining Representations
To address this, we propose contrastive corpus similarity, a novel and semantically meaningful scalar explanation output based on a reference corpus and a contrasting foil set of samples.
Graph Neural Networks Including Sparse Interpretability
Through a single self-attention layer, a GISST model learns an importance probability for each node feature and edge in the input graph.
Predicting Inpatient Discharge Prioritization With Electronic Health Records
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care.
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