GRAIL trains graph predictors via imitation learning by modeling generation as sequential decisions on partial graph embeddings, matching or exceeding prior methods on 18 benchmarks.
Asymmetric loss for multi-label classification
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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OrthTD disentangles shared and task-specific subspaces via orthogonality in a multimodal Transformer, yielding 87.5% average AUC and 37.2% average AUPRC on 12,430 surgical patients, outperforming prior multi-task and tabular methods especially on imbalanced outcomes.
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Building Deep Graph Predictors with Graph Imitation Learning
GRAIL trains graph predictors via imitation learning by modeling generation as sequential decisions on partial graph embeddings, matching or exceeding prior methods on 18 benchmarks.
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Disentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data
OrthTD disentangles shared and task-specific subspaces via orthogonality in a multimodal Transformer, yielding 87.5% average AUC and 37.2% average AUPRC on 12,430 surgical patients, outperforming prior multi-task and tabular methods especially on imbalanced outcomes.