Accuracy
Out-Of-Distribution Detection with Diversification (Provably)
Neural Information Processing Systems
Out-of-distribution (OOD) detection is crucial for ensuring reliable deployment of machine learning models. Recent advancements focus on utilizing easily accessible auxiliary outliers (e.g., data from the web or other datasets) in training.
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On the Scalability of GNNs for Molecular Graphs
Neural Information Processing Systems
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