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RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction
Machine learning-assisted retrosynthesis prediction models have been gaining widespread adoption, though their performances oftentimes degrade significantly when deployed in real-world applications embracing out-of-distribution (OOD) molecules or reactions.
Minimizing Memorization in Meta-learning: A Causal Perspective
Meta-learning has emerged as a potent paradigm for quick learning of few-shot tasks, by leveraging the meta-knowledge learned from meta-training tasks.
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