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- W4387687199 abstract "In-context learning has become an important approach for few-shot learning in Large Language Models because of its ability to rapidly adapt to new tasks without fine-tuning model parameters. However, it is restricted to applications in natural language and inapplicable to other domains. In this paper, we adapt the concepts underpinning in-context learning to develop a new algorithm for few-shot molecular property prediction. Our approach learns to predict molecular properties from a context of (molecule, property measurement) pairs and rapidly adapts to new properties without fine-tuning. On the FS-Mol and BACE molecular property prediction benchmarks, we find this method surpasses the performance of recent meta-learning algorithms at small support sizes and is competitive with the best methods at large support sizes." @default.
- W4387687199 created "2023-10-17" @default.
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- W4387687199 date "2023-10-13" @default.
- W4387687199 modified "2023-10-18" @default.
- W4387687199 title "In-Context Learning for Few-Shot Molecular Property Prediction" @default.
- W4387687199 doi "https://doi.org/10.48550/arxiv.2310.08863" @default.
- W4387687199 hasPublicationYear "2023" @default.
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