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Abstract: Meta-learning transfers knowledge across tasks and domains to learn new tasks efficiently, which has shown promise in drug discovery. However, the generalization ability of current meta-learning methods is limited by task heterogeneity and memorization. In this talk, I will first introduce two general principles to improve the generalization ability in meta-learning: organization and augmentation. Then, I will present several concrete few-shot drug discovery instantiations of using each principle. This includes algorithms to organize and adapt knowledge and a simple method for sufficiently overcoming task memorization. The remaining challenges and promising future research directions will also be discussed.

Speaker:  Huaxiu Yao - https://huaxiuyao.mystrikingly.com/  

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