Machine learning systems are often designed under the assumption that they will be deployed as a static model in a single static region of the world. However, the world is constantly changing, such that the future no longer looks exactly like the past, and even in relatively static settings, the system may be deployed in new, unseen parts of its world. While such continuous shifts in the data distribution can place major challenges on models acquired in machine learning, the model need not be static either: it can and should adapt. In this talk, I’ll discuss how we can allow deep networks to be robust to such distribution shift via adaptation. I will focus on meta-learning algorithms that enable this adaptation to be fast, first introducing the concept of meta-learning, then briefly overviewing several successful applications of meta-learning ranging from robotics to drug design, and finally discussing several recent works at the frontier of meta-learning research.
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