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• W3128249273 abstract "Deep models require a large amount of data perform well. When data is scarce for a target task, we can transfer the knowledge gained by training on similar tasks quickly the target. A successful approach is meta-learning, or learn a distribution of tasks, where learning is represented by an outer loop, and to learn by an inner loop of gradient descent. However, a number of recent empirical studies argue that the inner loop is unnecessary and more simple models work equally well or even better. We study the performance of MAML as a function of the rate of the inner loop, where zero rate implies that there is no inner loop. Using random matrix theory and exact solutions of linear models, we calculate an algebraic expression for the test loss of MAML applied mixed linear regression and nonlinear regression with overparameterized models. Surprisingly, while the optimal rate for adaptation is positive, we find that the optimal rate for training is always negative, a setting that has never been considered before. Therefore, not only does the performance increase by decreasing the rate zero, as suggested by recent work, but it can be increased even further by decreasing the rate negative values. These results help clarify under what circumstances meta-learning performs best." @default.
• W3128249273 created "2021-02-15" @default.
• W3128249273 creator A5079155200 @default.
• W3128249273 date "2021-02-01" @default.
• W3128249273 modified "2023-09-25" @default.
• W3128249273 title "Meta-learning with negative learning rates" @default.
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• W3128249273 hasPublicationYear "2021" @default.
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