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- W4313443056 abstract "In this paper, we introduce a novel variation of model-agnostic meta-learning, where an extra multiplicative parameter is introduced in the inner-loop adaptation. Our variation creates a shortcut in the parameter space for the inner-loop adaptation and increases model expressivity in a highly controllable manner. We show both theoretically and numerically that our variation alleviates the problem of conflicting gradients and improves training dynamics. We conduct experiments on 3 distinctive problems, including a toy classification problem for threshold comparison, a regression problem for wavelet transform, and a classification problem on MNIST. We also discuss ways to generalize our method to a broader class of problems." @default.
- W4313443056 created "2023-01-06" @default.
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- W4313443056 date "2022-12-28" @default.
- W4313443056 modified "2023-10-18" @default.
- W4313443056 title "Wormhole MAML: Meta-Learning in Glued Parameter Space" @default.
- W4313443056 doi "https://doi.org/10.48550/arxiv.2212.14094" @default.
- W4313443056 hasPublicationYear "2022" @default.
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