Jan 28, 2022 · Model-Agnostic Meta-Learning (MAML) aims to find initial weights that allow fast adaptation to new tasks. The adaptation (inner loop) ...
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate ... Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 2017. [2] ...
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Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate ... In this paper, we study the effect of the adaptation learning rate in meta-learning ...
Jun 4, 2023 · Recently, a paradigm known as meta-learning has emerged as a powerful means of learning multi-task representations. This was sparked in large ...
Apr 29, 2024 · Conclusions: Model-Agnostic Meta-Learning (MAML) is a beacon of progress in the quest for adaptable machine learning systems. Its ability to ...
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Unraveling model-agnostic meta-learning via the adaptation learning rate. In International Conference on Learning Representations, 2021. Nicolas Zucchet ...
Mar 17, 2021 · Figure 1: Graphical model of data generation in mixed linear regression. We show that, intuitively, the optimal learning rate at meta-testing ( ...
Jan 4, 2023 · In this paper, we investigate how the differences in the data distributions between the old tasks and the new target task impact performance in ...
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This paper studies the generalization of a widely used meta-learning approach, Model-Agnostic Meta-. Learning (MAML), which aims to find a good initialization ...
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and ...
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