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A Simple Neural Attentive Meta-Learner
[article]
2018
arXiv
pre-print
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively
arXiv:1707.03141v3
fatcat:ign7zqjvunbtdly4eyoma7fzkq