Metalearning with Hebbian Fast Weights

Tsendsuren Munkhdalai; Adam Trischler

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arXiv · 1807.05076

Metalearning with Hebbian Fast Weights

Tsendsuren Munkhdalai·Adam Trischler

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Abstract

We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast weights constructed by a Hebbian learning rule implement one-shot binding for each new task. On the Omniglot, Mini-ImageNet, and Penn Treebank one-shot learning benchmarks, our model achieves state-of-the-art results.

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BibTeX RIS

Tsendsuren Munkhdalai, Adam Trischler. 2018-07-12. Metalearning with Hebbian Fast Weights. https://arxiv.org/abs/1807.05076

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