**arXiv ID:** 1807.05076 **Authors:** Tsendsuren Munkhdalai, Adam Trischler **Published:** 2018-07-12T14:40:06Z **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 impleme...
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# Metalearning with Hebbian Fast Weights
**arXiv ID:** 1807.05076
**Authors:** Tsendsuren Munkhdalai, Adam Trischler
**Published:** 2018-07-12T14:40:06Z
**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.
## Skill Description
This skill is generated from the arXiv paper: Metalearning with Hebbian Fast Weights (1807.05076).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:1807.05076](http://arxiv.org/abs/1807.05076v1)
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