Keywords: metalearning, memory, few-shot, relational, self-attention, classification, sequential, reasoning, working memory, episodic memory
TL;DR: We introduce a model which generalizes quickly from few observations by storing surprising information and attending over the most relevant data at each time point.
Abstract: The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall. We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint. In addition, its memory compression allows it to scale to thousands of unknown labels. Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification. In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.
Code: [![github](/images/github_icon.svg) cogentlabs/apl](https://github.com/cogentlabs/apl)