AHA！一种用于情景机器学习的“人工海马算法”
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning
摘要：
大多数ML研究关注i.i.d.的缓慢统计学习。从大的，标记的数据集的样本。动物不会这样学习。动物学习的一个令人羡慕的特征是“情景式”学习--在仅仅一次经历之后，不需要提供标签，就能将特定的经历作为现有概念的组成来记忆。然后，新知识可用于区分相似的经历、概括不同类别的知识，并有选择地巩固为长期记忆。海马体对这些能力至关重要。AHA是海马的生物学上合理的计算模型。与大多数机器学习模型不同，AHA在没有外部标签的情况下进行训练，并且仅使用本地信用分配。我们在Omniglot一次性分类基准的超集中展示了AHA。扩展的基准涵盖了更广泛的已知海马功能测试模式分离，完成，并召回原始输入。这些功能都在计算模型的单个配置内执行。尽管有这些限制，图像分类结果与传统的深度卷积ANN相当。
英文摘要：
The majority of ML research concerns slow, statistical learning of i.i.d. samples from large, labelled datasets. Animals do not learn this way. An enviable characteristic of animal learning is `episodic' learning - the ability to memorise a specific experience as a composition of existing concepts, after just one experience, without provided labels. The new knowledge can then be used to distinguish between similar experiences, to generalise between classes, and to selectively consolidate to long-term memory. The Hippocampus is known to be vital to these abilities. AHA is a biologically-plausible computational model of the Hippocampus. Unlike most machine learning models, AHA is trained without external labels and uses only local credit assignment. We demonstrate AHA in a superset of the Omniglot one-shot classification benchmark. The extended benchmark covers a wider range of known hippocampal functions by testing pattern separation, completion, and recall of original input. These functions are all performed within a single configuration of the computational model. Despite these constraints, image classification results are comparable to conventional deep convolutional ANNs.
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