Intelligence relies on an agent's knowledge of what it does not know. This
capability can be assessed based on the quality of joint predictions of labels
across multiple inputs. In principle, ensemble-based approaches produce
effective joint predictions, but the computational costs of training large
ensembles can become prohibitive. We introduce the epinet: an architecture that
can supplement any conventional neural network, including large pretrained
models, and can be trained with modest incremental computation to estimate
uncertainty. With an epinet, conventional neural networks outperform very large
ensembles, consisting of hundreds or more particles, with orders of magnitude
less computation. The epinet does not fit the traditional framework of Bayesian
neural networks. To accommodate development of approaches beyond BNNs, such as
the epinet, we introduce the epistemic neural network (ENN) as an interface for
models that produce joint predictions.

本文介绍了一种能够辅助任何传统神经网络，包括大型预训练模型，能够用较少的计算量来预测不确定性的架构 ——epinet。使用 epinet，传统神经网络在预测标签上的表现优于包含数百个或更多粒子的非常大的许多集合，而且使用的计算资源和数据也大大降低了。