Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data. We present a method that mitigates these issues by converting NNs from weight space to function space, via a dual parameterization. Importantly, the dual parameterization enables us to formulate a sparse representation that captures information from the entire data set. This offers a compact and principled way of capturing uncertainty and enables us to incorporate new data without retraining whilst retaining predictive performance. We provide proof-of-concept demonstrations with the proposed approach for quantifying uncertainty in supervised learning on UCI benchmark tasks.

翻译：深度神经网络（NNs）已知缺乏不确定性估计，且难以整合新数据。本文提出一种方法，通过双参数化将神经网络从权重空间转换为函数空间，从而缓解这些问题。重要的是，双参数化使我们能够构建一种稀疏表征，捕捉来自整个数据集的信息。这提供了一种紧凑且规范的方式来表示不确定性，并使我们能够在无需重新训练的情况下整合新数据，同时保持预测性能。我们通过UCI基准任务上的监督学习不确定性量化实验，对提出的方法进行了概念验证演示。