As sequential neural architectures become deeper and more complex, uncertainty estimation is more and more challenging. Efforts in quantifying uncertainty often rely on specific training procedures, and bear additional computational costs due to the dimensionality of such models. In this paper, we propose to decompose a classification or regression task in two steps: a representation learning stage to learn low-dimensional states, and a state space model for uncertainty estimation. This approach allows to separate representation learning and design of generative models. We demonstrate how predictive distributions can be estimated on top of an existing and trained neural network, by adding a state space-based last layer whose parameters are estimated with Sequential Monte Carlo methods. We apply our proposed methodology to the hourly estimation of Electricity Transformer Oil temperature, a publicly benchmarked dataset. Our model accounts for the noisy data structure, due to unknown or unavailable variables, and is able to provide confidence intervals on predictions.

翻译：随着序列神经架构日益深入和复杂，不确定性估计变得愈发具有挑战性。量化不确定性的努力往往依赖于特定的训练过程，并因模型维度的增加而带来额外的计算成本。本文提出将分类或回归任务分解为两个步骤：表示学习阶段用于学习低维状态，以及状态空间模型用于不确定性估计。该方法能够分离表示学习与生成模型的设计。我们展示了如何通过添加基于状态空间的最后一层（其参数通过序贯蒙特卡洛方法估计）在现有已训练神经网络之上估计预测分布。将所提方法应用于电力变压器油温的小时级估计（一个公开基准数据集）。该模型能够处理因未知或不可得变量导致的噪声数据结构，并提供预测的置信区间。