A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and cast interpretability as a statistical inference problem. We propose a general deep probabilistic model designed to produce interpretable predictions. The model parameters can be learned via maximum likelihood, and the method can be adapted to any predictor network architecture and any type of prediction problem. Our method is a case of amortized interpretability models, where a neural network is used as a selector to allow for fast interpretation at inference time. Several popular interpretability methods are shown to be particular cases of regularised maximum likelihood for our general model. We propose new datasets with ground truth selection which allow for the evaluation of the features importance map. Using these datasets, we show experimentally that using multiple imputation provides more reasonable interpretations.

The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/.

基于能量的模型（EBMS）为密度估计提供了优雅的框架，但它们难以训练。最近的工作已经建立了与生成的对抗网络的联系，eBM通过具有变分值函数的最小游戏培训。我们提出了EBM Log-似然的双向界限，使得我们最大限度地提高了较低的界限，并在解决Minimax游戏时最小化上限。我们将一个绑定到梯度惩罚的一个稳定，稳定培训，从而提供最佳工程实践的基础。为了评估界限，我们开发了EBM发生器的Jacobi确定的新的高效估算器。我们证明这些发展显着稳定培训并产生高质量密度估计和样品生成。