神经网络的可解释性及其潜在的理论行为仍然是一个开放的学习领域，即使在实际应用的巨大成功之后，特别是在深度学习的出现。在这项工作中，提出了NN2Poly：一种理论方法，允许获得提供已经训练的深神经网络的替代表示的多项式。这扩展了ARXIV中提出的先前想法：2102.03865，其仅限于单个隐藏层神经网络，以便在回归和分类任务中使用任意深度前馈神经网络。本文的目的是通过在每层的激活函数上使用泰勒膨胀来实现，然后使用若干组合性质，允许识别所需多项式的系数。讨论了实现本理论方法时的主要计算限制，并介绍了NN2POLY工作所必需的神经网络权重的约束的示例。最后，呈现了一些模拟，得出结论，使用NN2Poly可以获得给定神经网络的表示，并且在所获得的预测之间具有低误差。

Data deprivation, or the lack of easily available and actionable information on the well-being of individuals, is a significant challenge for the developing world and an impediment to the design and operationalization of policies intended to alleviate poverty. In this paper we explore the suitability of data derived from OpenStreetMap to proxy for the location of two crucial public services: schools and health clinics. Thanks to the efforts of thousands of digital humanitarians, online mapping repositories such as OpenStreetMap contain millions of records on buildings and other structures, delineating both their location and often their use. Unfortunately much of this data is locked in complex, unstructured text rendering it seemingly unsuitable for classifying schools or clinics. We apply a scalable, unsupervised learning method to unlabeled OpenStreetMap building data to extract the location of schools and health clinics in ten countries in Africa. We find the topic modeling approach greatly improves performance versus reliance on structured keys alone. We validate our results by comparing schools and clinics identified by our OSM method versus those identified by the WHO, and describe OSM coverage gaps more broadly.

Although machine learning (ML) models of AI achieve high performances in medicine, they are not free of errors. Empowering clinicians to identify incorrect model recommendations is crucial for engendering trust in medical AI. Explainable AI (XAI) aims to address this requirement by clarifying AI reasoning to support the end users. Several studies on biomedical imaging achieved promising results recently. Nevertheless, solutions for models using tabular data are not sufficient to meet the requirements of clinicians yet. This paper proposes a methodology to support clinicians in identifying failures of ML models trained with tabular data. We built our methodology on three main pillars: decomposing the feature set by leveraging clinical context latent space, assessing the clinical association of global explanations, and Latent Space Similarity (LSS) based local explanations. We demonstrated our methodology on ML-based recognition of preterm infant morbidities caused by infection. The risk of mortality, lifelong disability, and antibiotic resistance due to model failures was an open research question in this domain. We achieved to identify misclassification cases of two models with our approach. By contextualizing local explanations, our solution provides clinicians with actionable insights to support their autonomy for informed final decisions.

已知神经网络对初始化敏感。依赖神经网络的解释方法并不强大，因为当模型被初始化并用不同的随机种子训练时，它们的解释可能会有所不同。在许多安全关键应用（例如医疗保健中的疾病诊断）中，对模型初始化的敏感性是不可取的，其中解释性可能会对有助于决策产生重大影响。在这项工作中，我们引入了一种基于参数平均的新方法，以在表格数据设置（称为XTAB）中进行可靠的解释性。我们首先初始化并训练具有不同随机种子的浅网络（称为本地面具）的多个实例，以进行下游任务。然后，我们通过“平均”本地掩码的参数来获得全局掩码模型，并表明全局模型使用多数规则根据所有本地模型中的相对重要性来对特征进行排名。我们对各种真实和合成数据集进行了广泛的实验，表明所提出的方法可用于特征选择，并获得对亚最佳模型初始化不敏感的全局特征重要性。