From smoothly pursuing moving objects to rapidly shifting gazes during visual search, humans employ a wide variety of eye movement strategies in different contexts. While eye movements provide a rich window into mental processes, building generative models of eye movements is notoriously difficult, and to date the computational objectives guiding eye movements remain largely a mystery. In this work, we tackled these problems in the context of a canonical spatial planning task, maze-solving. We collected eye movement data from human subjects and built deep generative models of eye movements using a novel differentiable architecture for gaze fixations and gaze shifts. We found that human eye movements are best predicted by a model that is optimized not to perform the task as efficiently as possible but instead to run an internal simulation of an object traversing the maze. This not only provides a generative model of eye movements in this task but also suggests a computational theory for how humans solve the task, namely that humans use mental simulation.

K均值是实践中使用最广泛的聚类模型之一。由于数据隔离的问题和对高模型性能的要求，如何共同建立实用和安全的K均值为多方成为行业中许多应用程序的重要主题。现有的工作主要是两种类型。第一种类型具有效率优势，但是信息泄漏会增加潜在的隐私风险。第二种类型是可证明的，但对于大规模数据稀疏方案而言，效率低下，甚至无助。在本文中，我们提出了一个新的框架，用于具有三个特征的有效稀疏感k均值。首先，我们的框架分为独立于数据的离线阶段和更快的在线阶段，并且离线阶段允许预先计算几乎所有的加密操作。其次，我们利用在线和离线阶段中的矢量化技术。第三，我们采用稀疏的矩阵乘法，以进一步提高效率。我们对三个合成数据集进行了全面的实验，并将模型部署在现实世界中的欺诈检测任务中。我们的实验结果表明，与最先进的解决方案相比，我们的模型在运行时间和沟通规模方面都能达到竞争性能，尤其是在稀疏数据集上。