Statistical Methodology towards Technology

该提案提出了将统计方法用于人工智能的两个主要长期研究方向。这两个方向有可能恢复统计模型作为复杂任务（如目标检测、图像分类和机器翻译）的主要工具。*** ***方向1：卷积概率模型。这个方向允许提供与产品划分模型相结合的统计混合模型的更深入和更丰富的版本。这个方向允许开发具有更多优势的深度神经网络的有统计意义的替代方案。由于混合模型和产品划分模型理论的完善，这些优势是显著的。******方向二：深度神经网络的边缘计算。这个方向允许将智能技术引入手机和可穿戴设备等边缘设备。目前，大多数智能计算依赖于云服务，消耗数据带宽。本研究提案允许探索使用完善的惩罚和正则化方法的途径，以在边缘设备上进行计算。*** **

This proposal puts in place two main long-term research directions to use statistical methodologies towards artificial intelligence. These two directions have the potential of reviving statistical models as the main tool for complicated tasks such as object detection, image classification, and machine translation.*** ***Direction 1: convolutional probabilistic models. This direction allows to provide a deep and richer version of statistical mixture models combined with product partition models. This direction allows to develop a statistically meaningful alternative to deep neural networks with more advantages. These advantages are significant thank to the well-established theory of mixture models and product partition models.******Direction 2: edge computation of deep neural networks. This direction allows to bring smart technology to edge devices such as cell phone and wearables. Currently most of smart computations rely on cloud services and eats data bandwidth. This research proposal allows to explore avenues for using well-established penalization and regularization methodologies, to bring computations on edge devices.*** **