Statistical Methodology towards Technology

该提案提出了两个主要的长期研究方向，将统计方法用于人工智能。这两个方向有可能复兴统计模型，使其成为目标检测、图像分类和机器翻译等复杂任务的主要工具。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深度和更丰富的版本。这个方向允许开发一种具有统计意义的替代深度神经网络的方法，具有更多优势。这些优点是显着的，由于完善的混合物模型和产品分配模型的理论。方向2：深度神经网络的边缘计算。这个方向允许将智能技术带到手机和可穿戴设备等边缘设备。目前，大多数智能计算都依赖于云服务，并消耗数据带宽。这项研究提案允许探索使用完善的惩罚和正则化方法的途径，将计算带到边缘设备上。

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.