Deep Probabilistic Models for Making Sense of Unstructured Data

未来的信息基础设施将以分布式数据源的大规模流集为特征。这些数据将从计算和理论角度挑战经典的统计和机器学习方法。该提案研究了在这些挑战的背景下学习和推理的灵活模型。我们将开发强大，灵活和“隐私意识”的学习基础设施，以用户为中心。这些学习基础设施将根据具体的应用挑战，包括心理健康、发展中国家和个人信息管理，加以开发。这些应用程序的灵感来自与citizenme，心理健康技术研究的NewMind网络和乌干达坎帕拉的Makerere大学的合作。

The future information infrastructure will be characterized by massive streaming sets of distributed data-sources. These data will challenge classical statistical and machine learning methodologies both from a computational and a theoretical perspective. This proposal investigates a flexible class of models for learning and inference in the context of these challenges. We will develop learning infrastructures that are powerful, flexible and 'privacyaware' with a user-centric focus. These learning infrastructures will be developed in the context of particular application challenges, including mental health, the developing world and personal information management. These applications are inspired by collaborations with citizenme, the NewMind Network for Mental Health Technology Research and Makerere University in Kampala, Uganda.