CIF: Small: Message Passing Networks

CIF：小型：消息传递网络

• 批准号: 1217043
• 负责人: Devavrat Shah
• 金额: $ 11万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217043&HistoricalAwards=false
• 关键词: CIF; Small; Message; Passing; Networks

The goal of this project is to develop foundations for message-passing algorithms, in the realm of probabilistic graphical models. The project will focus on three classes of problems in graphical models: (a) learning graphical models from observed data, (b) computing the mode of a graphical model, and (c) computing marginals of variables in a graphical model. The project will identify the strengths and limitations of the popular belief propagation algorithm for all three problems. In addition, the project will develop new class of efficient message-passing algorithms with provable performance guarantees by means of exploiting the geometry of the graphical model.Probabilistic graphical models have become a standard way to represent uncertainty succinctly in a wide variety of applications: communication and signal processing, computation, vision and image processing, bioinformatics, natural language processing, and more. The problems faced in these applications are largely The research outcome of this project will be folded in the course titled "Algorithms for Inference (6.438)" taught by the PI. The course is a popular entry level graduate course which also disseminates material online through MIT Open CourseWare and going forward, it may explore possibility of wider dissemination through MITx/EdX.

该项目的目标是在概率图形模型领域为消息传递算法开发基础。该项目将重点关注图模型中的三类问题：（a）从观察到的数据学习图模型，（b）计算图模型的模式，以及（c）计算图模型中变量的边际。该项目将确定流行的置信传播算法针对所有三个问题的优点和局限性。此外，该项目将通过利用图形模型的几何形状，开发具有可证明性能保证的新型高效消息传递算法。概率图形模型已成为在各种应用中简洁表示不确定性的标准方法：通信以及信号处理、计算、视觉和图像处理、生物信息学、自然语言处理等。这些应用中面临的问题很大程度上是该项目的研究成果将被纳入PI教授的名为“推理算法（6.438）”的课程中。该课程是一门受欢迎的入门级研究生课程，它还通过 MIT 开放课件在线传播材料，并且未来可能会探索通过 MITx/EdX 更广泛传播的可能性。