RI: Small: Diverse Particles for Continuous Learning and Inference

科学技术问题需要对许多空间和时间关系进行表示和推理。例如，任务包括：根据视频数据，跟踪人类进行各种活动的完整关节姿势；从机器人操纵目标的规范，确定可行和有效的运动计划；根据氨基酸的输入序列，以及现代成像技术提供的低分辨率观察，预测蛋白质最有可能折叠成的3D结构。然而，随着问题的规模和复杂性的增长，当前处理这类连续数据的算法变得难以处理。这项拟议的工作将通过创建一类新的算法来克服这个问题，该算法被称为“基于粒子的信任传播”，可以有效地处理连续的关系数据。该项目的更广泛影响包括创建针对研究生和本科课程的教育材料，以及面向当地初中生和高中生的社区外联。该项目的开源统计软件还支持许多重要的现实世界应用程序，这些应用程序通过由本科生和研究生组成的不同团队进行探索。这项工作可能有助于自主机器人和车辆的开发等技术问题，以及蛋白质错误折叠引起的疾病研究等科学问题。技术研究显著推进了用于非高斯、连续变量图形模型推理的消息传递算法的理论和实践。这项工作将在很大程度上推广最近开发的一系列不同的粒子信任传播推理算法，这些算法用可证明准确的离散优化取代经典粒子过滤器的(不稳定的)随机重采样。第一个研究目标是探索对计算算法和支持理论的一些改进，以实现有效的基于粒子的推理。受后验不确定性的精确量化问题的启发，该项目总结了“最大乘积”信任传播优化方面的现有工作，以支持“和积”信任传播集成，以及“混合乘积”更新以支持更一般的推理查询。该项目还概括了先前关于离散图形模型的工作，以研究本地消息中的错误如何在整个模型中传播。此外，研究人员还研究了基于粒子的推理如何能够实现针对连续估计问题的新型损失敏感结构化学习，包括从部分标记的训练数据进行的半监督学习。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Scientific and technological problems require representing and reasoning about many spatial and temporal relationships. Examples tasks include: from video data, tracking the full articulated pose of humans performing various activities; from specification of a robotic manipulation goal, identify feasible and efficient motion plans; from an input sequence of amino acids, and low-resolution observations provided by modern imaging technologies, predicting the 3D structures into which proteins are most likely to fold. However, as problems grow in size and complexity, current algorithms for handling this kind of continuous data become intractable. The proposed work will overcome this problem by creating a new class of algorithms, called "particle-based belief propagation", that efficiently handles continuous relational data. Broader impacts of the project include the creation of educational materials targeting graduate and undergraduate courses, as well as community outreach to local middle and high school students. The project's open source statistical software also enables a number of important real-world applications, which are explored via diverse teams of undergraduate and graduate students. This work may contribute to technological problems like the development of autonomous robots and vehicles, and scientific problems like the study of diseases caused by incorrect folding of proteins.The technical research significantly advances the theory and practice of message-passing algorithms for inference in graphical models with non-Gaussian, continuous variables. This work will substantially generalize a recently developed family of diverse particle belief propagation inference algorithms, that replace the (unstable) stochastic resampling of classic particle filters with provably accurate discrete optimization. The first research aim is to explore a number of improvements to the computational algorithms, and supporting theory, that enables effective particle-based inference. Motivated by problems where precise quantification of posterior uncertainty is important, the project generalizes existing work on "max-product" belief propagation optimization to support "sum-product" belief propagation integration, and "mixed-product" updates for more general inference queries. The project also generalizes prior work on discrete graphical models to study how errors made in local messages propagate throughout the model. Furthermore, the researchers study how particle-based inference can enable new types of loss-sensitive structured learning for continuous estimation problems, including semi-supervised learning from partially labeled training data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.