RI: Small: Diverse Particles for Continuous Learning and Inference

科学和技术问题需要对许多空间和时间关系进行表示和推理。 示例任务包括：从视频数据，跟踪人类执行各种活动的完整关节姿势;从机器人操作目标的规范，确定可行和有效的运动计划;从氨基酸的输入序列，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的3D结构。然而，随着问题的规模和复杂性的增长，目前处理这种连续数据的算法变得棘手。 拟议的工作将克服这个问题，通过创建一类新的算法，称为“基于粒子的信念传播”，有效地处理连续的关系数据。 该项目的更广泛影响包括针对研究生和本科课程创建教育材料，以及向当地初中和高中学生进行社区宣传。 该项目的开源统计软件还支持许多重要的现实应用程序，这些应用程序通过不同的本科生和研究生团队进行探索。 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，以及研究蛋白质错误折叠引起的疾病等科学问题。这项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。 这项工作将大大推广最近开发的家庭的不同粒子的信念传播推理算法，取代经典粒子滤波器的（不稳定）随机rescue与可证明准确的离散优化。 第一个研究目标是探索一些改进的计算算法，并支持理论，使有效的基于粒子的推理。 出于对后验不确定性进行精确量化的问题的考虑，该项目概括了现有的“最大乘积”置信传播优化工作，以支持“和-积”置信传播集成，以及用于更一般推理查询的“混合乘积”更新。 该项目还概括了先前在离散图形模型上的工作，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为连续估计问题提供新型的损失敏感结构化学习，包括从部分标记的训练数据中进行半监督学习。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.