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RI: Small: Diverse Particles for Continuous Learning and Inference

RI：小：用于持续学习和推理的多样化粒子

基本信息

批准号：
1816365
负责人：
Erik Sudderth
金额：
$ 44.92万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816365&HistoricalAwards=false
关键词：
RI Small Diverse Particles Continuous

项目摘要

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.

科学和技术问题需要对许多空间和时间关系进行表征和推理。示例任务包括：从视频数据中，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格，确定可行和有效的运动计划；从输入的氨基酸序列，以及现代成像技术提供的低分辨率观察结果，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，当前处理这种连续数据的算法变得难以处理。提出的工作将通过创建一类新的算法来克服这个问题，这种算法被称为“基于粒子的信念传播”，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为研究生和本科生课程编写教育材料，以及为当地中学生和高中生提供社区服务。该项目的开源统计软件也使许多重要的现实世界应用成为可能，这些应用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解决技术问题，如自主机器人和车辆的开发，以及科学问题，如研究蛋白质不正确折叠引起的疾病。该技术研究极大地推进了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一系列不同粒子信念传播推理算法，这些算法用可证明的精确离散优化取代经典粒子滤波器的（不稳定）随机重采样。第一个研究目标是探索一些计算算法的改进，以及支持理论，使有效的基于粒子的推理成为可能。由于后验不确定性的精确量化很重要，该项目推广了现有的“最大积”信念传播优化工作，以支持“和积”信念传播集成，并为更一般的推理查询提供“混合积”更新。该项目还推广了先前在离散图形模型上的工作，以研究局部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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。