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CRII: RI: TRUST—TRustworthy Uncertainty Propagation for Sequential Time-Series Analysis

CRII：RI：TRUST – 用于顺序时间序列分析的值得信赖的不确定性传播

基本信息

批准号：
2401828
负责人：
Dimah Dera
金额：
$ 17.49万
依托单位：
Rochester Institute of Tech
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-10-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2401828&HistoricalAwards=false
关键词：
CRII RI TRUST TRustworthy Uncertainty

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The massive production of time-series data through the internet of things, the digitalization of healthcare, and the rise of smart cities have surged the need for competent time-series modeling, analysis, and forecasting. Many critical applications rely on time-series analysis, including video analytics, stock market analysis, earthquake prediction, economic forecasting, healthcare monitoring and disease prognoses. Recent sequence machine learning (ML) models have achieved remarkable success in processing entire sequences of data (such as speech or video) and learning long-term dependencies of time-series. However, these sequence models are unable to understand or assess their uncertainty, particularly critical when predicting in heterogeneous and noisy environments or in the presence of an adversary. For example, missing a prediction of heart failure due to artifacts in the electrocardiogram (ECG) physiological signal or failing to detect a security vulnerability in a software product could cause tragic health, financial and societal damage to people and industries. This project will develop significant theoretical and algorithmic foundations for uncertainty quantification, self-assessment, and adversarial detection in modern ML sequence models towards safe and reliable time-series intelligent machines. The project will develop pioneering algorithms that are universally robust under noisy conditions and adversarial susceptions. The main focus is on sequential ML algorithms with quantified uncertainty for the provided solutions. Open-source implementations of the proposed algorithms will be publicly available for rapid dissemination and contribution to the ML community. Furthermore, the proposed research will support the cross-disciplinary development of a diverse cohort of graduate and undergraduate students at the University of Texas Rio Grande Valley and develop new courses, certificates, and research projects in trustworthy and robust ML.The primary technical aim of the project advocates a novel Bayesian estimation framework that propagates distributions across models’ non-linear layers inspired by powerful statistical frameworks for optimal tracking in non-linear and non-Gaussian systems. A comprehensive analysis of models’ performance and uncertainty measures under noisy conditions and adversarial attacks will pave the way for deploying sequential ML algorithms in a wide range of real-world applications. Applications of this research include industry and healthcare partners in the areas of security of industrial systems (in collaboration with Lockheed Martin Inc.) and brain tumor detection and surveillance from magnetic resonance imaging (in collaboration with MRIMath, LLC).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.

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。通过物联网大量生产时间序列数据，医疗保健数字化以及智慧城市的兴起，对胜任的时间序列建模，分析和预测的需求激增。许多关键应用依赖于时间序列分析，包括视频分析、股市分析、地震预测、经济预测、医疗监测和疾病诊断。最近的序列机器学习（ML）模型在处理整个数据序列（如语音或视频）和学习时间序列的长期依赖性方面取得了显着的成功。然而，这些序列模型无法理解或评估它们的不确定性，在异构和嘈杂的环境中或在存在对手的情况下进行预测时尤其关键。例如，由于心电图（ECG）生理信号中的伪影而错过心力衰竭的预测或未能检测到软件产品中的安全漏洞可能会对人们和行业造成悲惨的健康，财务和社会损害。该项目将为现代ML序列模型中的不确定性量化、自我评估和对抗性检测开发重要的理论和算法基础，以实现安全可靠的时间序列智能机器。该项目将开发开创性的算法，这些算法在嘈杂的条件下和对抗性的对抗中普遍鲁棒。主要重点是顺序ML算法与量化的不确定性提供的解决方案。所提出的算法的开源实现将公开提供给ML社区，以供快速传播和贡献。此外，拟议的研究将支持德克萨斯大学格兰德河谷分校研究生和本科生的跨学科发展，并开发新的课程、证书、该项目的主要技术目标是倡导一种新颖的贝叶斯估计框架，该框架可以在模型的非线性模型中传播分布，线性层的灵感来自于强大的统计框架，用于非线性和非高斯系统中的最佳跟踪。对模型在噪声条件和对抗性攻击下的性能和不确定性度量进行全面分析，将为在广泛的现实世界应用中部署序列ML算法铺平道路。这项研究的应用包括工业系统安全领域的工业和医疗保健合作伙伴（与洛克希德·马丁公司合作）。和脑肿瘤的检测和监测磁共振成像（与MRIMath，LLC合作）。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Robust Active Simultaneous Localization and Mapping Based on Bayesian Actor-Critic Reinforcement Learning

基于贝叶斯演员-批评家强化学习的鲁棒主动同步定位与建图

DOI：
10.1109/cai54212.2023.00035
发表时间：
2023
期刊：
IEEE Conference on Artificial Intelligence (CAI
影响因子：
0
作者：
Pedraza, Bryan;Dera, Dimah
通讯作者：
Dera, Dimah

Self-Assessment and Robust Anomaly Detection with Bayesian Deep Learning

使用贝叶斯深度学习进行自我评估和鲁棒异常检测

DOI：
10.23919/fusion49751.2022.9841358
发表时间：
2022
期刊：
IEEE 25th International Conference on Information Fusion (FUSION
影响因子：
0
作者：
Carannante, Giuseppina;Dera, Dimah;Aminul, Orune;Bouaynaya, Nidhal C.;Rasool, Ghulam
通讯作者：
Rasool, Ghulam

TRustworthy Uncertainty Propagation for Sequential Time-Series Analysis in RNNs