Collaborative Research: ATD: Robust, Accurate and Efficient Graph-Structured RNN for Spatio-Temporal Forecasting and Anomaly Detection

合作研究：ATD：用于时空预测和异常检测的鲁棒、准确和高效的图结构 RNN

• 批准号: 2110145
• 负责人: Bao Wang
• 金额: $ 12万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110145&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; Robust; Accurate

The project aims to develop robust, efficient, and transferrable deep learning algorithms for prediction and anomaly detection in human spatio-temporal dynamics. This will be a fundamental step in providing reliable and speedy decision support for mitigating infectious diseases and countering threats in a time varying and spatially complex environment. The project shall advance recent computational tools (deep neural networks) in adversarial conditions and on resource limited (low cost, low energy) platform, thereby contribute to information technology in adversarial learning, mobile computing and effective decision making. A broad range of applications include threat detection and prediction for traffic and public transportation networks, security and privacy critical data analysis and prediction, threat detection and error correction for hydraulic, electrical and nuclear power systems. The approaches to be used involve novel techniques in high dimensional non-smooth non-convex optimization and graph representation. Specifically, the project shall study (1) multi-scale graph-structured recurrent neural networks for spatio-temporal data modeling, prediction and anomaly detection; (2) adversarially robust, accurate, and transferable deep learning algorithms based on advection-diffusion equations; (3) efficient quantization algorithms under adversarial conditions to reduce the latency of deep networks. The projects shall train a diverse body of graduate and undergraduate students at the Irvine and Los Angeles campuses of University of California through collaborative education and research activities in applied mathematics, computer science, data science and social science.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.

该项目旨在开发人类时空动力学中的预测和异常检测的强大，高效和可转让的深度学习算法。这将是为减轻传染病和在空间复杂的时间内提供可靠和快速的决策支持的基本步骤。 该项目应在对抗条件和资源有限（低成本，低能）平台上推进最近的计算工具（深神经网络），从而有助于对抗性学习，移动计算和有效决策的信息技术。广泛的应用程序包括对交通和公共交通网络的威胁检测和预测，安全与隐私批判数据分析和预测，液压，电气和核电系统的威胁检测和误差校正。 所使用的方法涉及高维非平滑非凸优化和图表表示的新技术。具体而言，该项目应研究（1）多尺度的图形结构复发性神经网络，以进行时空数据建模，预测和异常检测； （2）基于对流扩散方程的对抗性，准确和可转移的深度学习算法； （3）在对抗条件下有效的量化算法，以减少深网的潜伏期。这些项目应通过在应用数学，计算机科学，数据科学和社会科学领域的合作教育和研究活动中培训加利福尼亚大学欧文和洛杉矶校园的各种各样的研究生和本科生。该奖项反映了NSF的法定使命，并认为通过基金会的知识优点和广泛的crietia crietia criperia criperia criperia criperia criperia criperia cribitia cribitia criperia rection the Appliation奖。

项目成果

Implicit Graph Neural Networks: A Monotone Operator Viewpoint

• 发表时间: 2023
• 作者: Justin Baker;Qingsong Wang;C. Hauck;Bao Wang

A deterministic gradient-based approach to avoid saddle points

一种避免鞍点的基于确定性梯度的方法

• DOI: 10.1017/s0956792522000316
• 发表时间: 2022
• 期刊: European Journal of Applied Mathematics
• 影响因子: 1.9
• 作者: Kreusser, L. M.;Osher, S. J.;Wang, B.
• 通讯作者: Wang, B.

Efficient and Reliable Overlay Networks for Decentralized Federated Learning

• DOI: 10.1137/21m1465081
• 发表时间: 2021-12
• 期刊: SIAM J. Appl. Math.
• 作者: Yifan Hua;Kevin Miller;A. Bertozzi;Chen Qian;Bao Wang

How does momentum benefit deep neural networks architecture design? A few case studies

• DOI: 10.1007/s40687-022-00352-0
• 发表时间: 2021-10
• 期刊: Research in the Mathematical Sciences
• 影响因子: 1.2
• 作者: Bao Wang;Hedi Xia;T. Nguyen;S. Osher