EAGER: IMPRESS-U: Exploratory Research in Robust Machine Learning for Object Detection and Classification

EAGER：IMPRESS-U：用于对象检测和分类的鲁棒机器学习的探索性研究

• 批准号: 2415299
• 负责人: Sergey Lyshevski
• 金额: $ 5万
• 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: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2415299&HistoricalAwards=false
• 关键词: EAGER; IMPRESS; Exploratory; Research; Robust

This project is jointly supported by NSF, Estonian Research Council (ETAG), US National Academy of Sciences, and Office of Naval Research Global (DoD). The multilateral partnership team (Rochester Institute of Technology, USA, the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Ukraine, and Tallinn Technical University, Estonia) will advance scientific knowledge in machine learning and computer vision. It is expected that the obtained findings will contribute to foundations in analysis and design of modern engineered semi-autonomous and autonomous systems, as well as control and machine intelligence. This project targets a range of educational and learning activities, fostering: (1) Multidisciplinary faculty, researchers and students experience, scholarship and knowledge generation; (2) Competitiveness and national security by transformative research and global diverse education in critical areas of recognized needs, opportunities and urgency; (3) Knowledge and research findings implementation, disseminations and institutionalization; (4) Building a diverse research team, and advancing early-carrier faculty, including underrepresented groups; (5) State-of-the-art ecosystem by integrating research and education; (6) A modern globally-competitive research workforce in critical areas of national economy and security.Multi-university research team will conduct exploratory transformative research, addressing open problems in adaptive machine learning, computer vision, object detection and classification. The researchers will investigate reduced-dimensionality convolutional neural networks to ensure high mean average precision, object detection probability, classification accuracy, robustness to nefarious data, and high speed. Adaptive machine learning will be empowered by applying singular value factorization analytics, supported by a calculus of compact multidimensional operator spaces. The proposed concept should guarantee content-aware information-dense data analytics, dimensionality and parameter reduction, robust image reconstruction, as well as information perception. Computationally efficient machine learning models will be trained on standard and custom datasets. Novel objective functions and algorithms will be investigated evaluating performance metrics and benchmarks.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.

该项目由美国国家科学基金会、爱沙尼亚研究委员会（ETAG）、美国国家科学院和全球海军研究办公室（DoD）共同支持。多边合作团队（美国罗彻斯特理工学院、乌克兰国立技术大学“伊戈尔·西科斯基基辅理工学院”和爱沙尼亚塔林技术大学）将推进机器学习和计算机视觉方面的科学知识。预计所获得的发现将有助于为现代工程半自主和自主系统以及控制和机器智能的分析和设计奠定基础。该项目针对一系列教育和学习活动，培养：（1）多学科教师、研究人员和学生的经验、学术和知识生成； (2) 通过在公认的需求、机会和紧迫性的关键领域进行变革性研究和全球多元化教育来提高竞争力和国家安全； (3) 知识和研究成果的实施、传播和制度化； (4) 建立多元化的研究团队，并提升早期载体师资力量，包括代表性不足的群体； (5) 融合研究和教育的最先进的生态系统； （6）在国民经济和安全关键领域建立一支具有全球竞争力的现代化研究队伍。多所大学的研究团队将进行探索性变革性研究，解决自适应机器学习、计算机视觉、物体检测和分类等领域的开放性问题。研究人员将研究降维卷积神经网络，以确保高平均精度、目标检测概率、分类精度、对恶意数据的鲁棒性和高速。自适应机器学习将通过应用奇异值分解分析来增强，并得到紧凑多维算子空间演算的支持。所提出的概念应保证内容感知的信息密集数据分析、维度和参数缩减、鲁棒的图像重建以及信息感知。计算高效的机器学习模型将在标准和自定义数据集上进行训练。将研究新颖的目标函数和算法，评估性能指标和基准。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。