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

该项目由NSF、爱沙尼亚研究理事会（ETAG）、美国国家科学院和海军全球研究办公室（DoD）共同支持。多边伙伴关系团队（美国罗切斯特理工学院、乌克兰国立技术大学“Igor Sikorsky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。预计所获得的研究结果将有助于现代工程半自主和自主系统的分析和设计以及控制和机器智能的基础。该项目针对一系列教育和学习活动，促进：（1）多学科教师、研究人员和学生的经验、奖学金和知识创造;（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样化教育，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，并推进早期载体教师，包括代表性不足的群体;（5）通过整合研究和教育的最先进的生态系统;（6）在国家经济和安全的关键领域拥有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的变革性研究，解决自适应机器学习中的开放问题，计算机视觉、目标检测和分类。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、分类准确性、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实现，并由紧凑的多维算子空间的微积分来支持。所提出的概念应保证内容感知的信息密集数据分析，降维和参数，鲁棒的图像重建，以及信息感知。计算效率高的机器学习模型将在标准和自定义数据集上进行训练。该奖项反映了NSF的法定使命，并被认为是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的支持。

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.