MRI: Acquisition of Artificial Intelligence & Deep Learning (AIDL) Training and Research Laboratory

MRI：人工智能的获取

• 批准号: 1828181
• 负责人: Xingquan Zhu
• 金额: $ 65.29万
• 依托单位: Florida Atlantic University
• 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: 2018-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828181&HistoricalAwards=false
• 关键词: MRI; Acquisition; Artificial; Intelligence; &amp

Researchers in health, biomedical science, and various engineering fields often do not receive sufficient training in using the most powerful approach to machine learning known to date, an approach called "deep learning", for analying their data. Deep learning is based on simulated artificial neural networks, and for large real-world problems requires access to specialized computer hardward and software. Such hardware and software platforms, however, are rarely part of the information technology resources available for researchers outside of the field of computer science. This project will overcome this barrier by procurement and development of a deep learning platform at Florida Atlantic University for such research. The project provides a training hub for industry and university to work closely on advanced artificial intelligence applications, and in turn might benefit economic growth.This infrastructure project supports creation of a deep learning platform for health, biomedicine, ocean research, and related domains at Florida Atlantic University. The platform will be shared across campus to service multiple domains. The project brings about a centralized cross campus interdisciplinary platform and augmented deep learning and related artificial intelligence tools for interdisciplinary research. The former, jointly managed by the College of Engineering and Computer Science, and the FAU office of Information Technology, enables building upon the experience and frameworks of others that eventually results in shared infrastructure savings. The latter is likely to contribute in building/augmenting AI and DL tool kits for interdisciplinary research, including augmentation of existing common machine learning and deep learning algorithms for domain experts to carry out analysis on their data without requiring intensive programming skills. Augmented AI and DL tools should be particularly useful to ocean engineers and health sciences.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.

健康、生物医学科学和各种工程领域的研究人员通常没有接受足够的培训，无法使用迄今为止已知的最强大的机器学习方法，即所谓的“深度学习”方法来分析他们的数据。 深度学习基于模拟的人工神经网络，对于大型现实世界的问题，需要访问专门的计算机硬件和软件。 然而，这样的硬件和软件平台很少是计算机科学领域之外的研究人员可用的信息技术资源的一部分。 该项目将通过在佛罗里达大西洋大学采购和开发深度学习平台来克服这一障碍。 该项目为工业和大学提供了一个培训中心，以便在先进的人工智能应用方面密切合作，进而可能有利于经济增长。该基础设施项目支持在佛罗里达大西洋大学创建一个用于健康，生物医学，海洋研究和相关领域的深度学习平台。 该平台将在校园内共享，为多个域提供服务。该项目带来了一个集中的跨校园跨学科平台，以及用于跨学科研究的增强深度学习和相关人工智能工具。 前者由工程和计算机科学学院以及FAU信息技术办公室共同管理，能够借鉴其他人的经验和框架，最终实现共享基础设施的节省。后者可能有助于构建/增强用于跨学科研究的AI和DL工具包，包括增强现有的通用机器学习和深度学习算法，以便领域专家在不需要密集编程技能的情况下对其数据进行分析。增强型人工智能和深度学习工具对海洋工程师和健康科学特别有用。该奖项反映了NSF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。

项目成果

Learning Convolutional Neural Networks from Ordered Features of Generic Data

从通用数据的有序特征中学习卷积神经网络

• DOI: 10.1109/icmla.2018.00145
• 发表时间: 2018
• 期刊: The 17th IEEE International Conference on Machine Learning and Applications (ICMLA
• 作者: Golinko, Eric;Sonderman, Thomas;Zhu, Xingquan
• 通讯作者: Zhu, Xingquan

User Response Prediction in Online Advertising

• DOI: 10.1145/3446662
• 发表时间: 2022-04-01
• 期刊: ACM COMPUTING SURVEYS
• 影响因子: 16.6
• 作者: Gharibshah，Zhabiz;Zhu，Xingquan
• 通讯作者: Zhu，Xingquan

Feature-Attention Graph Convolutional Networks for Noise Resilient Learning

用于抗噪学习的特征注意图卷积网络

• DOI: 10.1109/tcyb.2022.3143798
• 发表时间: 2022
• 期刊: IEEE Transactions on Cybernetics
• 影响因子: 11.8
• 作者: Shi, Min;Tang, Yufei;Zhu, Xingquan;Zhuang, Yuan;Lin, Maohua;Liu, Jianxun
• 通讯作者: Liu, Jianxun

Weak Supervision Network Embedding for Constrained Graph Learning

• DOI: 10.1007/978-3-030-75762-5_39
• 发表时间: 2021
• 作者: Ting Guo;Xingquan Zhu;Yang Wang;Fang Chen

Learning Graph Neural Networks with Positive and Unlabeled Nodes

• DOI: 10.1145/3450316
• 发表时间: 2021-03
• 期刊: ACM Transactions on Knowledge Discovery from Data (TKDD)
• 作者: Man Wu;Shirui Pan;Lan Du;Xingquan Zhu