Phase I IUCRC University of Oregon: Center for Big Learning

第一阶段 IUCCRC 俄勒冈大学：大学习中心

• 批准号: 1747798
• 负责人: Thien Nguyen
• 金额: $ 75万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1747798&HistoricalAwards=false
• 关键词: Phase; IUCRC; University; Oregon; Center

Recent advances in machine learning are ushering in a new era in intelligent computing. The NSF Industry/University Collaborative Research Center for Big Learning (CBL) at the University of Oregon (UO) aims to design novel algorithms and develop efficient systems for deep learning applications in the era of big data. CBL will catalyze diverse expertise of faculty members, students, industry partners, and federal agencies to create state-of-the-art deep learning methodologies, technologies, and applications across broad domains of business, healthcare, and Internet-of-Things. CBL will provide training for new scientists and graduate students, as well as a rich environment for cross-disciplinary engagement.The mission of CBL is to perform pioneering research and development in powerful deep learning algorithms, efficient intelligent systems, and novel applications through unified and coordinated efforts in the CBL consortium. The CBL research team at the UO includes experts in data science, artificial intelligence, machine learning, high-performance computing, health informatics, and bioinformatics. They will explore research projects related to medical record analysis, health behavior prediction, natural language processing, performance optimization, financial forecasting, and computer vision by deploying various deep learning models. CBL is expected to have broad transformative impacts in computational technologies, education, and society. Through research and applications to address a broad spectrum of real-world challenges, CBL will make significant contributions and impacts to the deep learning community. The discoveries from CBL will amplify opportunities for new products and services of industry in general, while delivering key techniques to CBL industry partners in particular. As the nexus of deep learning research and applications, CBL offers an ideal platform to nurture next-generation talents through world-class mentors from both academia and industry, disseminate the cutting-edge technologies, and facilitate industry/university collaborative research.The center's repository is hosted at http://nsfcbl.org. The data, code, and documents will be organized and maintained on the CBL server for the duration of the center plus five years. The internal code repository will be hosted at GitLab. After the software packages are well documented and tested, they will be released and hosted at popular public servers, such as GitHub and Bitbucket.

机器学习的最新进展正在引领智能计算的新时代。 位于俄勒冈州大学（UO）的NSF大学习产业/大学合作研究中心（CBL）旨在为大数据时代的深度学习应用设计新颖的算法并开发高效的系统。 CBL将催化教师，学生，行业合作伙伴和联邦机构的各种专业知识，以创建最先进的深度学习方法，技术和应用程序，跨越商业，医疗保健和物联网的广泛领域。 CBL将为新科学家和研究生提供培训，并为跨学科参与提供丰富的环境。CBL的使命是通过CBL联盟的统一和协调努力，在强大的深度学习算法，高效的智能系统和新颖的应用方面进行开创性的研究和开发。UO的CBL研究团队包括数据科学，人工智能，机器学习，高性能计算，健康信息学和生物信息学方面的专家。他们将通过部署各种深度学习模型来探索与医疗记录分析、健康行为预测、自然语言处理、性能优化、财务预测和计算机视觉相关的研究项目。CBL预计将在计算技术，教育和社会方面产生广泛的变革性影响。通过研究和应用来解决广泛的现实挑战，CBL将为深度学习社区做出重大贡献和影响。CBL的发现将扩大整个行业新产品和服务的机会，同时特别为CBL行业合作伙伴提供关键技术。作为深度学习研究和应用的纽带，CBL为培养下一代人才提供了一个理想的平台，通过来自学术界和工业界的世界级导师，传播前沿技术，促进产学合作研究。该中心的存储库托管在http://nsfcbl.org上，数据，代码和文档将在CBL服务器上组织和维护，为期五年。 内部代码库将托管在GitLab。 在软件包经过良好的文档记录和测试后，它们将被发布并托管在流行的公共服务器上，如GitHub和Bitbucket。

项目成果

Boosting Punctuation Restoration with Data Generation and Reinforcement Learning

通过数据生成和强化学习促进标点符号恢复

• 发表时间: 2023
• 期刊: Proceedings of INTERSPEECH
• 作者: Lai, Viet;Salinas, Abel;Tan, Hao;Bui, Trung;Tran, Quan;Yoon, Seunghyun;Deilamsalehy, Hanieh;Dernoncourt, Franck;Nguyen, Thien
• 通讯作者: Nguyen, Thien

Word-Label Alignment for Event Detection: A New Perspective via Optimal Transport

• DOI: 10.18653/v1/2022.starsem-1.11
• 发表时间: 2022
• 作者: Amir Pouran Ben Veyseh;Thien Huu Nguyen

ClinicalT5: A Generative Language Model for Clinical Text

• DOI: 10.18653/v1/2022.findings-emnlp.398
• 发表时间: 2022
• 作者: Qiuhao Lu;D. Dou;Thien Huu Nguyen

Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies

• DOI: 10.18653/v1/2022.naacl-main.324
• 发表时间: 2022
• 作者: Minh Le Nguyen;Bonan Min;Franck Dernoncourt;Thien Huu Nguyen

Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method

• DOI: 10.18653/v1/2022.findings-emnlp.407
• 发表时间: 2022
• 作者: Viet Dac Lai;Hieu Man;L. Van;Franck Dernoncourt;Thien Huu Nguyen