Workshop on Mathematical Machine Learning and Application

数学机器学习与应用研讨会

• 批准号: 2020623
• 负责人: Jinchao Xu
• 金额: $ 2.4万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020623&HistoricalAwards=false
• 关键词: Workshop; Mathematical; Machine; Learning; Application

This award supports participation in the 2020 Workshop on Mathematical Machine Learning and Application held at Penn State University on April 26-29, 2020. The workshop aims to bring together active scientists in the emerging field of data science to discuss recent advances in the study of algorithm development, theoretical analysis, and applications of machine learning. One focus of the workshop is on theoretical understanding of why and how deep learning works from mathematical viewpoints. This grant provides supports of participation of US-based invited speakers and US-based junior participants (graduate students, postdocs and early career researchers who lack their own funding). The main session of the workshop will take place during the period from April 27 to 29, with about 20 invited talks and a poster session. A short course featuring introductory lectures on the mathematics of deep learning will be held prior to the workshop on Sunday, April 26, with junior participants as the main target audience.In this workshop, researchers in mathematical machine learning and related fields from the United States and other countries around the world will discuss state-of-the-art methodologies and developments and propose future directions in mathematical data science and its applications. Examples of topics to be discussed in the workshop include: machine learning in physical modeling and computational engineering, non-convex optimization in machine learning, approximation theory of deep neural networks, interactions between deep learning and partial differential equations, architecture design and interpretation of convolutional neural networks, deep learning in computer vision and natural language processing, and deep learning with grammars, automata, and rules. More details of this workshop are available at https://ccma.math.psu.edu/2020workshop/.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.

该奖项支持参加2020年在宾夕法尼亚州立大学（Penn State University）于2020年4月26日在宾夕法尼亚州立大学举行的数学机器学习和应用研讨会。该研讨会旨在将活跃的数据科学领域的活跃科学家汇集在一起​​，讨论算法开发，理论分析，理论分析和机器学习应用的最新进展。 研讨会的一个重点是从数学观点中对为什么和深度学习起作用的理论理解。这项赠款提供了支持美国受邀演讲者和美国的初级参与者的参与（研究生，博士后和早期职业研究人员，他们缺乏自己的资金）。研讨会的主要会议将在4月27日至29日的期间举行，大约有20次受邀的会谈和海报会议。在4月26日星期日举行的研讨会之前，将举行一门简短的课程，其中包含有关深度学习数学的介绍性讲座，而初级参与者是主要的目标受众。在这个研讨会上，数学机器学习及其相关领域的研究人员来自美国和世界各地的其他国家和其他国家 /地区，将讨论现实的方法论，并讨论了未来的未来数据和未来的数据，并提出了数学数据的应用程序，并将其及其应用程序及其应用。研讨会中要讨论的主题的示例包括：物理建模和计算工程学中的机器学习，机器学习中的非凸优化，深度神经网络的近似理论，深度学习与部分微分方程之间的相互作用，建筑设计和卷积神经网络的解释，计算机视觉和自然语言处理中的深度学习，以及对语言处理中的深度学习，以及对语法和自然学习，以及自动学习，以及自动，自动，和自动和自动，和自动和自动化。该研讨会的更多详细信息可在https://ccma.math.psu.edu/2020workshop/.This奖中获得，反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。