Workshop on Mathematical Machine Learning and Application

该奖项支持参加2020年4月26日至29日在宾夕法尼亚州立大学举行的2020年数学机器学习与应用研讨会。研讨会旨在汇集数据科学新兴领域的活跃科学家，讨论算法开发、理论分析和机器学习应用研究的最新进展。研讨会的一个重点是从数学的角度对深度学习的原因和方式进行理论理解。这项资助为美国的受邀演讲者和美国的初级参与者（缺乏自己资助的研究生、博士后和早期职业研究人员）的参与提供支持。工作坊的主要会议将于四月二十七日至二十九日举行，包括约二十场特邀讲座及海报展映。在4月26日（周日）的研讨会之前，将举办一个短期课程，介绍深度学习的数学，主要目标受众是青少年参与者。在本次研讨会上，来自美国和世界其他国家的数学机器学习及相关领域的研究人员将讨论最新的方法和发展，并提出数学数据科学及其应用的未来方向。研讨会将讨论的主题包括：物理建模和计算工程中的机器学习、机器学习中的非凸优化、深度神经网络的近似理论、深度学习和偏微分方程之间的相互作用、卷积神经网络的架构设计和解释、计算机视觉和自然语言处理中的深度学习、语法、自动机和规则的深度学习。本次研讨会的更多细节可在https://ccma.math.psu.edu/2020workshop/.This上获得。该奖项反映了美国国家科学基金会的法定使命，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。

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.