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年数学机器学习和应用研讨会。研讨会旨在将新兴数据科学领域的活跃科学家聚集在一起，讨论机器学习在算法开发、理论分析和应用方面的最新进展。研讨会的重点之一是从数学的角度从理论上理解深度学习为什么以及如何起作用。这笔赠款为在美国的特邀演讲者和在美国的初级参与者(没有自己资金的研究生、博士后和早期职业研究人员)的参与提供支持。研讨会的主要环节将在4月27日至29日期间举行，约有20次受邀演讲和一次海报展示环节。在四月二十六日(星期日)工作坊举行前，将会举办一个介绍深度学习数学的短期课程，主要对像是青少年学员。在这个工作坊中，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发展方向。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，深度神经网络的逼近理论，深度学习和偏微分方程之间的相互作用，卷积神经网络的体系结构设计和解释，计算机视觉和自然语言处理的深度学习，以及语法、自动机和规则的深度学习。该研讨会的更多细节可在https://ccma.math.psu.edu/2020workshop/.This获得，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势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.