Deep learning has resulted in breakthroughs in dealing with big data, in speech recognition, computer vision, natural language processing, and many other domains. It is based on deep neural networks with structures designed for various purposes. A mathematical foundation is needed to help understand modelling, and the approximation or generalization capability of deep learning models with network architectures and structures. In this talk, Professor Zhou will consider deep convolutional neural networks (CNNs) that are induced by convolutions. The convolutional architecture identifies essential differences between deep CNNs and classic neural networks. Professor Zhou will describe a mathematical theory for deep CNNs associated with rectified linear unit activation. In particular, Professor Zhou will discuss approximation and learning capability of deep CNNs dealing with functions of many variables.
The IFAC workshop series on “Control Systems and Data Science Toward Industry 4.0”, co-organized by School of Data Science, Hong Kong Institute for Data Science and the International Federation of Automatic Control (IFAC) has come to the 3rd Module. The Module 3 of IFAC Workshop Series focus on Electrical and Computer Engineering Systems and the module is to be given by world-renowned scholars and industrial leaders from different continents. The module will be held on 9 July, 2021 (Friday) at 07:45pm. It is free for registration.
10 July 2021 (Saturday) | 22:00 - 00:35 (HKT)
This workshop reports the latest interdisciplinary research on developing novel data science and artificial intelligence methodologies to capitalize on the rich “big data” of human mobility, contact tracing, imaging, virology, bioinformatics, clinical, etc. to confront infectious diseases, with a particular focus on combating the ongoing COVID-19 pandemic.
The Workshop aims to provide a platform for innovative creation, development, and dissemination of research ideas and results on high-speed rail and metro systems with local, regional, and international professionals from government, academia and private sector.