Zhi-Hua Zhou

Nanjing University, China    

Title:  An Exploration to Non-NN Style Deep Learning


Deep learning is a hot topic during the past few years. Generally, the word "deep learning" is regarded as a synonym of "deep neural networks (DNNs)". In this talk, we will discuss on essentials in deep learning and claim that it is not necessarily to be realized by neural networks. We will then present an exploration to non-NN style deep learning, where the building blocks are non-differentiable modules and the training process does not rely on backpropagation.


Zhi-Hua Zhou is a Professor of Nanjing University, China. He is the Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, and Founding Director of the LAMDA Group. His main research interests are in artificial intelligence, machine learning and data mining. He authored the books "Ensemble Methods: Foundations and Algorithms (2012)" and "Machine Learning (in Chinese, 2016)", and published more than 150 papers in top-tier international journals/conferences. According to Google Scholar, his publications have received more than 30,000 citations, with an H-index of 85. He also holds 22 patents and has rich experiences in industrial applications. He has received various awards, including the National Natural Science Award of China, PAKDD Distinguished Contribution Award, IEEE ICDM Outstanding Service Award, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, and Action/Associate Editor of Machine Learning, IEEE PAMI, ACM TKDD, etc. He was Associate Editor of ACM TIST, IEEE TKDE, IEEE TNNLS, IEEE TCDS, etc. He founded ACML (Asian Conference on Machine Learning) and served as General Chair of IEEE ICDM 2016, Program Chair of IJCAI 2015 Machine Learning track, etc. He will serve as Program Chair of AAAI 2019 and IJCAI 2019. He is the Chair of CCF-AI, and was Chair of the IEEE CIS Data Mining Technical Committee. He is a foreign member of the Academy of Europe, and a Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, CCF and CAAI.

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