题目： CNN Architecture Design: from Deeper to Wider
Senior Researcher, Microsoft Research
时间： 2017.11.23（周四） 下午2: 30 – 3: 30
地点： 经管楼 312
In this talk, I will introduce our two works on deep convolutional neural network architecture design: from deeper to wider. The first work is about a novel building block, which assembles convolutional branches in parallel with merge and run mappings, which results in less deep but wider networks. In the second work along the path of going wider, I will introduce interleaved group convolutions. It is a drop-in replacement of regular convolutions and more efficient in using parameters and computation than regular convolutions.
Jingdong Wang is a Senior Researcher at the Visual Computing Group, Microsoft Research Asia. His areas of interest include computer vision, multimedia, and machine learning. At present, he is mainly working on deep learning, person re-identification, image recognition, and indexing and compact coding for large scale similarity search. He has published 100+ papers in top conferences and prestigious international journals and one book. His paper was selected into the best paper finalist at ACMMM 2015. He has shipped a dozen of technologies to Microsoft products, including Bing image search, Cognitive service, and XiaoIce （微软小冰） Chatbot. He has served as an associate editor for TMM, an area chair (or senior program committee member) in AAAI 2018, ICCV 2017, ICIP 2017, CVPR 2017, ECCV 2016, ACMMM 2015 and ICME 2015, a track chair in ICME 2012, a special session chair in ICMR 2014, and a program committee member or a reviewer for many international conferences and journals.