题  目：Principled Design of Convolutional Neural Networks

内容简介：The design of convolutional neural networks (CNNs) has undergone two phases: manual design at the early stage, which requires much engineering insights, and the automatic search at the current stage, which heavily relies on computing power. Whether there is an underlying theory for designing good CNNs becomes a crucial research problem. In this talk, I will illustrate our efforts on pursuing this goal. Although I haven’t found a unified principle that can result in all the effective CNNs, I do find multiple principles that can help design CNNs from various aspects.

报告人：北京大学  林宙辰  教授

报告人简介：博士生导师，IAPR/IEEE Fellow，国家杰青，中国图象图形学学会机器视觉专委会主任，中国自动化学会模式识别与机器智能专委会副主任。研究领域为机器学习、计算机视觉和数值优化。发表论文200余篇，英文专著2本。多次担任CVPR 、ICCV、NIPS/NeurIPS、ICML、IJCAI、AAAI和ICLR领域主席，曾任IEEE T. PAMI编委，现任IJCV编委。

时  间：2020年11月29日（周日） 上午9：30开始

地  点：南海楼338室

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信息科学技术学院

2020年11月26日