題  目：Principled Design of Convolutional Neural Networks

内容簡介：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.

報告人：北京大學  林宙辰  教授

報告人簡介：博士生導師，IAPR/IEEE Fellow，國家傑青，中國圖象圖形學學會機器視覺專委會主任，中國自動化學會模式識别與機器智能專委會副主任。研究領域為機器學習、計算機視覺和數值優化。發表論文200餘篇，英文專著2本。多次擔任CVPR 、ICCV、NIPS/NeurIPS、ICML、IJCAI、AAAI和ICLR領域主席，曾任IEEE T. PAMI編委，現任IJCV編委。

時  間：2020年11月29日（周日） 上午9：30開始

地  點：南海樓338室

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2020年11月26日