摘要：

Large deep learning models or foundation models such as chatGPT or GPT-4 have been the key factor in driving the recent new wave of AI breakthrough, resulting in huge social and economic impacts. However, even GPT-3 (the predecessor of ChatGPT) was trained on half a trillion words and equipped with 175 billion parameters, which required huge computing resource and energy consumption. With the scale of large AI models keeps increasing, the training and inference efficiency as well as the deployment efficiency become more pressing in order to make large models energy-friendly, accessible and deployable on diverse edge devices and diverse deployment scenarios. In this talk, I will introduce a few works that have been done in my group along this line, particularly on our recently developed elastic deep learning model - stitchable neural networks and large model sparse fine-tuning. 

报告人简介：

Jianfei Cai is a Professor at Faculty of IT, Monash University, where he had served as the inaugural Head for the Data Science & AI Department. Before that, he was Head of Visual and Interactive Computing Division and Head of Computer Communications Division in Nanyang Technological University (NTU). His major research interests include computer vision, deep learning and multimedia. He has successfully trained 30+ PhD students with three getting NTU SCSE Outstanding PhD thesis award. Many of his PhD students joined leading IT companies such as Facebook, Apple, Amazon, and Adobe or become faculty members in reputable universities. He is a co-recipient of paper awards in ACCV, ICCM, IEEE ICIP and MMSP. He serves or has served as an Associate Editor for TPAMI, IJCV, IEEE T-IP, T-MM, and T-CSVT as well as serving as Area Chair for CVPR, ICCV, ECCV, IJCAI, ACM Multimedia, ICME, ICIP and ISCAS. He was the Chair of IEEE CAS VSPC-TC during 2016-2018. He had served as the leading TPC Chair for IEEE ICME 2012 and the best paper award committee chair & co-chair for IEEE T-MM 2020 & 2019. He is the leading General Chair for ACM Multimedia 2024, and a Fellow of IEEE.