数学科学学院学术报告[2025] 013号

(高水平大学建设系列报告1036号)

报告题目: DeepSuM: Deep Sufficient and Efficient Modality Learning Framework

报告人：李挺 助理教授（香港理工大学）

报告时间：2025年3月24日下午16:00-17:00

报告地点：汇星楼514 　　　　　　　　　　　　　　　　　　　　　　　　　

报告内容：Multimodal learning has become a pivotal approach in developing robust learning models with applications spanning multimedia, robotics, large language models, and healthcare. The efficiency of multimodal systems is a critical concern, given the varying costs and resource demands of different modalities. This underscores the necessity for effective modality selection to balance performance gains against resource expenditures. In this study, we propose a novel framework for modality selection that independently learns the representation of each modality. This approach allows for the assessment of each modality's significance within its unique representation space, enabling the development of tailored encoders and facilitating the joint analysis of modalities with distinct characteristics. Our framework aims to enhance the efficiency and effectiveness of multimodal learning by optimizing modality integration and selection.

报告人简历：Dr. Li is an assistant professor in the Department of Applied Mathematics at Hong Kong Polytechnic University. Prior to joining PolyU, he was a postdoctoral associate in Yale University, Biostatistics Department. He received his PhD in Hong Kong University of Science and Technology. His research focuses on data science and statistical learning on complex data, especially on network data, brain data and imaging genomics, generative learning and large models.

欢迎师生参加！

邀请人：王江洲

　　　　　　　　　　　　　　　　　　　　　　　 数学科学学院

　　　　　　　　　　　　　　　　　　　　　 2025年3月17日