2 citations · 3 across the 4 of their papers we have counts for
4 papers
Similarity-Aware Mixture-of-Experts for Data-Efficient Continual Learning
Connor Mclaughlin, Nigel Lee, Lili Su
Machine learning models often need to adapt to new data after deployment due to structured or unstructured real-world dynamics. The Continual Learning (CL) framework enables contin…
On the Power of Source Screening for Learning Shared Feature Extractors
Leo Muxing Wang, Connor Mclaughlin, Lili Su
Learning with shared representation is widely recognized as an effective way to separate commonalities from heterogeneity across various heterogeneous sources. Most existing work i…
Personalized Federated Learning via Feature Distribution Adaptation
Connor J. Mclaughlin, Lili Su
Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. Under heterogeneous clients, h…
RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model
Mingze Wang, Lili Su, Cilin Yan +4
The intelligent interpretation of buildings plays a significant role in urban planning and management, macroeconomic analysis, population dynamics, etc. Remote sensing image buildi…