Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
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…
cs.LG2026
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…
cs.LG2024
Fast and Robust State Estimation and Tracking via Hierarchical Learning
Connor Mclaughlin, Matthew Ding, Deniz Erdogmus +1
Fast and reliable state estimation and tracking are essential for real-time situation awareness in Cyber-Physical Systems (CPS) operating in tactical environments or complicated ci…