1 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Evidential Uncertainty Probes for Graph Neural Networks
Linlin Yu, Kangshuo Li, Pritom Kumar Saha +2
Accurate quantification of both aleatoric and epistemic uncertainties is essential when deploying Graph Neural Networks (GNNs) in high-stakes applications such as drug discovery an…
Federated In-Context LLM Agent Learning
Panlong Wu, Kangshuo Li, Junbao Nan +1
Large Language Models (LLMs) have revolutionized intelligent services by enabling logical reasoning, tool use, and interaction with external systems as agents. The advancement of L…
Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Function
Linlin Yu, Bowen Yang, Tianhao Wang +2
The fusion of raw sensor data to create a Bird's Eye View (BEV) representation is critical for autonomous vehicle planning and control. Despite the growing interest in using deep l…
FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models
Panlong Wu, Kangshuo Li, Ting Wang +1
Foundation models have shown great success in natural language processing, computer vision, and multimodal tasks. FMs have a large number of model parameters, thus requiring a subs…