1 citations · 3 across the 6 of their papers we have counts for
6 papers
Measuring Aleatoric and Epistemic Uncertainty in LLMs: Empirical Evaluation on ID and OOD QA Tasks
Kevin Wang, Subre Abdoul Moktar, Jia Li +2
Large Language Models (LLMs) have become increasingly pervasive, finding applications across many industries and disciplines. Ensuring the trustworthiness of LLM outputs is paramou…
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…
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
Changbin Li, Kangshuo Li, Yuzhe Ou +5
Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes…
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…