most citedEvidential Uncertainty Quantification: A Variance-Based Perspective

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Evidential Neural Radiance Fields

Ruxiao Duan, Alex Wong

Understanding sources of uncertainty is fundamental to trustworthy three-dimensional scene modeling. While recent advances in neural radiance fields (NeRFs) achieve impressive accu…

cs.CV2026

Fisheye3R: Adapting Unified 3D Feed-Forward Foundation Models to Fisheye Lenses

Ruxiao Duan, Erin Hong, Dongxu Zhao +3

Feed-forward foundation models for multi-view 3-dimensional (3D) reconstruction have been trained on large-scale datasets of perspective images; when tested on wide field-of-view i…

cs.LG2026

Tuning Out-of-Distribution (OOD) Detectors Without Given OOD Data

Sudeepta Mondal, Xinyi Mary Xie, Ruxiao Duan +2

Existing out-of-distribution (OOD) detectors are often tuned by a separate dataset deemed OOD with respect to the training distribution of a neural network (NN). OOD detectors proc…

cs.LG20231 cited

Evidential Uncertainty Quantification: A Variance-Based Perspective

Ruxiao Duan, Brian Caffo, Harrison X. Bai +2

Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advan…

cs.CV2023

Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning

Ruxiao Duan, Jieneng Chen, Adam Kortylewski +2

Replay-based methods in class-incremental learning (CIL) have attained remarkable success. Despite their effectiveness, the inherent memory restriction results in saving a limited…