R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning
Mengyuan Chen, Junyu Gao, Changsheng Xu
2/12/2026
No official code found. Best implementation: MengyuanChen21/ICLR2024-REDL (139 stars, MIT, last commit 2024-11-18).
No official code found. Best implementation: MengyuanChen21/ICLR2024-REDL (139 stars, MIT, last commit 2024-11-18).
Ego4D Goal-Step: Toward Hierarchical Understanding of Procedural Activities
Confidential-DPproof: Confidential Proof of Differentially Private Training
CSG-ODE: ControlSynth Graph ODE For Modeling Complex Evolution of Dynamic Graphs
Grid-Attention: Enhancing Computational Efficiency of Large Vision Models Without Fine-Tuning
Federated Residual Low-Rank Adaptation of Large Language Models
Kernel-Based Evaluation of Conditional Biological Sequence Models
@article{chen2026redl,
title = {R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning},
author = {Mengyuan Chen and Junyu Gao and Changsheng Xu},
year = {2026},
url = {https://api.semanticscholar.org/CorpusID:07bc95eb55d21c6b4e192641179108303c409954},
journal = {ICLR 2024 2024}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/07bc95eb55d21c6b4e192641179108303c409954)