3 papers
cs.CV2026
Instance-Level Post Hoc Uncertainty Quantification in Object Detection
Chongzhe Zhang, Zifan Zeng, Qunli Zhang +2
Object detection is a safety-critical component of autonomous driving. It is essential to quantify the uncertainty in bounding-box predictions for safety assurance. Post hoc uncert…
cs.AI2025
The Safety Challenge of World Models for Embodied AI Agents: A Review
Lorenzo Baraldi, Zifan Zeng, Chongzhe Zhang +8
The rapid progress in embodied artificial intelligence has highlighted the necessity for more advanced and integrated models that can perceive, interpret, and predict environmental…
cs.DC2024
Training Overhead Ratio: A Practical Reliability Metric for Large Language Model Training Systems
Ning Lu, Qian Xie, Hao Zhang +4
Large Language Models (LLMs) are revolutionizing the AI industry with their superior capabilities. Training these models requires large-scale GPU clusters and significant computing…