5 papers
Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability
Young-Jin Park, Carson Sobolewski, Navid Azizan
DETR and its variants have emerged as promising architectures for object detection, offering an end-to-end prediction pipeline. In practice, however, DETRs generate hundreds of pre…
Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
Ji Young Byun, Young-Jin Park, Jean-Philippe Corbeil +1
As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust their predictions is equally critical…
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
Young-Jin Park, Kristjan Greenewald, Kaveh Alim +2
Process reward models (PRMs) play a central role in guiding inference-time scaling algorithms for large language models (LLMs). However, we observe that even state-of-the-art PRMs…
Test-Time-Scaling for Zero-Shot Diagnosis with Visual-Language Reasoning
Ji Young Byun, Young-Jin Park, Navid Azizan +1
As a cornerstone of patient care, clinical decision-making significantly influences patient outcomes and can be enhanced by large language models (LLMs). Although LLMs have demonst…
Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models
Young Jin Park, Francois Germain, Jing Liu +6
Decision-making in building energy systems critically depends on the predictive accuracy of relevant time-series models. In scenarios lacking extensive data from a target building,…