3 papers
cs.LG2026
Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning
Tobias Jan Wieczorek, Leon de Andrade, Thomas Möllenhoff +1
Modern deep learning models remain notoriously prone to overconfidence, limiting their reliability in high-stakes applications. Bayesian methods aim to counter this by learning a d…
cs.CV2026
Variational Visual Question Answering for Uncertainty-Aware Selective Prediction
Tobias Jan Wieczorek, Nathalie Daun, Mohammad Emtiyaz Khan +1
Despite remarkable progress in recent years, Vision Language Models (VLMs) remain prone to overconfidence and hallucinations on tasks such as Visual Question Answering (VQA) and Vi…
cs.CV2026
Evaluating the Impact of Post-Training Quantization on Reliable VQA with Multimodal LLMs
Paul Jonas Kurz, Tobias Jan Wieczorek, Mohamed A. Abdelsalam +2
Multimodal Large Language Models (MLLM) are increasingly deployed in domains where both reliability and efficiency are critical. However, current models remain overconfident, produ…