3 papers
cs.LG2025
Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification
Adrien Lafage, Olivier Laurent, Firas Gabetni +1
Deep Neural Networks (DNNs) have demonstrated remarkable performance across various domains, including computer vision and natural language processing. However, they often struggle…
cs.LG2025
Ensembling Pruned Attention Heads For Uncertainty-Aware Efficient Transformers
Firas Gabetni, Giuseppe Curci, Andrea Pilzer +3
Uncertainty quantification (UQ) is essential for deploying deep neural networks in safety-critical settings. Although methods like Deep Ensembles achieve strong UQ performance, the…
cs.AI2024
Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation
Gianni Franchi, Dat Nguyen Trong, Nacim Belkhir +2
Uncertainty quantification in text-to-image (T2I) generative models is crucial for understanding model behavior and improving output reliability. In this paper, we are the first to…