3 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It
Guoxuan Xia, Olivier Laurent, Gianni Franchi +1
Label smoothing (LS) is a popular regularisation method for training neural networks as it is effective in improving test accuracy and is simple to implement. ``Hard'' one-hot labe…
Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models
Gianni Franchi, Olivier Laurent, Maxence Leguéry +3
Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks, yet they often struggle with reliable uncertainty quantification - a critical requirement for real…
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
Olivier Laurent, Emanuel Aldea, Gianni Franchi
The distribution of the weights of modern deep neural networks (DNNs) - crucial for uncertainty quantification and robustness - is an eminently complex object due to its extremely…
Learning to Generate Training Datasets for Robust Semantic Segmentation
Marwane Hariat, Olivier Laurent, Rémi Kazmierczak +4
Semantic segmentation methods have advanced significantly. Still, their robustness to real-world perturbations and object types not seen during training remains a challenge, partic…
Packed-Ensembles for Efficient Uncertainty Estimation
Olivier Laurent, Adrien Lafage, Enzo Tartaglione +4
Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detec…