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Adrien Lafage

2 accepted papers

2025

Torch-Uncertainty: Deep Learning Uncertainty Quantification

NeurIPS 2025spotlight

Deep Neural Networks (DNNs) have demonstrated remarkable performance across various domains, including computer vision and natural language processing. However, they often struggle to accurately quantify their predictions' uncertainty, limiting their broader adoption in critical industrial applicati…

Cited by 0SourcecodeScholar
2023

Packed Ensembles for efficient uncertainty estimation

ICLR 2023top-25%

Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection. However, hardware limitations of real-world systems constrain to smaller ensembles and lower-capacity networks, si…