1 citations · 1 across the 3 of their papers we have counts for
3 papers
Uncertain but Useful: Leveraging CNN Training Variability into Data Augmentation
Inés Gonzalez-Pepe, Vinuyan Sivakolunthu, Yohan Chatelain +1
Deep learning (DL) has transformed neuroimaging by delivering state-of-the-art performance with reduced computation times. Yet, the numerical uncertainty inherent to DL training re…
Fuzzy PyTorch: Rapid Numerical Variability Evaluation for Deep Learning Models
Inés Gonzalez-Pepe, Hiba Akhaddar, Tristan Glatard +1
We introduce Fuzzy PyTorch, a framework for rapid evaluation of numerical variability in deep learning (DL) models. As DL is increasingly applied to diverse tasks, understanding va…
Conservative & Aggressive NaNs Accelerate U-Nets for Neuroimaging
Inés Gonzalez-Pepe, Vinuyan Sivakolunthu, Jacob Fortin +2
Deep learning models for neuroimaging increasingly rely on large architectures, making efficiency a persistent concern despite advances in hardware. Through an analysis of numerica…