Yohan Chatelain
18 papers 37 citations h-index 4 2018–2026
An Analysis of Performance Bottlenecks in MRI Pre-Processing
Magnetic Resonance Image (MRI) pre-processing is a critical step for neuroimaging analysis. However, the computational cost of MRI pre-processing pipelines is a major bottleneck for large cohort...
Conservative & Aggressive NaNs Accelerate U-Nets for Neuroimaging
Deep learning models for neuroimaging increasingly rely on large architectures, making efficiency a persistent concern despite advances in hardware. Through an analysis of numerical uncertainty of...
A numerical variability approach to results stability tests and its application to neuroimaging
Ensuring the long-term reproducibility of data analyses requires results stability tests to verify that analysis results remain within acceptable variation bounds despite inevitable software...
Numerical Stability of DeepGOPlus Inference
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems....
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
Machine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models...
PyTracer: Automatically profiling numerical instabilities in Python
Numerical stability is a crucial requirement of reliable scientific computing. However, despite the pervasiveness of Python in data science, analyzing large Python programs remains challenging due...
Reducing numerical precision preserves classification accuracy in Mondrian Forests
Mondrian Forests are a powerful data stream classification method, but their large memory footprint makes them ill-suited for low-resource platforms such as connected objects. We explored using...
Stochastic Rounding in Low-Precision Transformer Inference: A Variable-Precision Emulation Study of a Small GPT-2
Should low-precision transformer inference use stochastic rounding (SR) or round-to-nearest (RN)? The answer depends on where in the network you look. We isolate this effect by holding the...
Automatic Exploration of Reduced Floating-Point Representations in Iterative Methods
The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging
Reducing numerical precision preserves classification accuracy in Mondrian Forests
Accurate Simulation of Operating System Updates in Neuroimaging Using Monte-Carlo Arithmetic
Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis
Uncertain but Useful: Leveraging CNN Training Variability into Data Augmentation
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 remains largely...
Fuzzy PyTorch: Rapid Numerical Variability Evaluation for Deep Learning Models
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 variability from...