4 papers
Robustness of deep learning classification to adversarial input on GPUs: asynchronous parallel accumulation is a source of vulnerability
Sanjif Shanmugavelu, Mathieu Taillefumier, Christopher Culver +3
The ability of machine learning (ML) classification models to resist small, targeted input perturbations -- known as adversarial attacks -- is a key measure of their safety and rel…
Pruning as a Defense: Reducing Memorization in Large Language Models
Mansi Gupta, Nikhar Waghela, Sarthak Gupta +2
Large language models have been shown to memorize significant portions of their training data, which they can reproduce when appropriately prompted. This work investigates the impa…
Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
Sanjif Shanmugavelu, Mathieu Taillefumier, Christopher Culver +3
Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumu…
Scientific Computing with Large Language Models
Christopher Culver, Peter Hicks, Mihailo Milenkovic +2
We provide an overview of the emergence of large language models for scientific computing applications. We highlight use cases that involve natural language processing of scientifi…