19 citations · 39 across the 10 of their papers we have counts for
5 papers · 1 filter
Large-Scale Application of Fault Injection into PyTorch Models -- an Extension to PyTorchFI for Validation Efficiency
Ralf Graafe, Qutub Syed Sha, Florian Geissler +1
Transient or permanent faults in hardware can render the output of Neural Networks (NN) incorrect without user-specific traces of the error, i.e. silent data errors (SDE). On the o…
BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture
Syed Sha Qutub, Neslihan Kose, Rafael Rosales +6
This paper introduces the Budding Ensemble Architecture (BEA), a novel reduced ensemble architecture for anchor-based object detection models. Object detection models are crucial i…
Envisioning a Safety Island to Enable HPC Devices in Safety-Critical Domains
Jaume Abella, Francisco J. Cazorla, Sergi Alcaide +3
HPC (High Performance Computing) devices increasingly become the only alternative to deliver the performance needed in safety-critical autonomous systems (e.g., autonomous cars, un…
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity
Rafael Rosales, Pablo Munoz, Michael Paulitsch
In this paper, we investigate the relationship between diversity metrics, accuracy, and resiliency to natural image corruptions of Deep Learning (DL) image classifier ensembles. We…
Evaluation of Confidence-based Ensembling in Deep Learning Image Classification
Rafael Rosales, Peter Popov, Michael Paulitsch
Ensembling is a successful technique to improve the performance of machine learning (ML) models. Conf-Ensemble is an adaptation to Boosting to create ensembles based on model confi…