19 citations · 41 across the 5 of their papers we have counts for
4 papers · 1 filter
Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing
Juan Pablo García Amboage, Eric Wulff, Maria Girone +1
Hyperparameter Optimization (HPO) of Deep Learning-based models tends to be a compute resource intensive process as it usually requires to train the target model with many differen…
Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors
Joosep Pata, Eric Wulff, Farouk Mokhtar +4
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Ci…
Progress towards an improved particle flow algorithm at CMS with machine learning
Farouk Mokhtar, Joosep Pata, Javier Duarte +3
The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CER…
Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC
Eric Wulff, Maria Girone, David Southwick +2
Training and Hyperparameter Optimization (HPO) of deep learning-based AI models are often compute resource intensive and calls for the use of large-scale distributed resources as w…