3 papers
cs.LG2025
LEAP: Local ECT-Based Learnable Positional Encodings for Graphs
Juan Amboage, Ernst Röell, Patrick Schnider +1
Graph neural networks (GNNs) largely rely on the message-passing paradigm, where nodes iteratively aggregate information from their neighbors. Yet, standard message passing neural…
cs.LG2023
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…
physics.data-an2023
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…