44 citations · 139 across the 20 of their papers we have counts for
5 papers · 1 filter
Stable Anderson Acceleration for Deep Learning
Massimiliano Lupo Pasini, Junqi Yin, Viktor Reshniak +1
Anderson acceleration (AA) is an extrapolation technique designed to speed-up fixed-point iterations like those arising from the iterative training of DL models. Training DL models…
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma +40
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…
Neural network based order parameter for phase transitions and its applications in high-entropy alloys
Junqi Yin, Zongrui Pei, Michael Gao
Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters…
Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data
Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin +2
We propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discri…
Distributed Training and Optimization Of Neural Networks
Jean-Roch Vlimant, Junqi Yin
Deep learning models are yielding increasingly better performances thanks to multiple factors. To be successful, model may have large number of parameters or complex architectures…