6 citations · 14 across the 6 of their papers we have counts for
8 papers
HammingMesh: A Network Topology for Large-Scale Deep Learning
Torsten Hoefler, Tommaso Bonato, Daniele De Sensi +7
Numerous microarchitectural optimizations unlocked tremendous processing power for deep neural networks that in turn fueled the AI revolution. With the exhaustion of such optimizat…
The static parallel distribution algorithms for hybrid density-functional calculations in HONPAS package
Xinming Qin, Honghui Shang, Lei Xu +4
Hybrid density-functional calculation is one of the most commonly adopted electronic structure theory used in computational chemistry and materials science because of its balance b…
Deep Learning for Post-Processing Ensemble Weather Forecasts
Peter Grönquist, Chengyuan Yao, Tal Ben-Nun +4
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which c…
Predicting Weather Uncertainty with Deep Convnets
Peter Grönquist, Tal Ben-Nun, Nikoli Dryden +4
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations.…
The Scalability for Parallel Machine Learning Training Algorithm: Dataset Matters
Daning Cheng, Hanping Zhang, Fen Xia +2
To gain a better performance, many researchers put more computing resource into an application. However, in the AI area, there is still a lack of a successful large-scale machine l…
Using Known Information to Accelerate HyperParameters Optimization Based on SMBO
Cheng Daning, Zhang Hanping, Xia Fen +2
Automl is the key technology for machine learning problem. Current state of art hyperparameter optimization methods are based on traditional black-box optimization methods like SMB…