research
∙
07/28/2023
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
Hyperparameter optimization (HPO) is crucial for fine-tuning machine lea...
share
research
∙
02/20/2023
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles
Classical problems in computational physics such as data-driven forecast...
research
∙
10/03/2022
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization
Distributed data storage services tailored to specific applications have...
research
∙
07/01/2022
Asynchronous Distributed Bayesian Optimization at HPC Scale
Bayesian optimization (BO) is a widely used approach for computationally...
research
∙
10/26/2021
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
Deep neural networks are powerful predictors for a variety of tasks. How...
research
∙
10/30/2020
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data
Developing high-performing predictive models for large tabular data sets...
research
∙
09/01/2019
