35 citations · 69 across the 12 of their papers we have counts for
4 papers · 1 filter
Optimizing Distributed Training on Frontier for Large Language Models
Sajal Dash, Isaac Lyngaas, Junqi Yin +5
Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning. Recent studies on loss sca…
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives
Romain Egele, Tyler Chang, Yixuan Sun +2
Machine learning (ML) methods offer a wide range of configurable hyperparameters that have a significant influence on their performance. While accuracy is a commonly used performan…
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
Romain Egele, Isabelle Guyon, Yixuan Sun +1
Hyperparameter optimization (HPO) is crucial for fine-tuning machine learning models but can be computationally expensive. To reduce costs, Multi-fidelity HPO (MF-HPO) leverages in…
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles
Romit Maulik, Romain Egele, Krishnan Raghavan +1
Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DN…