Surrogate Model Based Optimization for Finding Robust Deep Learning Model Architectures

Mueller, Juliane; Dumont, Vincent; Ju, Xiangyang

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DOE OSTI · 2001225

Surrogate Model Based Optimization for Finding Robust Deep Learning Model Architectures

Mueller, Juliane·Dumont, Vincent·Ju, Xiangyang

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Abstract

Deep Learning (DL) models are increasingly used throughout the sciences. However, their performance and usefulness depend greatly on their architecture which is defined by hyperparameters such as the number of nodes, layers, the learning rate, etc. Tuning these hyperparameters is time-consuming because evaluating their performance requires a lengthy training step. Stochastic optimizers used in training lead to performance variability and potentially prediction reliability issues. In this talk, we will describe an automated optimization method based on surrogate models and active learning strategies for tuning DL model architectures. We take into account the prediction variability with the goal to identify architectures that make reliable and robust predictions. We demonstrate our developments on an application arising in particle physics.

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deep learning high energy physics hyperparameter optimization MATHEMATICS AND COMPUTING surrogate modeling

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BibTeX RIS

Mueller, Juliane, Dumont, Vincent, Ju, Xiangyang. 2023-09-15. Surrogate Model Based Optimization for Finding Robust Deep Learning Model Architectures. https://www.osti.gov/biblio/2001225

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