Jan 18, 2020 · In this paper, we develop a novel decoupled and cost-aware multi-objective optimization algorithm, we call Flexible Multi-Objective Bayesian Optimization ( ...
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This is an abstract reprint of a journal article by Iqbal, Su, Kotthoff, and Jamshidi (2023). Abstract. The design of machine learning systems often requires ...
Jul 6, 2023 · Our results indicate that, given the same total experimental budget, FlexiBO discovers designs with 4.8% to 12.4% lower hypervolume error than ...
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Our results indicate that, given the same total experimental budget, FlexiBO discovers designs with 4.8% to. 12.4% lower hypervolume error than the best method ...
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An ap- proach based on decoupled objective evaluations has been proposed to enable independent evaluations across objec- tives in two-objectives optimization of ...
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FlexiBO: A Decoupled Cost-Aware Multi-objective Optimization Approach for Deep Neural Networks (Abstract Reprint). M. Iqbal, J. Su, L. Kotthoff, and P ...
May 6, 2023 · To address this issue, we propose a novel cost-aware decoupled approach that weights the improvement of the hypervolume of the Pareto region by ...
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... Learning and Systems, 184-191, 2024. 3, 2024. FlexiBO: A Decoupled Cost-Aware Multi-objective Optimization Approach for Deep Neural Networks (Abstract Reprint).
Our approach is developed to perform multi-objective optimization on resource constrained devices specially NVIDIA Jetson Tegra X2 (TX2) and NVIDIA Jetson ...
Missing: (Abstract Reprint).
A novel decoupled and cost-aware multi-objective optimization algorithm, which is called Flexible Multi-Objective Bayesian Optimization (FlexiBO),