NeuroPareto uses a calibrated Bayesian classifier, deep GP surrogates, and an online-trained acquisition network to outperform baselines on Pareto proximity and hypervolume in costly many-objective search.
Differentiable expected hypervolume improvement for parallel multi-objective bayesian optimization.Advances in neural information processing systems, 33:9851–9864
2 Pith papers cite this work. Polarity classification is still indexing.
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An AI interoperability framework between FINALES and Kadi4Mat uses batched Bayesian optimization to explore trade-offs between shorter formation time and higher end-of-life performance in sodium-ion coin cells.
citing papers explorer
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NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces
NeuroPareto uses a calibrated Bayesian classifier, deep GP surrogates, and an online-trained acquisition network to outperform baselines on Pareto proximity and hypervolume in costly many-objective search.
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Accelerating battery research with an AI interface between FINALES and Kadi4Mat
An AI interoperability framework between FINALES and Kadi4Mat uses batched Bayesian optimization to explore trade-offs between shorter formation time and higher end-of-life performance in sodium-ion coin cells.