A Bayesian active learning method with statistical feature engineering and multi-output Gaussian processes selects target hyperelastic metamaterial designs from 50,000 candidates using under 0.5% high-fidelity oracle calls.
Deep learning object detection in materials science: Current state and future directions.Computa- tional Materials Science, 211:111527, 2022
2 Pith papers cite this work. Polarity classification is still indexing.
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Mask-conditioned LDM generates synthetic TEM defect image-mask pairs that augment small experimental sets and produce up to 0.02 gain in harmonic-mean F1 for combined detection and classification with Mask R-CNN.
citing papers explorer
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Data-efficient Bayesian-guided design selection from large candidate sets: Application to hyperelastic stochastic metamaterials
A Bayesian active learning method with statistical feature engineering and multi-output Gaussian processes selects target hyperelastic metamaterial designs from 50,000 candidates using under 0.5% high-fidelity oracle calls.
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Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation
Mask-conditioned LDM generates synthetic TEM defect image-mask pairs that augment small experimental sets and produce up to 0.02 gain in harmonic-mean F1 for combined detection and classification with Mask R-CNN.