Resonance statistics-informed methods in automated fitting reduce spin group bias, enhance Wigner statistics consistency, and stabilize resonance density with minimal impact on cross section fit quality.
Breiman et al.Classification and Regression Trees
4 Pith papers cite this work. Polarity classification is still indexing.
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Continuous trajectory representations of lithium-ion battery aging enable consistent knee-point detection and early remaining useful life predictions that remain robust across heterogeneous datasets.
Image augmentations generally improve deep neural network accuracy for galaxy morphology classification, but the gains diminish substantially with larger training datasets and simpler augmentations often suffice.
On 4080 German deceased donors, an ensemble ML model reached MCC 0.76 for kidney discard prediction, with standardized preprocessing and feature selection proving more important than the specific algorithm chosen.
citing papers explorer
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Resonance Statistics -Informed Fitting Applied to Automated Cross Section Evaluation
Resonance statistics-informed methods in automated fitting reduce spin group bias, enhance Wigner statistics consistency, and stabilize resonance density with minimal impact on cross section fit quality.
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Continuous ageing trajectory representations for knee-aware lifetime prediction of lithium-ion batteries across heterogeneous dataset
Continuous trajectory representations of lithium-ion battery aging enable consistent knee-point detection and early remaining useful life predictions that remain robust across heterogeneous datasets.
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The effects of image augmentations when training machine learning models in astronomy
Image augmentations generally improve deep neural network accuracy for galaxy morphology classification, but the gains diminish substantially with larger training datasets and simpler augmentations often suffice.
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Comparative Evaluation of Machine Learning Models for Predicting Donor Kidney Discard
On 4080 German deceased donors, an ensemble ML model reached MCC 0.76 for kidney discard prediction, with standardized preprocessing and feature selection proving more important than the specific algorithm chosen.