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A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction
Surface-based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019
Prediction of Fluid Intelligence From T1-Weighted Magnetic Resonance Images
Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI
Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry
Predict Fluid Intelligence of Adolescent Using Ensemble Learning
Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach
Predicting Fluid intelligence from structural MRI using Random Forest regression
Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data
An AutoML Approach for the Prediction of Fluid Intelligence From MRI-Derived Features
Predicting Fluid Intelligence from MRI images with Encoder-decoder Regularization
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
Ensemble Modeling of Neurocognitive Performance Using MRI-derived Brain Structure Volumes
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
Predicting fluid intelligence using anatomical measures within functionally defined brain networks
Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs
Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction
Adolescent fluid intelligence prediction from regional brain volumes and cortical curvatures using BlockPC-XGBoost
Cortical and Subcortical Contributions to Predicting Intelligence using 3D ConvNets.
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction
Surface-based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019
Prediction of Fluid Intelligence From T1-Weighted Magnetic Resonance Images
Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI
Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry
Predict Fluid Intelligence of Adolescent Using Ensemble Learning
Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach
Predicting Fluid intelligence from structural MRI using Random Forest regression
Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data
An AutoML Approach for the Prediction of Fluid Intelligence From MRI-Derived Features
Predicting Fluid Intelligence from MRI images with Encoder-decoder Regularization
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
Ensemble Modeling of Neurocognitive Performance Using MRI-derived Brain Structure Volumes
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
Predicting fluid intelligence using anatomical measures within functionally defined brain networks
Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs
Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction
Adolescent fluid intelligence prediction from regional brain volumes and cortical curvatures using BlockPC-XGBoost
Cortical and Subcortical Contributions to Predicting Intelligence using 3D ConvNets.