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Found 11 papers, 0 papers with code
Adaptive regularization for Lasso models in the context of non-stationary data streams
In this work consider the problem of learning $\ell_1$ regularized linear models in the context of streaming data.
Text-mining the NeuroSynth corpus using Deep Boltzmann Machines
Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function.
Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection
In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity.
Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings.
Streaming regularization parameter selection via stochastic gradient descent
We propose a framework to perform streaming covariance selection.
Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces
Such studies have recently gained momentum and have been applied in a wide variety of settings; ranging from training of healthy subjects to self-regulate neuronal activity to being suggested as potential treatments for clinical populations.
Estimating Optimal Active Learning via Model Retraining Improvement
This MRI framework reveals intricate estimation issues that in turn motivate the construction of new statistical AL algorithms.
When does Active Learning Work?
To address these questions, a comprehensive experimental simulation study of Active Learning is presented.
Targeting Optimal Active Learning via Example Quality
This work presents a new theoretical approach to AL, example quality, which defines optimal AL behaviour in terms of ELR.
Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series
We apply the SINGLE algorithm to functional MRI data from 24 healthy patients performing a choice-response task to demonstrate the dynamic changes in network structure that accompany a simple but attentionally demanding cognitive task.
A better Beta for the H measure of classification performance
The area under the ROC curve is widely used as a measure of performance of classification rules.
