Search Results for author:
Found 5 papers, 1 papers with code
Bayesian optimization for automatic design of face stimuli
Formally, we employ Bayesian optimization to efficiently search the latent space of state-of-the-art GAN models, with the aim to automatically generate novel faces, to maximize an individual subject's response.
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.
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.
