3 accepted papers
Quantile Regression (QR) can be used to estimate aleatoric uncertainty in deep neural networks and can generate prediction intervals. Quantifying uncertainty is particularly important in critical applications such as clinical diagnosis, where a realistic assessment of uncertainty is essential in det…
In the case of learning from positive and unlabeled data, the input data consist of (1) observations from the positive class and their corresponding labels and (2) unlabeled observations from both positive and negative classes. Generative Adversarial Networks (GANs) have been used to reduce the prob…
Partial correlations (PCs) of functional magnetic resonance imaging (fMRI) time series play a principal role in revealing connectivity of brain networks. To explore nonlinear behavior of the blood-oxygen-level dependent signal, the present work postulates a kernel-based nonlinear connectivity model…