PURPOSE To propose and evaluate P-LORAKS a new calibrationless parallel imaging reconstruction framework. THEORY AND METHODS LORAKS is a flexible and powerful framework that was recently proposed for constrained MRI reconstruction. LORAKS was based on the observation that certain matrices constructed from fully sampled k-space data should have low rank… (More)
Feature noising is an effective mechanism on reducing the risk of overfitting. To avoid an explosive searching space, existing work typically assumes that all features share a single noise level, which is often cross-validated. In this paper, we present a Bayesian feature noising model that flexibly allows for dimension-specific or group-specific noise… (More)
In this section, we show more details about the training and inference of Semi-CRFs following the settings we made in the main paper.
Overlapping community detection plays a key role in statistical network modeling. Despite the importance, popular models such as mixed membership stochastic blockmodels (MMSB)  are often not applicable to real world massive networks due to limited speed and memory of a single computing node. In this project, we develop distributed inference for models… (More)