5 accepted papers
Using a subset of observed network links, high-order link prediction (HOLP) infers missing hyperedges, that is links connecting three or more nodes. HOLP emerges in several applications, but existing approaches have not dealt with the associated predictor’s performance. To overcome this limitation,…
Higher-order link prediction (HOLP) seeks missing links capturing dependencies among three or more network nodes. Predicting high-order links (HOLs) can for instance reveal hyperlinks in the structure of drug substance and metabolic networks. Existing methods either make restrictive assumptions rega…
Gaussian process latent variable models (GPLVMs) are powerful, yet computationally heavy tools for nonlinear dimensionality reduction. Existing scalable variants utilize low- rank kernel matrix approximants that in essence subsample the embedding space. This work develops an efficient online approac…
Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not fully understood. Sever…
Segmentation of ventricles from cardiac magnetic resonance (MR) images is a key step to obtaining clinical parameters useful for prognosis of cardiac pathologies. To improve upon the performance of existing fully convolutional network (FCN) based automatic right ventricle (RV) segmentation approache…