7 citations · 14 across the 4 of their papers we have counts for
6 papers
Scaling Knowledge Graph Embedding Models
Nasrullah Sheikh, Xiao Qin, Berthold Reinwald +1
Developing scalable solutions for training Graph Neural Networks (GNNs) for link prediction tasks is challenging due to the high data dependencies which entail high computational c…
Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention
Nasrullah Sheikh, Xiao Qin, Berthold Reinwald +3
Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space which can be used for various downstream machine learning tasks such as link…
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training
Xiao Qin, Nasrullah Sheikh, Berthold Reinwald +1
This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. We particularly address two building blocks in the pipel…
Ultrasound Image Classification using ACGAN with Small Training Dataset
Sudipan Saha, Nasrullah Sheikh
B-mode ultrasound imaging is a popular medical imaging technique. Like other image processing tasks, deep learning has been used for analysis of B-mode ultrasound images in the las…
Dynamic Embeddings for Interaction Prediction
Zekarias T. Kefato, Sarunas Girdzijauskas, Nasrullah Sheikh +1
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. While the last decade has seen an explosion of RSs aimed at identi…
Which way? Direction-Aware Attributed Graph Embedding
Zekarias T. Kefato, Nasrullah Sheikh, Alberto Montresor
Graph embedding algorithms are used to efficiently represent (encode) a graph in a low-dimensional continuous vector space that preserves the most important properties of the graph…