7 citations · 14 across the 5 of their papers we have counts for
7 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…
Reiterative Domain Aware Multi-Target Adaptation
Sudipan Saha, Shan Zhao, Nasrullah Sheikh +1
Most domain adaptation methods focus on single-source-single-target adaptation settings. Multi-target domain adaptation is a powerful extension in which a single classifier is lear…
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…