no code implementations • 8 Jan 2022 • Nasrullah Sheikh, Xiao Qin, Berthold Reinwald, Chuan Lei
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 cost and huge memory footprint.
no code implementations • 26 Aug 2021 • Sudipan Saha, Shan Zhao, Nasrullah Sheikh, Xiao Xiang Zhu
Multi-target domain adaptation is a powerful extension in which a single classifier is learned for multiple unlabeled target domains.
no code implementations • 14 Feb 2021 • Nasrullah Sheikh, Xiao Qin, Berthold Reinwald, Christoph Miksovic, Thomas Gschwind, Paolo Scotton
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 prediction and entity matching.
no code implementations • 14 Feb 2021 • Xiao Qin, Nasrullah Sheikh, Berthold Reinwald, Lingfei Wu
Furthermore, the expressivity of the learned representation depends on the quality of negative samples used during training.
1 code implementation • 31 Jan 2021 • Sudipan Saha, Nasrullah Sheikh
The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis.
1 code implementation • 10 Nov 2020 • Zekarias T. Kefato, Sarunas Girdzijauskas, Nasrullah Sheikh, Alberto Montresor
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention.
1 code implementation • 30 Jan 2020 • Zekarias T. Kefato, Nasrullah Sheikh, Alberto Montresor
Most studies ignore the directionality, so as to learn high-quality representations optimized for node classification.