Search Results for author: Nasrullah Sheikh

Found 7 papers, 3 papers with code

Scaling Knowledge Graph Embedding Models

no code implementations8 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.

Knowledge Graph Embedding Link Prediction

Reiterative Domain Aware Multi-Target Adaptation

no code implementations26 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.

Domain Adaptation Multi-target Domain Adaptation

Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention

no code implementations14 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.

Graph Attention Knowledge Graph Embedding +1

Relation-aware Graph Attention Model With Adaptive Self-adversarial Training

no code implementations14 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.

Entity Embeddings Graph Attention +1

Ultrasound Image Classification using ACGAN with Small Training Dataset

1 code implementation31 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.

Classification Data Augmentation +3

Dynamic Embeddings for Interaction Prediction

1 code implementation10 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.

Recommendation Systems

Which way? Direction-Aware Attributed Graph Embedding

1 code implementation30 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.

General Classification Graph Embedding +2

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