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OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
Detecting Temporal shape changes with the Euler Characteristic Transform
Organoids are multi-cellular structures which are cultured in vitro from stem cells to resemble specific organs (e. g., brain, liver) in their three-dimensional composition.
Scaling Knowledge Graph Embedding Models
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.
Medical Entity Disambiguation Using Graph Neural Networks
Entity disambiguation (also referred to as entity linking) is considered as an essential task in unlocking the wealth of such medical KBs.
Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention
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.
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training
Furthermore, the expressivity of the learned representation depends on the quality of negative samples used during training.
A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events
An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug.
