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Found 7 papers, 3 papers with code
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.
Reiterative Domain Aware Multi-Target Adaptation
Multi-target domain adaptation is a powerful extension in which a single classifier is learned for multiple unlabeled target domains.
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.
Ultrasound Image Classification using ACGAN with Small Training Dataset
The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis.
Dynamic Embeddings for Interaction Prediction
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention.
Which way? Direction-Aware Attributed Graph Embedding
Most studies ignore the directionality, so as to learn high-quality representations optimized for node classification.
