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GNNFormer: A Graph-based Framework for Cytopathology Report Generation
A common weakness of these works is that they do not explicitly model the structural information among cells, which is a key feature of pathology images and provides significant information for making diagnoses.
Asymmetric Learning for Graph Neural Network based Link Prediction
To the best of our knowledge, AML is the first GNN-LP method adopting an asymmetric learning strategy for node representation learning.
Full-Precision Free Binary Graph Neural Networks
Binary neural networks have become a promising research topic due to their fast inference speed and low energy consumption advantages.
Collaborative Self-Attention for Recommender Systems
Recommender systems (RS), which have been an essential part in a wide range of applications, can be formulated as a matrix completion (MC) problem.
Convolutional Geometric Matrix Completion
Recently, geometric deep learning on graphs (GDLG) is proposed to solve the GMC problem, showing better performance than existing GMC methods including traditional graph regularization based methods.
