The Factor Graph Neural Network model for Interpretable Deep Learning. While deep learning has achieved great success in many fields, one common criticism about deep learning is its lack of ...

In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, ...

Traditional methods of multi-label text classification, particularly deep learning, have achieved remarkable results. However, most of these methods use word2vec technology to represent sequential ...

Abstract: Aiming at improving the classification accuracy with limited numbers of labeled pixels in polarimetric synthetic aperture radar (PolSAR) image classification task, this paper presents a ...

The knowledge graph enhanced deep-learning model can exhibit excellent performance in the clinical practice task. Background: Although much progress has been made in AI, several challenges remain ...

Deep Learning and Machine Learning has made breakthroughs in recent years. There is tens of billions of dollars going into development of the new AI. Google and Deep Mind are recognizing that Deep ...

For multimodal, symbolic sequences and graph data are used as the inputs of a neural network. This configuration file specifies the program of model as "model_multimodal.py", which includes definition ...

Graph Foundation Model. Higher Education Press . Journal Frontiers of Computer Science DOI 10.1007/s11704-024-40046-0 ...