Layer attentionGraph convolution networkPredictIn this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. LAGCN has achieved good performance in predicting miRNA-disease associations, and it is superior to other association prediction methods and baseline methods....
Graph Convolutional Network (GCN):gnn.GCNLayer Graph Attention Network (GAT):gnn.GATLayerandgnn.MultiHeadGATLayer Prerequisites TensorFlow 2.0 References Kipf, Thomas N., and Max Welling. “Semi-Supervised Classification with Graph Convolutional Networks.” ArXiv:1609.02907 [Cs, Stat], September 9,...
The medical field has attracted the attention of researchers in applying machine learning techniques to the detection, and monitoring of life-threatening diseases such as breast cancer (BC). Proper detection and monitoring contribute immensely to the survival of BC patients, which is largely dependent...
python transformers pytorch neural-networks gpt layer-normalization attention-is-all-you-need multi-head-self-attention gpt-3 dropout-layers residual-connections large-language-models llms llm-training Updated Dec 5, 2023 Python CyberZHG / torch-layer-normalization Star 19 Code Issues Pull requests...
UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification Graph and hypergraph representation learning has attracted increasing attention from various research fields. Despite the decent performance and fruitful a... M Zou,Z Gan,Y Wang,... 被引量: 0发表: 2023年 On di...
Shi, J. et al. Cervical cell classification with graph convolutional network.Comput Methods Prog. Biomed.198, 105807 (2021). ArticleGoogle Scholar Sornapudi, S. et al. Comparing deep learning models for multi-cell classification in liquid-based cervical cytology image.AMIA Annu Symp. Proc.2019,...
Recurrent neural network (RNN) What is the Recurrent Neural Network? Defination A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a temporal sequenc...论文阅读——R2U-Net:Recurrent Residual Convolutional Neural ...
Furthermore, autoencoders are exploited for HSI clustering techniques, where special graph convolutional embedding is incorporated to preserve the adaptive layer-wise feature locality [20,21]. The effectiveness of deep learning paradigms has been attributed to their learning ability, achieved through non...
The framework has two stages: a multi-layer preprocessing stage and a subsequent segmentation stage employing a U-Net with a multi-residual attention block. The multi-layer preprocessing stage has three steps. The first step is noise reduction, employing a U-shaped convolutional neural network ...
[19] used a Cifar-style convolutional neural network (CNN) to create class labels and probability maps, followed by a graph theory and dynamic programming step to derive final layer boundaries in OCT images from patients with age-related macular degeneration (AMD). Shortly thereafter, Roy et al...