IEEE

Transformer-Based Phase Space Graph Convolutional Network for Modulation Recognition

Abstract: Recent advances in deep learning-based signal re-representation methods have achieved significant breakthroughs in tasks like signal modulation recognition. In this paper, we propose a ...
Frontiers

A multi-domain graph convolutional network-based prediction model for personalized motor imagery action

Brain-computer interfaces (BCIs) are advanced and innovative systems that enable direct communication between humans and external devices by utilizing data encoded in the brain activity (Shi et al., ...
IEEE

STr-GCN: Dual Spatial Graph Convolutional Network and Transformer Graph Encoder for 3D Hand Gesture Recognition

Abstract: Skeleton-based hand gesture recognition is a challenging task that sparked a lot of attention in recent years, especially with the rise of Graph Neural Networks. In this paper, we propose a ...
techtimes

Graph Convolutional Networks are Bringing Emotion Recognition Closer to Machines. Here’s how.

Graph Convolutional Network (GCN): a type of Convolutional Neural Network that works with graphs to leverage the structural information represented in them. Your main takeaway here should be that what ...
GitHub

Small Sample Multi-Omics Classification via a Graph Convolutional Encoder–Decoder Network for Missing-Omics Recovery and Biomarker Discovery

Separate encoders for each omics type (mRNA, DNA methylation, miRNA). Extract latent representations from high-dimensional input features. MOGEDN/ ├── checkpoint_pretraining/ # Saved pretrained model ...
GitHub

ugrkilc/Graph-Convolutional-Networks

This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of nodes in a graph, as described in our paper: Thomas N. Kipf, Max Welling, ...