Search Results for author:
Found 7 papers, 3 papers with code
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation
So, can we propose a novel framework to combine GNN and Transformer, integrating both GNN's local information aggregation and Transformer's global information modeling ability to eliminate the over-smoothing problem?
SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention
In this work, we propose a novel insight into integrating SNNs with Graph Transformers and design a Spiking Graph Attention (SGA) module.
Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling
To better model the diversity of interactions, STGNN introduces a novel aggregation mechanism to organize the most significant historical neighbors' information and adaptively obtain the significance of node pairs.
Spectrum-BERT: Pre-training of Deep Bidirectional Transformers for Spectral Classification of Chinese Liquors
Spectral detection technology, as a non-invasive method for rapid detection of substances, combined with deep learning algorithms, has been widely used in food detection.
SCAI: A Spectral data Classification framework with Adaptive Inference for the IoT platform
To address the above issues, we propose a Spectral data Classification framework with Adaptive Inference.
CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks
Cascade prediction aims at modeling information diffusion in the network.
Data-Driven Based Tiny-YOLOv3 Method for Front Vehicle Detection Inducing SPP-Net
In addition, combined with the characteristics of the vehicle size in the road image ahead, K-means clustering method is used to select the appropriate number and size of target candidate boxes.
