Interpretable Graph Transformer Network for Predicting Adsorption Isotherms of Metal-Organic Frameworks
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00876
Abstract: Predicting interactions between metal-organic frameworks (MOFs) and their adsorbates based on structures is critical to design high-performance porous materials. Many gas uptake prediction models have been proposed, but adsorption isotherm prediction is still challenging for… read more here.
Keywords: adsorption; adsorption isotherms; network; organic frameworks ... See more keywords
Integrating Heterogeneous Graphs Using Graph Transformer Encoder for Solving Math Word Problems
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3257571
Abstract: This paper introduces a novel method that integrates structural information with training deep neural models to solve math word problems. Prior works adopt the graph structure to represent rich information residing in the input sentences.… read more here.
Keywords: heterogeneous graphs; graph; transformer encoder; math word ... See more keywords
Local Information-Enhanced Graph-Transformer for Hyperspectral Image Change Detection With Limited Training Samples
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2023.3269892
Abstract: Hyperspectral image change detection (HSI-CD) is a challenging task that focuses on identifying the differences between multitemporal HSIs. The recent advancement of convolutional neural network (CNN) has made great progress on HSIs-CD. However, due to… read more here.
Keywords: information; local information; image change; graph transformer ... See more keywords
A Graph-Transformer for Whole Slide Image Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on medical imaging"
DOI: 10.1109/tmi.2022.3176598
Abstract: Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade.… read more here.
Keywords: classification; learning; whole slide; slide image ... See more keywords
Synchronous Spatiotemporal Graph Transformer: A New Framework for Traffic Data Prediction.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3169488
Abstract: Modeling the spatiotemporal relationship (STR) of traffic data is important yet challenging for existing graph networks. These methods usually capture features separately in temporal and spatial dimensions or represent the spatiotemporal data by adopting multiple… read more here.
Keywords: synchronous spatiotemporal; spatiotemporal graph; graph; traffic data ... See more keywords
MeshFormer: High‐resolution Mesh Segmentation with Graph Transformer
Sign Up to like & getrecommendations! Published in 2022 at "Computer Graphics Forum"
DOI: 10.1111/cgf.14655
Abstract: Graph transformer has achieved remarkable success in graph‐based segmentation tasks. Inspired by this success, we propose a novel method named MeshFormer for applying the graph transformer to the semantic segmentation of high‐resolution meshes. The main… read more here.
Keywords: graph transformer; segmentation; high resolution; resolution ... See more keywords