Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions
In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks
(GNNs), a domain where deep learning-based approaches have increasingly shown …
(GNNs), a domain where deep learning-based approaches have increasingly shown …
HLGST: Hybrid local–global spatio-temporal model for travel time estimation using Siamese graph convolutional with triplet networks
Travel time estimation (TTE) is a crucial and challenging task due to the complex spatial and
dynamic temporal correlations between local and global traffic regions. Though many …
dynamic temporal correlations between local and global traffic regions. Though many …
Improved similarity assessment and spectral clustering for unsupervised linking of data extracted from bridge inspection reports
K Liu, N El-Gohary - Advanced Engineering Informatics, 2022 - Elsevier
Textual bridge inspection reports are important data sources for supporting data-driven
bridge deterioration prediction and maintenance decision making. Information extraction …
bridge deterioration prediction and maintenance decision making. Information extraction …
TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features
G Bazhenov, O Platonov, L Prokhorenkova - arXiv preprint arXiv …, 2024 - arxiv.org
Tabular machine learning is an important field for industry and science. In this field, table
rows are usually treated as independent data samples, but additional information about …
rows are usually treated as independent data samples, but additional information about …
Business entity matching with siamese graph convolutional networks
Data integration has been studied extensively for decades and approached from different
angles. However, this domain still remains largely rule-driven and lacks universal …
angles. However, this domain still remains largely rule-driven and lacks universal …
Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning
M Atzeni - 2024 - infoscience.epfl.ch
The ability to reason, plan and solve highly abstract problems is a hallmark of human
intelligence. Recent advancements in artificial intelligence, propelled by deep neural …
intelligence. Recent advancements in artificial intelligence, propelled by deep neural …
Clustered Federated Learning for Heterogeneous Feature Spaces using Siamese Graph Convolutional Neural Network Distance Prediction
Y Suzuki, F Banaei-Kashani - Federated Learning Systems (FLSys) …, 2023 - openreview.net
Federated learning (FL) has been proposed to enhance performance of local machine
learning models across multiple devices while maintaining data privacy. One of the main …
learning models across multiple devices while maintaining data privacy. One of the main …
Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network
There are vast number of configurable parameters in a Radio Access Telecom Network. A
significant amount of these parameters is configured by Radio Node or cell based on their …
significant amount of these parameters is configured by Radio Node or cell based on their …
Building knowledge graphs from technical documents using named entity recognition and edge weight updating neural network with triplet loss for entity normalization
SH Jeon, HJ Lee, J Park, S Cho - Intelligent Data Analysis, 2024 - content.iospress.com
Attempts to express information from various documents in graph form are rapidly
increasing. The speed and volume in which these documents are being generated call for …
increasing. The speed and volume in which these documents are being generated call for …
Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph
SH Jeon, S Cho - arXiv preprint arXiv:2106.07549, 2021 - arxiv.org
Discriminating the matched named entity pairs or identifying the entities' canonical forms are
critical in text mining tasks. More precise named entity normalization in text mining will …
critical in text mining tasks. More precise named entity normalization in text mining will …