A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
[HTML][HTML] Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence
The frequent outbreak of global infectious diseases has prompted the development of rapid
and effective diagnostic tools for the early screening of potential patients in point-of-care …
and effective diagnostic tools for the early screening of potential patients in point-of-care …
Explainable AI methods-a brief overview
Abstract Explainable Artificial Intelligence (xAI) is an established field with a vibrant
community that has developed a variety of very successful approaches to explain and …
community that has developed a variety of very successful approaches to explain and …
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
Trustworthy graph neural networks: Aspects, methods and trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …
methods for diverse real-world scenarios, ranging from daily applications like …
Multi-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspective
Accurate remaining useful life (RUL) prediction has gained increasing attention in modern
maintenance management. Considering the data privacy requirements of distributed multi …
maintenance management. Considering the data privacy requirements of distributed multi …
An analysis of graph convolutional networks and recent datasets for visual question answering
AA Yusuf, F Chong, M **anling - Artificial Intelligence Review, 2022 - Springer
Graph neural network is a deep learning approach widely applied on structural and non-
structural scenarios due to its substantial performance and interpretability recently. In a non …
structural scenarios due to its substantial performance and interpretability recently. In a non …
Enhanced multi-scenario running safety assessment of railway bridges based on graph neural networks with self-evolutionary capability
Accurate and efficient safety assessment for train-bridge coupled (TBC) systems is
paramount in railway engineering. Traditional neural networks, though efficient and apt for …
paramount in railway engineering. Traditional neural networks, though efficient and apt for …
Systematic assessment of various universal machine‐learning interatomic potentials
Abstract Machine‐learning interatomic potentials have revolutionized materials modeling at
the atomic scale. Thanks to these, it is now indeed possible to perform simulations of ab …
the atomic scale. Thanks to these, it is now indeed possible to perform simulations of ab …
Topological deep learning: Going beyond graph data
Topological deep learning is a rapidly growing field that pertains to the development of deep
learning models for data supported on topological domains such as simplicial complexes …
learning models for data supported on topological domains such as simplicial complexes …