Search Results for author:
Found 3 papers, 1 papers with code
GPINN: Physics-informed Neural Network with Graph Embedding
This work proposes a Physics-informed Neural Network framework with Graph Embedding (GPINN) to perform PINN in graph, i. e. topological space instead of traditional Euclidean space, for improved problem-solving efficiency.
Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning
Photoplethysmography (PPG) refers to the measurement of variations in blood volume using light and is a feature of most wearable devices.
Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation
Graph-based deep learning algorithms could utilise the graph structure but raise a few challenges, such as how to determine the weights of the edges and the shallow receptive field caused by the over-smoothing issue.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
