13 citations · 29 across the 9 of their papers we have counts for
4 papers · 1 filter
Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation
Yuyang Miao, Yao Xu, Danilo Mandic
Predicting metro passenger flow precisely is of great importance for dynamic traffic planning. Deep learning algorithms have been widely applied due to their robust performance in…
Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Analytics of financial data is inherently a Big Data paradigm, as such data are collected over many assets, asset classes, countries, and time periods. This represents a challenge…
Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks
Chuang Liu, Xueqi Ma, Yibing Zhan +5
Graph Neural Networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and the number of model parameters, which restricts…
Last-iterate convergence analysis of stochastic momentum methods for neural networks
Dongpo Xu, Jinlan Liu, Yinghua Lu +2
The stochastic momentum method is a commonly used acceleration technique for solving large-scale stochastic optimization problems in artificial neural networks. Current convergence…