13 citations · 29 across the 9 of their papers we have counts for
9 papers
Steering topology distributions for unified generative design of architected metamaterials
Haolin Li, Yuyang Miao, Menglei Li +9
Architected metamaterials derive their functions from structure, creating vast opportunities to program physical responses through topology design. However, existing design methods…
UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization
Yiming Jiang, Jinlan Liu, Dongpo Xu +1
Adam-type algorithms have become a preferred choice for optimisation in the deep learning setting, however, despite success, their convergence is still not well understood. To this…
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Andrea Cini, Danilo Mandic, Cesare Alippi
Relationships among time series can be exploited as inductive biases in learning effective forecasting models. In hierarchical time series, relationships among subsets of sequences…
Graph Tensor Networks: An Intuitive Framework for Designing Large-Scale Neural Learning Systems on Multiple Domains
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Despite the omnipresence of tensors and tensor operations in modern deep learning, the use of tensor mathematics to formally design and describe neural networks is still under-expl…
Fair and skill-diverse student group formation via constrained k-way graph partitioning
Alexander Jenkins, Imad Jaimoukha, Ljubisa Stankovic +1
Forming the right combination of students in a group promises to enable a powerful and effective environment for learning and collaboration. However, defining a group of students i…
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…