134 citations · 220 across the 14 of their papers we have counts for
6 papers · 1 filter
Learning Vehicle Routing Problems using Policy Optimisation
Nasrin Sultana, Jeffrey Chan, A. K. Qin +1
Deep reinforcement learning (DRL) has been used to learn effective heuristics for solving complex combinatorial optimisation problem via policy networks and have demonstrated promi…
Learning to Optimise General TSP Instances
Nasrin Sultana, Jeffrey Chan, A. K. Qin +1
The Travelling Salesman Problem (TSP) is a classical combinatorial optimisation problem. Deep learning has been successfully extended to meta-learning, where previous solving effor…
Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
Wenfeng Liu, Maoguo Gong, Zedong Tang +1
Graph convolutional networks (GCNs) have been widely used for representation learning on graph data, which can capture structural patterns on a graph via specifically designed conv…
Distributed Machine Learning for Predictive Analytics in Mobile Edge Computing Based IoT Environments
Prabath Abeysekara, Hai Dong, A. K. Qin
Predictive analytics in Mobile Edge Computing (MEC) based Internet of Things (IoT) is becoming a high demand in many real-world applications. A prediction problem in an MEC-based I…
A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization
Boyu Zhang, A. K. Qin, Hong Pan +1
Conventional DNN training paradigms typically rely on one training set and one validation set, obtained by partitioning an annotated dataset used for training, namely gross trainin…
PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and Tumors
Ziqiang Li, Hong Pan, Yaping Zhu +1
Precise segmentation of organs and tumors plays a crucial role in clinical applications. It is a challenging task due to the irregular shapes and various sizes of organs and tumors…