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Learning Vehicle Routing Problems using Policy Optimisation
Deep reinforcement learning (DRL) has been used to learn effective heuri...
12/24/2020 ∙ by Nasrin Sultana, et al. ∙
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Learning to Optimise General TSP Instances
The Travelling Salesman Problem (TSP) is a classical combinatorial optim...
10/23/2020 ∙ by Nasrin Sultana, et al. ∙
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Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
Graph convolutional networks (GCNs) have been widely used for representa...
10/12/2020 ∙ by Wenfeng Liu, et al. ∙
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A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization
Conventional DNN training paradigms typically rely on one training set a...
07/02/2020 ∙ by Boyu Zhang, et al. ∙
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PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and Tumors
Precise segmentation of organs and tumors plays a crucial role in clinic...
07/02/2020 ∙ by Ziqiang Li, et al. ∙
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DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition
State-of-the-art video action recognition models with complex network ar...
06/13/2020 ∙ by Ziming Liu, et al. ∙
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Location-Centered House Price Prediction: A Multi-Task Learning Approach
Accurate house prediction is of great significance to various real estat...
01/07/2019 ∙ by Guangliang Gao, et al. ∙
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Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results
In this report, we suggest nine test problems for multi-task single-obje...
06/12/2017 ∙ by Bingshui Da, et al. ∙
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Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
In this report, we suggest nine test problems for multi-task multi-objec...
06/08/2017 ∙ by Yuan Yuan, et al. ∙
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