research
∙
07/26/2023
One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration
The precision of unsupervised point cloud registration methods is typica...
share
research
∙
07/08/2023
Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction
Physics-informed neural networks (PINNs) are a newly emerging research f...
research
∙
02/24/2023
TrafFormer: A Transformer Model for Prediction Long-term Traffic
Traffic prediction is a flourishing research field due to its importance...
research
∙
10/24/2022
Deep Edge Intelligence: Architecture, Key Features, Enabling Technologies and Challenges
With the breakthroughs in Deep Learning, recent years have witnessed a m...
research
∙
05/31/2022
Multi-task Optimization Based Co-training for Electricity Consumption Prediction
Real-world electricity consumption prediction may involve different task...
research
∙
05/31/2022
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems
Model-free deep-reinforcement-based learning algorithms have been applie...
research
∙
05/29/2022
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks
Deep neural networks (DNNs) often rely on massive labelled data for trai...
research
∙
10/29/2021
ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
Distributed multi-party learning provides an effective approach for trai...
research
∙
09/17/2021
Learning Enhanced Optimisation for Routing Problems
Deep learning approaches have shown promising results in solving routing...
research
∙
08/22/2021
Evolutionary Ensemble Learning for Multivariate Time Series Prediction
Multivariate time series (MTS) prediction plays a key role in many field...
research
∙
08/20/2021
AdvDrop: Adversarial Attack to DNNs by Dropping Information
Human can easily recognize visual objects with lost information: even lo...
research
∙
04/14/2021
Multi-Party Dual Learning
The performance of machine learning algorithms heavily relies on the ava...
research
∙
04/14/2021
Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework
Multi-party learning provides solutions for training joint models with d...
research
∙
04/02/2021
An attention-based unsupervised adversarial model for movie review spam detection
With the prevalence of the Internet, online reviews have become a valuab...
research
∙
03/11/2021
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Though it is well known that the performance of deep neural networks (DN...
research
∙
12/24/2020
Learning Vehicle Routing Problems using Policy Optimisation
Deep reinforcement learning (DRL) has been used to learn effective heuri...
research
∙
10/23/2020
Learning to Optimise General TSP Instances
The Travelling Salesman Problem (TSP) is a classical combinatorial optim...
research
∙
10/12/2020
Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
Graph convolutional networks (GCNs) have been widely used for representa...
research
∙
07/02/2020
A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization
Conventional DNN training paradigms typically rely on one training set a...
research
∙
07/02/2020
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...
research
∙
06/13/2020
DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition
State-of-the-art video action recognition models with complex network ar...
research
∙
01/07/2019
Location-Centered House Price Prediction: A Multi-Task Learning Approach
Accurate house prediction is of great significance to various real estat...
research
∙
06/12/2017
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...
research
∙
06/08/2017
