The precision of unsupervised point cloud registration methods is typica...
Physics-informed neural networks (PINNs) are a newly emerging research
f...
Traffic prediction is a flourishing research field due to its importance...
With the breakthroughs in Deep Learning, recent years have witnessed a
m...
Real-world electricity consumption prediction may involve different task...
Model-free deep-reinforcement-based learning algorithms have been applie...
Deep neural networks (DNNs) often rely on massive labelled data for trai...
Distributed multi-party learning provides an effective approach for trai...
Deep learning approaches have shown promising results in solving routing...
Multivariate time series (MTS) prediction plays a key role in many field...
Human can easily recognize visual objects with lost information: even lo...
The performance of machine learning algorithms heavily relies on the
ava...
Multi-party learning provides solutions for training joint models with
d...
With the prevalence of the Internet, online reviews have become a valuab...
Though it is well known that the performance of deep neural networks (DN...
Deep reinforcement learning (DRL) has been used to learn effective heuri...
The Travelling Salesman Problem (TSP) is a classical combinatorial
optim...
Graph convolutional networks (GCNs) have been widely used for representa...
Conventional DNN training paradigms typically rely on one training set a...
Precise segmentation of organs and tumors plays a crucial role in clinic...
State-of-the-art video action recognition models with complex network
ar...
Accurate house prediction is of great significance to various real estat...
In this report, we suggest nine test problems for multi-task single-obje...
In this report, we suggest nine test problems for multi-task multi-objec...