Graph neural networks (GNNs) have shown prominent performance on attribu...
Recommender systems play a crucial role in helping users discover inform...
Accurate segmentation of large areas from very high spatial-resolution (...
To provide real-time parking information, existing studies focus on
pred...
This paper presents a novel 3D mapping robot with an omnidirectional
fie...
Molecular representation learning is crucial for the problem of molecula...
Deep learning based change detection methods have received wide attentoi...
Knowledge Graph (KG) errors introduce non-negligible noise, severely
aff...
The training of graph neural networks (GNNs) is extremely time consuming...
Cold start is an essential and persistent problem in recommender systems...
Graph anomaly detection (GAD) is a vital task since even a few anomalies...
Graph contrastive learning (GCL) has emerged as an effective tool for
le...
Although there are many improvements to WENO3-Z that target the achievem...
We introduce a novel masked graph autoencoder (MGAE) framework to perfor...
In the analytic hierarchy process (AHP) based flood risk estimation mode...
As we found previously, when critical points occur within grid intervals...
Graph neural networks (GNNs) integrate deep architectures and topologica...
Floods are highly uncertain events, occurring in different regions, with...
In this paper, we introduce a challenging global large-scale ship databa...
Most existing deep learning-based pan-sharpening methods have several wi...
Hyperspectral images (HSIs) have been widely used in a variety of
applic...
Recently, satellites with high temporal resolution have fostered wide
at...
In response to the soaring needs of human mobility data, especially duri...
The rapid development of remote sensing techniques provides rich,
large-...
Rapid, accurate and robust detection of looming objects in cluttered mov...
Robust and accurate detection of small moving targets in cluttered movin...
Sequential recommendation has become increasingly essential in various o...
On the idea of mapped WENO-JS scheme, properties of mapping methods are
...
Understanding human mobility dynamics among places provides fundamental
...
Controlled experiments are widely used in many applications to investiga...
The conventional deep learning approaches for solving time-series proble...
The outbreak of COVID-19 highlights the need for a more harmonized, less...
Graph neural networks (GNNs), which learn the representation of a node b...
Advances in extractive machine reading comprehension (MRC) rely heavily ...
Car-sharing issue is a popular research field in sharing economy. In thi...
Graph neural networks (GNN) has been demonstrated to be effective in
cla...
Sequence labeling is a fundamental framework for various natural languag...
Named entity recognition (NER) identifies typed entity mentions in raw t...
Car-sharing problem is a popular research field in sharing economy. In t...
Graph neural networks (GNN) has been successfully applied to operate on ...
This paper describes a multimodal vision sensor that integrates three ty...
Anomaly detection aims to distinguish observations that are rare and
dif...
Visual localization is an attractive problem that estimates the camera
l...
The aim of this paper is to introduce a new design of experiment method ...
Adversarial examples are delicately perturbed inputs, which aim to misle...
In recent years, multi-agent epistemic planning has received attention f...