In traditional Graph Neural Networks (GNNs), the assumption of a fixed
e...
Sleep stage classification is crucial for detecting patients' health
con...
With the boom of Large Language Models (LLMs), the research of solving M...
AI has made significant progress in solving math problems, but geometry
...
Graph neural network (GNN) has gained increasing popularity in recent ye...
Connectivity is a fundamental structural property of matroids, and has b...
Solving math word problem (MWP) with AI techniques has recently made gre...
Vulnerability detection is a critical problem in software security and
a...
Verification plays an essential role in the formal analysis of
safety-cr...
In recent years, there has been an explosion of research into developing...
Deep neural networks (DNNs) are known to be vulnerable to adversarial
ge...
Spiking neural networks (SNNs), a variant of artificial neural networks
...
Heterogeneous graph neural network has unleashed great potential on grap...
Dense prediction tasks are common for 3D point clouds, but the inherent
...
With the employment of smart meters, massive data on consumer behaviour ...
Hazard and Operability Analysis (HAZOP) is a powerful safety analysis
te...
Spatial-temporal data contains rich information and has been widely stud...
With its growing use in safety/security-critical applications, Deep Lear...
Adversarial training has been shown to be one of the most effective
appr...
In this paper, we propose a novel multi-level aggregation network to reg...
This paper proposes an adaptive auxiliary task learning based approach f...
Place recognition is key to Simultaneous Localization and Mapping (SLAM)...
While dropout is known to be a successful regularization technique, insi...
This paper proposes to study neural networks through neuronal correlatio...
Graph representation learning has drawn increasing attention in recent y...
The increasing use of Machine Learning (ML) components embedded in auton...
Segmentation is an essential operation of image processing. The convolut...
While Deep Reinforcement Learning (DRL) provides transformational
capabi...
This tutorial aims to introduce the fundamentals of adversarial robustne...
Deep learning achieves remarkable performance on pattern recognition, bu...
Semi-supervised approaches for crowd counting attract attention, as the ...
The utilisation of Deep Learning (DL) is advancing into increasingly mor...
We propose a new approach to train a variational information bottleneck ...
The utilisation of Deep Learning (DL) raises new challenges regarding it...
Intensive research has been conducted on the verification and validation...
Spiking neural networks (SNNs) offer an inherent ability to process
spat...
A key impediment to the use of AI is the lacking of transparency, especi...
This paper presents a formal verification guided approach for a principl...
This paper studies the embedding and synthesis of knowledge in tree ense...
The previous study has shown that universal adversarial attacks can fool...
This paper studies the novel concept of weight correlation in deep neura...
Safety concerns on the deep neural networks (DNNs) have been raised when...
Sudden bursts of information cascades can lead to unexpected consequence...
Long-term autonomy requires autonomous systems to adapt as their capabil...
Neural Networks (NNs) are known to be vulnerable to adversarial attacks....
Increasingly sophisticated mathematical modelling processes from Machine...
This paper studies the reliability of a real-world learning-enabled syst...
Recurrent neural networks (RNNs) have been applied to a broad range of
a...
The battery is a key component of autonomous robots. Its performance lim...
Deep neural networks (DNNs) increasingly replace traditionally developed...