b'Xiaowei Huang'

research

∙ 09/09/2023

Symplectic Structure-Aware Hamiltonian (Graph) Embeddings

In traditional Graph Neural Networks (GNNs), the assumption of a fixed e...

0 Jiaxu Liu, et al. ∙

research

∙ 09/05/2023

Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning

Sleep stage classification is crucial for detecting patients' health con...

0 Yuze Liu, et al. ∙

research

∙ 09/04/2023

MathAttack: Attacking Large Language Models Towards Math Solving Ability

With the boom of Large Language Models (LLMs), the research of solving M...

0 ZiHao Zhou, et al. ∙

research

∙ 08/05/2023

A Symbolic Character-Aware Model for Solving Geometry Problems

AI has made significant progress in solving math problems, but geometry ...

0 Maizhen Ning, et al. ∙

research

∙ 07/07/2023

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

Graph neural network (GNN) has gained increasing popularity in recent ye...

0 Tiehua Zhang, et al. ∙

research

∙ 06/21/2023

Quantum and classical query complexities for determining connectedness of matroids

Connectivity is a fundamental structural property of matroids, and has b...

0 Xiaowei Huang, et al. ∙

research

∙ 06/15/2023

Learning by Analogy: Diverse Questions Generation in Math Word Problem

Solving math word problem (MWP) with AI techniques has recently made gre...

0 ZiHao Zhou, et al. ∙

research

∙ 06/02/2023

DSHGT: Dual-Supervisors Heterogeneous Graph Transformer – A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities

Vulnerability detection is a critical problem in software security and a...

0 Tiehua Zhang, et al. ∙

research

∙ 04/03/2023

Model-Agnostic Reachability Analysis on Deep Neural Networks

Verification plays an essential role in the formal analysis of safety-cr...

0 Chi Zhang, et al. ∙

research

∙ 03/19/2023

Randomized Adversarial Training via Taylor Expansion

In recent years, there has been an explosion of research into developing...

0 Gaojie Jin, et al. ∙

research

∙ 01/29/2023

Towards Verifying the Geometric Robustness of Large-scale Neural Networks

Deep neural networks (DNNs) are known to be vulnerable to adversarial ge...

0 Fu Wang, et al. ∙

research

∙ 01/23/2023

Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff

Spiking neural networks (SNNs), a variant of artificial neural networks ...

0 Dengyu Wu, et al. ∙

research

∙ 10/31/2022

Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks

Heterogeneous graph neural network has unleashed great potential on grap...

0 Tiehua Zhang, et al. ∙

research

∙ 09/29/2022

Exploring Cross-Point Embeddings for 3D Dense Uncertainty Estimation

Dense prediction tasks are common for 3D point clouds, but the inherent ...

0 Kaiwen Cai, et al. ∙

research

∙ 08/01/2022

Short-term Load Forecasting with Distributed Long Short-Term Memory

With the employment of smart meters, massive data on consumer behaviour ...

0 Yi Dong, et al. ∙

research

∙ 06/21/2022

A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems

Hazard and Operability Analysis (HAZOP) is a powerful safety analysis te...

0 Yi Qi, et al. ∙

research

∙ 06/07/2022

FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning

Spatial-temporal data contains rich information and has been widely stud...

0 Tiehua Zhang, et al. ∙

research

∙ 05/17/2022

Hierarchical Distribution-Aware Testing of Deep Learning

With its growing use in safety/security-critical applications, Deep Lear...

20 Wei Huang, et al. ∙

research

∙ 03/11/2022

Enhancing Adversarial Training with Second-Order Statistics of Weights

Adversarial training has been shown to be one of the most effective appr...

10 Gaojie Jin, et al. ∙

research

∙ 03/09/2022

3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs

In this paper, we propose a novel multi-level aggregation network to reg...

0 Yanda Meng, et al. ∙

research

∙ 03/08/2022

Counting with Adaptive Auxiliary Learning

This paper proposes an adaptive auxiliary task learning based approach f...

8 Yanda Meng, et al. ∙

research

∙ 03/03/2022

STUN: Self-Teaching Uncertainty Estimation for Place Recognition

Place recognition is key to Simultaneous Localization and Mapping (SLAM)...

0 Kaiwen Cai, et al. ∙

research

∙ 01/23/2022

Weight Expansion: A New Perspective on Dropout and Generalization

While dropout is known to be a successful regularization technique, insi...

0 Gaojie Jin, et al. ∙

research

∙ 01/22/2022

Neuronal Correlation: a Central Concept in Neural Network

This paper proposes to study neural networks through neuronal correlatio...

0 Gaojie Jin, et al. ∙

research

∙ 12/29/2021

GPS: A Policy-driven Sampling Approach for Graph Representation Learning

Graph representation learning has drawn increasing attention in recent y...

0 Tiehua Zhang, et al. ∙

research

∙ 11/30/2021

Reliability Assessment and Safety Arguments for Machine Learning Components in Assuring Learning-Enabled Autonomous Systems

The increasing use of Machine Learning (ML) components embedded in auton...

3 Xingyu Zhao, et al. ∙

research

∙ 10/27/2021

BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation

Segmentation is an essential operation of image processing. The convolut...

8 Yanda Meng, et al. ∙

research

∙ 09/14/2021

Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems

While Deep Reinforcement Learning (DRL) provides transformational capabi...

0 Yi Dong, et al. ∙

research

∙ 08/24/2021

Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications

This tutorial aims to introduce the fundamentals of adversarial robustne...

0 Wenjie Ruan, et al. ∙

research

∙ 08/03/2021

Tutorials on Testing Neural Networks

Deep learning achieves remarkable performance on pattern recognition, bu...

0 Nicolas Berthier, et al. ∙

research

∙ 07/28/2021

Spatial Uncertainty-Aware Semi-Supervised Crowd Counting

Semi-supervised approaches for crowd counting attract attention, as the ...

0 Yanda Meng, et al. ∙

research

∙ 06/02/2021

Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles

The utilisation of Deep Learning (DL) is advancing into increasingly mor...

24 Xingyu Zhao, et al. ∙

research

∙ 04/29/2021

Learning Robust Variational Information Bottleneck with Reference

We propose a new approach to train a variational information bottleneck ...

0 Weizhu Qian, et al. ∙

research

∙ 04/13/2021

Detecting Operational Adversarial Examples for Reliable Deep Learning

The utilisation of Deep Learning (DL) raises new challenges regarding it...

0 Xingyu Zhao, et al. ∙

research

∙ 03/05/2021

Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features

Intensive research has been conducted on the verification and validation...

0 Nicolas Berthier, et al. ∙

research

∙ 03/01/2021

A Little Energy Goes a Long Way: Energy-Efficient, Accurate Conversion from Convolutional Neural Networks to Spiking Neural Networks

Spiking neural networks (SNNs) offer an inherent ability to process spat...

0 Dengyu Wu, et al. ∙

research

∙ 12/05/2020

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations

A key impediment to the use of AI is the lacking of transparency, especi...

0 Xingyu Zhao, et al. ∙

research

∙ 10/16/2020

Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems for Robotics

This paper presents a formal verification guided approach for a principl...

0 Wei Huang, et al. ∙

research

∙ 10/16/2020

Embedding and Synthesis of Knowledge in Tree Ensemble Classifiers

This paper studies the embedding and synthesis of knowledge in tree ense...

24 Wei Huang, et al. ∙

research

∙ 10/15/2020

Generalizing Universal Adversarial Attacks Beyond Additive Perturbations

The previous study has shown that universal adversarial attacks can fool...

6 Yanghao Zhang, et al. ∙

research

∙ 10/12/2020

How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks

This paper studies the novel concept of weight correlation in deep neura...

1 Gaojie Jin, et al. ∙

research

∙ 09/13/2020

Towards the Quantification of Safety Risks in Deep Neural Networks

Safety concerns on the deep neural networks (DNNs) have been raised when...

0 Peipei Xu, et al. ∙

research

∙ 09/10/2020

CasGCN: Predicting future cascade growth based on information diffusion graph

Sudden bursts of information cascades can lead to unexpected consequence...

0 Zhixuan Xu, et al. ∙

research

∙ 07/23/2020

Adaptable and Verifiable BDI Reasoning

Long-term autonomy requires autonomous systems to adapt as their capabil...

0 Peter Stringer, et al. ∙

research

∙ 07/10/2020

Generating Adversarial Inputs Using A Black-box Differential Technique

Neural Networks (NNs) are known to be vulnerable to adversarial attacks....

0 João Batista Pereira Matos Juúnior, et al. ∙

research

∙ 03/07/2020

A Safety Framework for Critical Systems Utilising Deep Neural Networks

Increasingly sophisticated mathematical modelling processes from Machine...

0 Xingyu Zhao, et al. ∙

research

∙ 02/06/2020

Reliability Validation of Learning Enabled Vehicle Tracking

This paper studies the reliability of a real-world learning-enabled syst...

0 Youcheng Sun, et al. ∙

research

∙ 11/05/2019

Test Metrics for Recurrent Neural Networks

Recurrent neural networks (RNNs) have been applied to a broad range of a...

15 Wei Huang, et al. ∙

research

∙ 08/22/2019

Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management

The battery is a key component of autonomous robots. Its performance lim...

0 Xingyu Zhao, et al. ∙

research

∙ 08/06/2019

Explaining Deep Neural Networks Using Spectrum-Based Fault Localization

Deep neural networks (DNNs) increasingly replace traditionally developed...

8 Youcheng Sun, et al. ∙

Xiaowei Huang

Featured Co-authors