173 citations · 854 across the 53 of their papers we have counts for
55 papers
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
Yuze Liu, Ziming Zhao, Tiehua Zhang +5
Sleep stage classification is crucial for detecting patients' health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data…
Symplectic Structure-Aware Hamiltonian (Graph) Embeddings
Jiaxu Liu, Xinping Yi, Tianle Zhang +1
In traditional Graph Neural Networks (GNNs), the assumption of a fixed embedding manifold often limits their adaptability to diverse graph geometries. Recently, Hamiltonian system-…
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems
Saddek Bensalem, Chih-Hong Cheng, Wei Huang +3
Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges. Among the challenges, it…
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
Xiaowei Huang, Wenjie Ruan, Wei Huang +14
Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many…
Model-Agnostic Reachability Analysis on Deep Neural Networks
Chi Zhang, Wenjie Ruan, Fu Wang +3
Verification plays an essential role in the formal analysis of safety-critical systems. Most current verification methods have specific requirements when working on Deep Neural Net…
Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT
Yi Qi, Xingyu Zhao, Siddartha Khastgir +1
Can safety analysis make use of Large Language Models (LLMs)? A case study explores Systems Theoretic Process Analysis (STPA) applied to Automatic Emergency Brake (AEB) and Electri…
Randomized Adversarial Training via Taylor Expansion
Gaojie Jin, Xinping Yi, Dengyu Wu +2
In recent years, there has been an explosion of research into developing more robust deep neural networks against adversarial examples. Adversarial training appears as one of the m…
Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation
Yi Dong, Zhongguo Li, Xingyu Zhao +2
Multi-robot cooperative control has gained extensive research interest due to its wide applications in civil, security, and military domains. This paper proposes a cooperative cont…
STPA for Learning-Enabled Systems: A Survey and A New Practice
Yi Qi, Yi Dong, Siddartha Khastgir +3
Systems Theoretic Process Analysis (STPA) is a systematic approach for hazard analysis that has been used across many industrial sectors including transportation, energy, and defen…
Towards Verifying the Geometric Robustness of Large-scale Neural Networks
Fu Wang, Peipei Xu, Wenjie Ruan +1
Deep neural networks (DNNs) are known to be vulnerable to adversarial geometric transformation. This paper aims to verify the robustness of large-scale DNNs against the combination…
Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
Dengyu Wu, Gaojie Jin, Han Yu +2
Spiking neural network (SNN), as the next generation of artificial neural network (ANN), offer a closer mimicry of natural neural networks and hold promise for significant improvem…
Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems
Yi Dong, Xingyu Zhao, Sen Wang +1
Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RAS). A key challenge to its deployment in real-life operations is the pre…
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Wei Huang, Xingyu Zhao, Gaojie Jin +1
Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistingui…
Short-term Load Forecasting with Distributed Long Short-Term Memory
Yi Dong, Yang Chen, Xingyu Zhao +1
With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile infor…
A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems
Yi Qi, Philippa Ryan Conmy, Wei Huang +2
Hazard and Operability Analysis (HAZOP) is a powerful safety analysis technique with a long history in industrial process control domain. With the increasing use of Machine Learnin…
Hierarchical Distribution-Aware Testing of Deep Learning
Wei Huang, Xingyu Zhao, Alec Banks +2
Deep Learning (DL) is increasingly used in safety-critical applications, raising concerns about its reliability. DL suffers from a well-known problem of lacking robustness, especia…
Enhancing Adversarial Training with Second-Order Statistics of Weights
Gaojie Jin, Xinping Yi, Wei Huang +2
Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over mo…
STUN: Self-Teaching Uncertainty Estimation for Place Recognition
Kaiwen Cai, Chris Xiaoxuan Lu, Xiaowei Huang
Place recognition is key to Simultaneous Localization and Mapping (SLAM) and spatial perception. However, a place recognition in the wild often suffers from erroneous predictions d…
Neuronal Correlation: a Central Concept in Neural Network
Gaojie Jin, Xinping Yi, Xiaowei Huang
This paper proposes to study neural networks through neuronal correlation, a statistical measure of correlated neuronal activity on the penultimate layer. We show that neuronal cor…
GPS: A Policy-driven Sampling Approach for Graph Representation Learning
Tiehua Zhang, Yuze Liu, Xin Chen +3
Graph representation learning has drawn increasing attention in recent years, especially for learning the low dimensional embedding at both node and graph level for classification…
Weight Expansion: A New Perspective on Dropout and Generalization
Gaojie Jin, Xinping Yi, Pengfei Yang +3
While dropout is known to be a successful regularization technique, insights into the mechanisms that lead to this success are still lacking. We introduce the concept of \emph{weig…
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
Yi Dong, Wei Huang, Vibhav Bharti +6
The increasing use of Machine Learning (ML) components embedded in autonomous systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the pressing need to assure th…
BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
Yanda Meng, Hongrun Zhang, Dongxu Gao +5
Segmentation is an essential operation of image processing. The convolution operation suffers from a limited receptive field, while global modelling is fundamental to segmentation…
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
Yi Dong, Xingyu Zhao, Xiaowei Huang
While Deep Reinforcement Learning (DRL) provides transformational capabilities to the control of Robotics and Autonomous Systems (RAS), the black-box nature of DRL and uncertain de…