173 citations · 854 across the 53 of their papers we have counts for
29 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications
Wenjie Ruan, Xinping Yi, Xiaowei Huang
This tutorial aims to introduce the fundamentals of adversarial robustness of deep learning, presenting a well-structured review of up-to-date techniques to assess the vulnerabilit…
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
Xingyu Zhao, Wei Huang, Alec Banks +4
The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications. While it shows great potential to provide transformational capabilities, DL al…
Learning Robust Variational Information Bottleneck with Reference
Weizhu Qian, Bowei Chen, Xiaowei Huang
We propose a new approach to train a variational information bottleneck (VIB) that improves its robustness to adversarial perturbations. Unlike the traditional methods where the ha…
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features
Nicolas Berthier, Amany Alshareef, James Sharp +2
Intensive research has been conducted on the verification and validation of deep neural networks (DNNs), aiming to understand if, and how, DNNs can be applied to safety critical ap…
How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks
Gaojie Jin, Xinping Yi, Liang Zhang +3
This paper studies the novel concept of weight correlation in deep neural networks and discusses its impact on the networks' generalisation ability. For fully-connected layers, the…
Towards the Quantification of Safety Risks in Deep Neural Networks
Peipei Xu, Wenjie Ruan, Xiaowei Huang
Safety concerns on the deep neural networks (DNNs) have been raised when they are applied to critical sectors. In this paper, we define safety risks by requesting the alignment of…
Generating Adversarial Inputs Using A Black-box Differential Technique
João Batista Pereira Matos Juúnior, Lucas Carvalho Cordeiro, Marcelo d'Amorim +1
Neural Networks (NNs) are known to be vulnerable to adversarial attacks. A malicious agent initiates these attacks by perturbing an input into another one such that the two inputs…
A Safety Framework for Critical Systems Utilising Deep Neural Networks
Xingyu Zhao, Alec Banks, James Sharp +4
Increasingly sophisticated mathematical modelling processes from Machine Learning are being used to analyse complex data. However, the performance and explainability of these model…
Coverage Guided Testing for Recurrent Neural Networks
Wei Huang, Youcheng Sun, Xingyu Zhao +4
Recurrent neural networks (RNNs) have been applied to a broad range of applications, including natural language processing, drug discovery, and video recognition. Their vulnerabili…
Explaining Image Classifiers using Statistical Fault Localization
Youcheng Sun, Hana Chockler, Xiaowei Huang +1
The black-box nature of deep neural networks (DNNs) makes it impossible to understand why a particular output is produced, creating demand for "Explainable AI". In this paper, we s…
Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification
Jianlin Li, Pengfei Yang, Jiangchao Liu +3
Deep neural networks (DNNs) have been shown lack of robustness for the vulnerability of their classification to small perturbations on the inputs. This has led to safety concerns o…
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang, Daniel Kroening, Wenjie Ruan +5
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deplo…