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09/09/2023
Symplectic Structure-Aware Hamiltonian (Graph) Embeddings
In traditional Graph Neural Networks (GNNs), the assumption of a fixed e...
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09/05/2023
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
Sleep stage classification is crucial for detecting patients' health con...
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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...
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08/05/2023
A Symbolic Character-Aware Model for Solving Geometry Problems
AI has made significant progress in solving math problems, but geometry ...
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07/07/2023
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Graph neural network (GNN) has gained increasing popularity in recent ye...
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06/21/2023
Quantum and classical query complexities for determining connectedness of matroids
Connectivity is a fundamental structural property of matroids, and has b...
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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...
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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...
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04/03/2023
Model-Agnostic Reachability Analysis on Deep Neural Networks
Verification plays an essential role in the formal analysis of safety-cr...
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03/19/2023
Randomized Adversarial Training via Taylor Expansion
In recent years, there has been an explosion of research into developing...
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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...
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01/23/2023
Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
Spiking neural networks (SNNs), a variant of artificial neural networks ...
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10/31/2022
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks
Heterogeneous graph neural network has unleashed great potential on grap...
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09/29/2022
Exploring Cross-Point Embeddings for 3D Dense Uncertainty Estimation
Dense prediction tasks are common for 3D point clouds, but the inherent ...
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08/01/2022
Short-term Load Forecasting with Distributed Long Short-Term Memory
With the employment of smart meters, massive data on consumer behaviour ...
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06/21/2022
A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems
Hazard and Operability Analysis (HAZOP) is a powerful safety analysis te...
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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...
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05/17/2022
Hierarchical Distribution-Aware Testing of Deep Learning
With its growing use in safety/security-critical applications, Deep Lear...
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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...
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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...
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03/08/2022
Counting with Adaptive Auxiliary Learning
This paper proposes an adaptive auxiliary task learning based approach f...
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03/03/2022
STUN: Self-Teaching Uncertainty Estimation for Place Recognition
Place recognition is key to Simultaneous Localization and Mapping (SLAM)...
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01/23/2022
Weight Expansion: A New Perspective on Dropout and Generalization
While dropout is known to be a successful regularization technique, insi...
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01/22/2022
Neuronal Correlation: a Central Concept in Neural Network
This paper proposes to study neural networks through neuronal correlatio...
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12/29/2021
GPS: A Policy-driven Sampling Approach for Graph Representation Learning
Graph representation learning has drawn increasing attention in recent y...
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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...
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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...
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09/14/2021
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems
While Deep Reinforcement Learning (DRL) provides transformational capabi...
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08/24/2021
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications
This tutorial aims to introduce the fundamentals of adversarial robustne...
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08/03/2021
Tutorials on Testing Neural Networks
Deep learning achieves remarkable performance on pattern recognition, bu...
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07/28/2021
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
Semi-supervised approaches for crowd counting attract attention, as the ...
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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...
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04/29/2021
Learning Robust Variational Information Bottleneck with Reference
We propose a new approach to train a variational information bottleneck ...
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04/13/2021
Detecting Operational Adversarial Examples for Reliable Deep Learning
The utilisation of Deep Learning (DL) raises new challenges regarding it...
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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...
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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...
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12/05/2020
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
A key impediment to the use of AI is the lacking of transparency, especi...
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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...
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10/16/2020
Embedding and Synthesis of Knowledge in Tree Ensemble Classifiers
This paper studies the embedding and synthesis of knowledge in tree ense...
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10/15/2020
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations
The previous study has shown that universal adversarial attacks can fool...
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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...
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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...
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09/10/2020
CasGCN: Predicting future cascade growth based on information diffusion graph
Sudden bursts of information cascades can lead to unexpected consequence...
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07/23/2020
Adaptable and Verifiable BDI Reasoning
Long-term autonomy requires autonomous systems to adapt as their capabil...
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07/10/2020
Generating Adversarial Inputs Using A Black-box Differential Technique
Neural Networks (NNs) are known to be vulnerable to adversarial attacks....
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03/07/2020
A Safety Framework for Critical Systems Utilising Deep Neural Networks
Increasingly sophisticated mathematical modelling processes from Machine...
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02/06/2020
Reliability Validation of Learning Enabled Vehicle Tracking
This paper studies the reliability of a real-world learning-enabled syst...
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11/05/2019
Test Metrics for Recurrent Neural Networks
Recurrent neural networks (RNNs) have been applied to a broad range of a...
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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...
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08/06/2019
