
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems
While Deep Reinforcement Learning (DRL) provides transformational capabi...
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Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications
This tutorial aims to introduce the fundamentals of adversarial robustne...
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Tutorials on Testing Neural Networks
Deep learning achieves remarkable performance on pattern recognition, bu...
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Spatial UncertaintyAware SemiSupervised Crowd Counting
Semisupervised approaches for crowd counting attract attention, as the ...
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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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Learning Robust Variational Information Bottleneck with Reference
We propose a new approach to train a variational information bottleneck ...
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Detecting Operational Adversarial Examples for Reliable Deep Learning
The utilisation of Deep Learning (DL) raises new challenges regarding it...
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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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A Little Energy Goes a Long Way: EnergyEfficient, 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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BayLIME: Bayesian Local Interpretable ModelAgnostic Explanations
A key impediment to the use of AI is the lacking of transparency, especi...
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Formal Verification of Robustness and Resilience of LearningEnabled State Estimation Systems for Robotics
This paper presents a formal verification guided approach for a principl...
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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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Generalizing Universal Adversarial Attacks Beyond Additive Perturbations
The previous study has shown that universal adversarial attacks can fool...
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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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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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CasGCN: Predicting future cascade growth based on information diffusion graph
Sudden bursts of information cascades can lead to unexpected consequence...
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Adaptable and Verifiable BDI Reasoning
Longterm autonomy requires autonomous systems to adapt as their capabil...
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Generating Adversarial Inputs Using A Blackbox Differential Technique
Neural Networks (NNs) are known to be vulnerable to adversarial attacks....
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A Safety Framework for Critical Systems Utilising Deep Neural Networks
Increasingly sophisticated mathematical modelling processes from Machine...
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Reliability Validation of Learning Enabled Vehicle Tracking
This paper studies the reliability of a realworld learningenabled syst...
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Test Metrics for Recurrent Neural Networks
Recurrent neural networks (RNNs) have been applied to a broad range of a...
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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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Explaining Deep Neural Networks Using SpectrumBased Fault Localization
Deep neural networks (DNNs) increasingly replace traditionally developed...
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testRNN: Coverageguided Testing on Recurrent Neural Networks
Recurrent neural networks (RNNs) have been widely applied to various seq...
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Reasoning about Cognitive Trust in Stochastic Multiagent Systems
We consider the setting of stochastic multiagent systems modelled as sto...
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Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification
Deep neural networks (DNNs) have been shown lack of robustness for the v...
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Safety and Trustworthiness of Deep Neural Networks: A Survey
In the past few years, significant progress has been made on deep neural...
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A GameBased Approximate Verification of Deep Neural Networks with Provable Guarantees
Despite the improved accuracy of deep neural networks, the discovery of ...
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Reachability Analysis of Deep Neural Networks with Provable Guarantees
Verifying correctness of deep neural networks (DNNs) is challenging. We ...
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Concolic Testing for Deep Neural Networks
Concolic testing alternates between CONCrete program execution and symbO...
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Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for L0 Norm
Deployment of deep neural networks (DNNs) in safety or securitycritical...
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Testing Deep Neural Networks
Deep neural networks (DNNs) have a wide range of applications, and softw...
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FeatureGuided BlackBox Safety Testing of Deep Neural Networks
Despite the improved accuracy of deep neural networks, the discovery of ...
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Safety Verification of Deep Neural Networks
Deep neural networks have achieved impressive experimental results in im...
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Xiaowei Huang
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