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Found 33 papers, 14 papers with code
Formal Specification, Assessment, and Enforcement of Fairness for Generative AIs
The risk of reinforcing or exacerbating societal biases and inequalities is growing as generative AI increasingly produces content that resembles human output, from text to images and beyond.
ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation
Text-to-Image (T2I) Diffusion Models (DMs) have shown impressive abilities in generating high-quality images based on simple text descriptions.
Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration
Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collection.
Building Guardrails for Large Language Models
As Large Language Models (LLMs) become more integrated into our daily lives, it is crucial to identify and mitigate their risks, especially when the risks can have profound impacts on human users and societies.
ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning
Then, to mitigate the vulnerability of existing GCRL algorithms, we introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer to improve the adversarial robustness of the underlying RL agent against various types of perturbations.
SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
Furthermore, the boundary loss capitalizes on the distinctive features of SGB by directing the model's attention to the boundary information of the object.
Scalable Label Distribution Learning for Multi-Label Classification
Most existing MLC methods are based on the assumption that the correlation of two labels in each label pair is symmetric, which is violated in many real-world scenarios.
Deep-learning-based decomposition of overlapping-sparse images: application at the vertex of neutrino interactions
Image decomposition plays a crucial role in various computer vision tasks, enabling the analysis and manipulation of visual content at a fundamental level.
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems
Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges.
Risk Controlled Image Retrieval
Most image retrieval research focuses on improving predictive performance, ignoring scenarios where the reliability of the prediction is also crucial.
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
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 knowledge domains.
Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT
Can safety analysis make use of Large Language Models (LLMs)?
Bayesian Learning for the Robust Verification of Autonomous Robots
Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments.
Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation
Then, based on the distributed optimisation algorithm, an output regulation method is utilised to solve the optimal coordination problem for general linear dynamic systems.
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing.
Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field
In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators.
Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems
Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RAS).
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness.
Short-term Load Forecasting with Distributed Long Short-Term Memory
With the employment of smart meters, massive data on consumer behaviour can be collected by retailers.
Hierarchical Distribution-Aware Testing of Deep Learning
In this paper, we propose a new robustness testing approach for detecting AEs that considers both the feature level distribution and the pixel level distribution, capturing the perceptual quality of adversarial perturbations.
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
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 their functional safety.
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
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 deployment environments of RAS pose new challenges on its dependability.
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications.
Detecting Operational Adversarial Examples for Reliable Deep Learning
The utilisation of Deep Learning (DL) raises new challenges regarding its dependability in critical applications.
Joint Active and Passive Beamforming for Intelligent Reflecting Surface Aided Multiuser MIMO Communications
This letter investigates the joint active and passive beamforming optimization for intelligent reflecting surface (IRS) aided multiuser multiple-input multiple-output systems with the objective of maximizing the weighted sum-rate.
Information Theory Information Theory
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research.
Embedding and Extraction of Knowledge in Tree Ensemble Classifiers
Whilst, as the increasing use of machine learning models in security-critical applications, the embedding and extraction of malicious knowledge are equivalent to the notorious backdoor attack and its defence, respectively.
Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles
Context: Demonstrating high reliability and safety for safety-critical systems (SCSs) remains a hard problem.
A Safety Framework for Critical Systems Utilising Deep Neural Networks
Increasingly sophisticated mathematical modelling processes from Machine Learning are being used to analyse complex data.
Coverage Guided Testing for Recurrent Neural Networks
The test metrics and test case generation algorithm are implemented into a tool TestRNN, which is then evaluated on a set of LSTM benchmarks.
Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management
The battery is a key component of autonomous robots.
Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing
There is an urgent societal need to assess whether autonomous vehicles (AVs) are safe enough.
Probabilistic Model Checking of Robots Deployed in Extreme Environments
Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans.
