{"total":93,"rows":[{"id":"2305.11391","title":"A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation","created":"2023-05-19","cited_by_count":93,"area":null},{"id":"2402.01822","title":"Building Guardrails for Large Language Models","created":"2024-02-02","cited_by_count":65,"area":"safety"},{"id":"2105.03640","title":"On Guaranteed Optimal Robust Explanations for NLP Models","created":"2021-05-08","cited_by_count":54,"area":null},{"id":"2205.00403","title":"A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness","created":"2022-05-01","cited_by_count":49,"area":null},{"id":"2206.12227","title":"Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective","created":"2022-06-24","cited_by_count":42,"area":null},{"id":"2405.03658","title":"A review on data-driven constitutive laws for solids","created":"2024-05-06","cited_by_count":35,"area":null},{"id":"2104.02466","title":"A Review of Formal Methods applied to Machine Learning","created":"2021-04-06","cited_by_count":33,"area":null},{"id":"2108.13179","title":"Reachability Is NP-Complete Even for the Simplest Neural Networks","created":"2021-08-30","cited_by_count":27,"area":null},{"id":"2105.12841","title":"DNNV: A Framework for Deep Neural Network Verification","created":"2021-05-26","cited_by_count":23,"area":null},{"id":"2311.11871","title":"Training robust and generalizable quantum models","created":"2023-11-20","cited_by_count":21,"area":null},{"id":"2207.02036","title":"PRoA: A Probabilistic Robustness Assessment against Functional Perturbations","created":"2022-07-05","cited_by_count":19,"area":null},{"id":"2202.03898","title":"Verification-Aided Deep Ensemble Selection","created":"2022-02-08","cited_by_count":18,"area":null},{"id":"2111.07505","title":"A Survey on AI Assurance","created":"2021-11-15","cited_by_count":16,"area":null},{"id":"2103.07633","title":"Attack as Defense: Characterizing Adversarial Examples using Robustness","created":"2021-03-13","cited_by_count":14,"area":null},{"id":"2110.09929","title":"Minimal Multi-Layer Modifications of Deep Neural Networks","created":"2021-10-18","cited_by_count":14,"area":null},{"id":"2111.13557","title":"On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments","created":"2021-11-26","cited_by_count":14,"area":null},{"id":"2305.03686","title":"Provable Preimage Under-Approximation for Neural Networks (Full Version)","created":"2023-05-05","cited_by_count":14,"area":null},{"id":"2306.03048","title":"From Robustness to Explainability and Back Again","created":"2023-06-05","cited_by_count":14,"area":null},{"id":"2111.03165","title":"Infinite Time Horizon Safety of Bayesian Neural Networks","created":"2021-11-04","cited_by_count":13,"area":null},{"id":"2207.06531","title":"Reachability Analysis of a General Class of Neural Ordinary Differential Equations","created":"2022-07-13","cited_by_count":13,"area":null},{"id":"2101.10102","title":"Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning","created":"2021-01-25","cited_by_count":12,"area":null},{"id":"2304.01218","title":"POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems","created":"2023-03-31","cited_by_count":12,"area":null},{"id":"2111.05468","title":"Sparse Adversarial Video Attacks with Spatial Transformations","created":"2021-11-10","cited_by_count":11,"area":null},{"id":"2112.07324","title":"On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training","created":"2021-12-14","cited_by_count":11,"area":null},{"id":"2112.12490","title":"Curriculum Learning for Safe Mapless Navigation","created":"2021-12-23","cited_by_count":11,"area":null},{"id":"2301.12456","title":"Towards Verifying the Geometric Robustness of Large-scale Neural Networks","created":"2023-01-29","cited_by_count":11,"area":null},{"id":"2302.05745","title":"Verifying Generalization in Deep Learning","created":"2023-02-11","cited_by_count":11,"area":null},{"id":"2401.03188","title":"A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence","created":"2024-01-06","cited_by_count":10,"area":null},{"id":"2402.15429","title":"ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation","created":"2024-02-23","cited_by_count":10,"area":"diffusion"},{"id":"2603.16952","title":"Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies","created":"2026-03-16","cited_by_count":9,"area":null},{"id":"2103.05746","title":"Analyzing Human Models that Adapt Online","created":"2021-03-09","cited_by_count":8,"area":null},{"id":"2110.15764","title":"{\\epsilon}-weakened Robustness of Deep Neural Networks","created":"2021-10-29","cited_by_count":8,"area":null},{"id":"2205.08589","title":"Hierarchical Distribution-Aware Testing of Deep Learning","created":"2022-05-17","cited_by_count":8,"area":null},{"id":"2208.09872","title":"Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks","created":"2022-08-21","cited_by_count":8,"area":null},{"id":"2301.12100","title":"Reachability Analysis of Neural Network Control Systems","created":"2023-01-28","cited_by_count":8,"area":null},{"id":"2408.09262","title":"PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks","created":"2024-08-17","cited_by_count":8,"area":null},{"id":"2102.01904","title":"A Scalable Two Stage Approach to Computing Optimal Decision Sets","created":"2021-02-03","cited_by_count":7,"area":null},{"id":"2212.11746","title":"Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning","created":"2022-12-22","cited_by_count":7,"area":null},{"id":"2312.06436","title":"Reward Certification for Policy Smoothed Reinforcement Learning","created":"2023-12-11","cited_by_count":7,"area":null},{"id":"2101.00989","title":"Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks","created":"2021-01-04","cited_by_count":6,"area":null},{"id":"2104.14435","title":"Customizable Reference Runtime Monitoring of Neural Networks using Resolution Boxes","created":"2021-04-25","cited_by_count":6,"area":null},{"id":"2210.08339","title":"Reachable Polyhedral Marching (RPM): An Exact Analysis Tool for Deep-Learned Control Systems","created":"2022-10-15","cited_by_count":6,"area":null},{"id":"2307.11784","title":"What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems","created":"2023-07-20","cited_by_count":6,"area":null},{"id":"2404.01769","title":"Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systems","created":"2024-04-02","cited_by_count":6,"area":"deeprl"},{"id":"2409.18214","title":"Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey","created":"2024-09-26","cited_by_count":6,"area":"diffusion"},{"id":"2410.14219","title":"Formal Explanations for Neuro-Symbolic AI","created":"2024-10-18","cited_by_count":6,"area":null},{"id":"2502.14833","title":"Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide","created":"2025-02-20","cited_by_count":6,"area":null},{"id":"2104.03180","title":"Adversarial Robustness Guarantees for Gaussian Processes","created":"2021-04-07","cited_by_count":5,"area":null},{"id":"2108.00893","title":"Static analysis of ReLU neural networks with tropical polyhedra","created":"2021-07-30","cited_by_count":5,"area":null},{"id":"2108.10451","title":"Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications","created":"2021-08-24","cited_by_count":5,"area":null},{"id":"2203.07941","title":"Reachability In Simple Neural Networks","created":"2022-03-15","cited_by_count":5,"area":null},{"id":"2205.00210","title":"Software Testing for Machine Learning","created":"2022-04-30","cited_by_count":5,"area":null},{"id":"2205.06992","title":"Verifying Neural Networks Against Backdoor Attacks","created":"2022-05-14","cited_by_count":5,"area":null},{"id":"2212.05337","title":"Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking","created":"2022-12-10","cited_by_count":5,"area":null},{"id":"2107.05754","title":"EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks","created":"2021-07-12","cited_by_count":4,"area":null},{"id":"2405.17556","title":"Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound","created":"2024-05-27","cited_by_count":4,"area":null},{"id":"2102.06585","title":"A Computability Perspective on (Verified) Machine Learning","created":"2021-02-12","cited_by_count":3,"area":null},{"id":"2109.06523","title":"Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking","created":"2021-09-14","cited_by_count":3,"area":null},{"id":"2210.14991","title":"Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems","created":"2022-10-26","cited_by_count":3,"area":null},{"id":"2404.08285","title":"A Survey of Neural Network Robustness Assessment in Image Recognition","created":"2024-04-12","cited_by_count":3,"area":null},{"id":"2410.07917","title":"Understanding Human Activity with Uncertainty Measure for Novelty in Graph Convolutional Networks","created":"2024-10-10","cited_by_count":3,"area":null},{"id":"2411.01644","title":"Achieving Domain-Independent Certified Robustness via Knowledge Continuity","created":"2024-11-03","cited_by_count":3,"area":null},{"id":"2103.03076","title":"Dynamic Efficient Adversarial Training Guided by Gradient Magnitude","created":"2021-03-04","cited_by_count":2,"area":null},{"id":"2106.02867","title":"Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness","created":"2021-06-05","cited_by_count":2,"area":null},{"id":"2201.03413","title":"Systems Challenges for Trustworthy Embodied Systems","created":"2022-01-10","cited_by_count":2,"area":null},{"id":"2302.06455","title":"Incremental Satisfiability Modulo Theory for Verification of Deep Neural Networks","created":"2023-02-10","cited_by_count":2,"area":null},{"id":"2305.07618","title":"Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz","created":"2023-05-12","cited_by_count":2,"area":null},{"id":"2406.02024","title":"Verifying the Generalization of Deep Learning to Out-of-Distribution Domains","created":"2024-06-04","cited_by_count":2,"area":null},{"id":"2501.14009","title":"Scalable and Interpretable Verification of Image-based Neural Network Controllers for Autonomous Vehicles","created":"2025-01-23","cited_by_count":2,"area":null},{"id":"2103.03704","title":"Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features","created":"2021-03-05","cited_by_count":1,"area":null},{"id":"2106.00512","title":"The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning","created":"2021-06-01","cited_by_count":1,"area":null},{"id":"2205.05793","title":"Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits","created":"2022-05-11","cited_by_count":1,"area":null},{"id":"2208.07464","title":"An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models","created":"2022-08-15","cited_by_count":1,"area":null},{"id":"2304.00813","title":"Model-Agnostic Reachability Analysis on Deep Neural Networks","created":"2023-04-03","cited_by_count":1,"area":null},{"id":"2505.09640","title":"Feature Relevancy, Necessity and Usefulness: Complexity and Algorithms","created":"2025-05-06","cited_by_count":1,"area":null},{"id":"2510.05261","title":"ECLipsE-Gen-Local: Efficient Compositional Local Lipschitz Estimates for Deep Neural Networks","created":"2025-10-06","cited_by_count":1,"area":null},{"id":"2106.11881","title":"Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe","created":"2021-06-22","cited_by_count":0,"area":null},{"id":"2201.10436","title":"Safe AI -- How is this Possible?","created":"2022-01-25","cited_by_count":0,"area":null},{"id":"2207.07232","title":"Lipschitz Bound Analysis of Neural Networks","created":"2022-07-14","cited_by_count":0,"area":null},{"id":"2302.13844","title":"Safe Multi-agent Learning via Trapping Regions","created":"2023-02-27","cited_by_count":0,"area":null},{"id":"2306.02639","title":"Evaluating robustness of support vector machines with the Lagrangian dual approach","created":"2023-06-05","cited_by_count":0,"area":null},{"id":"2306.12495","title":"Verifying Global Neural Network Specifications using Hyperproperties","created":"2023-06-21","cited_by_count":0,"area":null},{"id":"2311.10780","title":"Extending Neural Network Verification to a Larger Family of Piece-wise Linear Activation Functions","created":"2023-11-16","cited_by_count":0,"area":null},{"id":"2311.10863","title":"Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks","created":"2023-11-17","cited_by_count":0,"area":null},{"id":"2312.09695","title":"Robustness Verification of Deep Reinforcement Learning Based Control Systems using Reward Martingales","created":"2023-12-15","cited_by_count":0,"area":null},{"id":"2403.13441","title":"Robustness Verifcation in Neural Networks","created":"2024-03-20","cited_by_count":0,"area":null},{"id":"2404.01642","title":"Patch Synthesis for Property Repair of Deep Neural Networks","created":"2024-04-02","cited_by_count":0,"area":null},{"id":"2409.13770","title":"A constrained optimization approach to improve robustness of neural networks","created":"2024-09-18","cited_by_count":0,"area":null},{"id":"2411.14374","title":"Using Formal Models, Safety Shields and Certified Control to Validate AI-Based Train Systems","created":"2024-11-21","cited_by_count":0,"area":null},{"id":"2505.11546","title":"Control Invariant Sets for Neural Network Dynamical Systems and Recursive Feasibility in Model Predictive Control","created":"2025-05-15","cited_by_count":0,"area":null},{"id":"2510.05707","title":"Stable Robot Motions on Manifolds: Learning Lyapunov-Constrained Neural Manifold ODEs","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2605.23203","title":"Lipschitz Optimization for Formal Verification of Homographies","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2605.29537","title":"The Complexity of Verifying Feedforward Neural Networks in Quantised Settings","created":"2026-05-28","cited_by_count":0,"area":null}],"next_cursor":null}