{"total":71,"rows":[{"id":"2501.00200","title":"Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes","created":"2024-12-31","cited_by_count":33,"area":null},{"id":"2406.02981","title":"Local vs. Global Interpretability: A Computational Complexity Perspective","created":"2024-06-05","cited_by_count":23,"area":null},{"id":"2506.08505","title":"Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations","created":"2025-06-10","cited_by_count":13,"area":null},{"id":"2406.04184","title":"Shield Synthesis for LTL Modulo Theories","created":"2024-06-06","cited_by_count":12,"area":null},{"id":"2501.05867","title":"Neural Network Verification is a Programming Language Challenge","created":"2025-01-10","cited_by_count":12,"area":null},{"id":"2502.03391","title":"Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons","created":"2025-02-05","cited_by_count":12,"area":null},{"id":"2408.03915","title":"Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation","created":"2024-08-07","cited_by_count":11,"area":null},{"id":"2512.11087","title":"Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification","created":"2025-12-11","cited_by_count":9,"area":null},{"id":"2510.21599","title":"SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism","created":"2025-10-24","cited_by_count":8,"area":null},{"id":"2405.10611","title":"A Certified Proof Checker for Deep Neural Network Verification in Imandra","created":"2024-05-17","cited_by_count":7,"area":null},{"id":"2401.14961","title":"Set-Based Training for Neural Network Verification","created":"2024-01-26","cited_by_count":6,"area":null},{"id":"2505.17854","title":"Out of the Shadows: Exploring a Latent Space for Neural Network Verification","created":"2025-05-23","cited_by_count":6,"area":null},{"id":"2412.03154","title":"SoundnessBench: A Soundness Benchmark for Neural Network Verifiers","created":"2024-12-04","cited_by_count":5,"area":null},{"id":"2506.09455","title":"Abstraction-Based Proof Production in Formal Verification of Neural Networks","created":"2025-06-11","cited_by_count":5,"area":null},{"id":"2407.01295","title":"Towards Formal Verification of Deep Neural Networks for Object Detection","created":"2024-07-01","cited_by_count":4,"area":null},{"id":"2408.00117","title":"Certifying Robustness of Learning-Based Keypoint Detection and Pose Estimation Methods","created":"2024-07-31","cited_by_count":4,"area":null},{"id":"2409.03060","title":"Efficiently Computing Compact Formal Explanations","created":"2024-09-04","cited_by_count":4,"area":null},{"id":"2410.20207","title":"Revisiting Differential Verification: Equivalence Verification with Confidence","created":"2024-10-26","cited_by_count":4,"area":null},{"id":"2505.19932","title":"Logic Gate Neural Networks are Good for Verification","created":"2025-05-26","cited_by_count":4,"area":null},{"id":"2511.08198","title":"Proof Minimization in Neural Network Verification","created":"2025-11-11","cited_by_count":4,"area":null},{"id":"2503.12083","title":"PICID: Proof-Driven Clause Learning in Neural Network Verification","created":"2025-03-15","cited_by_count":3,"area":null},{"id":"2509.11838","title":"Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network","created":"2025-09-15","cited_by_count":3,"area":null},{"id":"2510.21292","title":"Additive Models Explained: A Computational Complexity Approach","created":"2025-10-24","cited_by_count":3,"area":null},{"id":"2603.10661","title":"FAME: Formal Abstract Minimal Explanation for Neural Networks","created":"2026-03-11","cited_by_count":3,"area":null},{"id":"2404.03017","title":"Distributionally Robust Policy and Lyapunov-Certificate Learning","created":"2024-04-03","cited_by_count":2,"area":null},{"id":"2502.11672","title":"Exact Upper and Lower Bounds for the Output Distribution of Neural Networks with Random Inputs","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2507.05405","title":"Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verification","created":"2025-07-07","cited_by_count":2,"area":null},{"id":"2510.23389","title":"Floating-Point Neural Network Verification at the Software Level","created":"2025-10-27","cited_by_count":2,"area":null},{"id":"2602.18160","title":"Unifying Formal Explanations: A Complexity-Theoretic Perspective","created":"2026-02-20","cited_by_count":2,"area":null},{"id":"2603.12232","title":"Incremental Neural Network Verification via Learned Conflicts","created":"2026-03-12","cited_by_count":2,"area":null},{"id":"2406.14265","title":"VeriFlow: Modeling Distributions for Neural Network Verification","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2504.12031","title":"Proof-Carrying Neuro-Symbolic Code","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2505.00466","title":"A General Framework for Property-Driven Machine Learning","created":"2025-05-01","cited_by_count":1,"area":null},{"id":"2507.23197","title":"Solution-aware vs global ReLU selection: partial MILP strikes back for DNN verification","created":"2025-07-31","cited_by_count":1,"area":null},{"id":"2508.15454","title":"Mini-Batch Robustness Verification of Deep Neural Networks","created":"2025-08-21","cited_by_count":1,"area":null},{"id":"2601.19337","title":"SETA: Statistical Fault Attribution for Compound AI Systems","created":"2026-01-27","cited_by_count":1,"area":null},{"id":"2602.17530","title":"Provably Explaining Neural Additive Models","created":"2026-02-19","cited_by_count":1,"area":null},{"id":"2603.03043","title":"IoUCert: Robustness Verification for Anchor-based Object Detectors","created":"2026-03-03","cited_by_count":1,"area":null},{"id":"2605.07451","title":"VNN-LIB 2.0: Rigorous Foundations for Neural Network Verification","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2607.17290","title":"Lookahead Branching for Neural Network Verification","created":"2026-07-19","cited_by_count":1,"area":null},{"id":"2609.34457","title":"ZonoGPT: Towards An Abstract Domain for Verifying Large GPT Models","created":"2026-09-28","cited_by_count":1,"area":null},{"id":"2402.04892","title":"Probabilistic ML Verification via Weighted Model Integration","created":"2024-02-07","cited_by_count":0,"area":null},{"id":"2504.03650","title":"BoxRL-NNV: Boxed Refinement of Latin Hypercube Samples for Neural Network Verification","created":"2025-02-25","cited_by_count":0,"area":null},{"id":"2505.14001","title":"VeRecycle: Reclaiming Guarantees from Probabilistic Certificates for Stochastic Dynamical Systems after Change","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2506.12084","title":"The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verification","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2507.02916","title":"Efficient Certified Reasoning for Binarized Neural Networks","created":"2025-06-25","cited_by_count":0,"area":null},{"id":"2507.17453","title":"Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees","created":"2025-07-23","cited_by_count":0,"area":null},{"id":"2507.22760","title":"Of Good Demons and Bad Angels: Guaranteeing Safe Control under Finite Precision","created":"2025-07-30","cited_by_count":0,"area":null},{"id":"2509.03948","title":"Formal Verification of Local Robustness of a Classification Algorithm for a Spatial Use Case","created":"2025-09-04","cited_by_count":0,"area":null},{"id":"2511.06495","title":"Probably Approximately Global Robustness Certification","created":"2025-11-09","cited_by_count":0,"area":null},{"id":"2511.07293","title":"Formal Reasoning About Confidence and Automated Verification of Neural Networks","created":"2025-11-10","cited_by_count":0,"area":null},{"id":"2512.14170","title":"On Improving Deep Active Learning with Formal Verification","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2603.05604","title":"From Decoupled to Coupled: Robustness Verification for Learning-based Keypoint Detection with Joint Specifications","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2603.13334","title":"Lipschitz-Based Robustness Certification Under Floating-Point Execution","created":"2026-03-06","cited_by_count":0,"area":null},{"id":"2603.23878","title":"The Luna Bound Propagator for Formal Analysis of Neural Networks","created":"2026-03-25","cited_by_count":0,"area":null},{"id":"2604.06289","title":"Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2604.18728","title":"The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2604.21556","title":"Probabilistic Verification of Neural Networks via Efficient Probabilistic Hull Generation","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2605.13845","title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","created":"2026-05-13","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":"2605.30155","title":"Neural Network Verification using Partial Multi-Neuron Relaxation","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.23754","title":"Verifiable Foundation Models for Robot Safety","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2606.23858","title":"Are Safety Guarantees in Neural Networks Safe? How to Compute Trustworthy Robustness Certifications","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2607.03232","title":"Fast SDP certification of neural networks : towards large multi-class datasets","created":"2026-07-03","cited_by_count":0,"area":null},{"id":"2607.05253","title":"Repurposing CLIP to Localize at Pixel Level","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2607.08773","title":"Interval Certifications for Multilayered Perceptrons via Lattice Traversal","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2607.28954","title":"Mining Verdict Boundaries for Neural Network Verification","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2608.12037","title":"Clustered Randomized Smoothing for Stochastic Prediction Functions","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2608.19351","title":"Uncovering the Limits of Proof Sharing for Neural Networks","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2609.30050","title":"NNV3: Expanding Neural Network Verification to New Architectures and Domains","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.34628","title":"CLAD: Constrained Abstract Domain for Neural Network Verification","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}