{"total":34,"rows":[{"id":"2206.00512","title":"Neural Network Verification with Proof Production","created":"2022-06-01","cited_by_count":22,"area":null},{"id":"2401.06379","title":"Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs","created":"2024-01-12","cited_by_count":14,"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":"2108.07961","title":"Verifying Low-dimensional Input Neural Networks via Input Quantization","created":"2021-08-18","cited_by_count":7,"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":"2411.05197","title":"Hardware and Software Platform Inference","created":"2024-11-07","cited_by_count":7,"area":null},{"id":"2106.05997","title":"Verifying Quantized Neural Networks using SMT-Based Model Checking","created":"2021-06-10","cited_by_count":6,"area":null},{"id":"2307.06299","title":"Towards a Certified Proof Checker for Deep Neural Network Verification","created":"2023-07-12","cited_by_count":6,"area":null},{"id":"2506.01054","title":"No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks","created":"2025-06-01","cited_by_count":6,"area":null},{"id":"2202.05207","title":"Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers","created":"2022-02-10","cited_by_count":5,"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":"2208.08609","title":"A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables","created":"2022-08-18","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":"2602.22631","title":"TorchLean: Formalizing Neural Networks in Lean","created":"2026-02-26","cited_by_count":4,"area":null},{"id":"2203.07078","title":"A Mixed Integer Programming Approach for Verifying Properties of Binarized Neural Networks","created":"2022-03-11","cited_by_count":3,"area":null},{"id":"2207.07209","title":"Sound Randomized Smoothing in Floating-Point Arithmetics","created":"2022-07-14","cited_by_count":3,"area":null},{"id":"2207.10562","title":"CheckINN: Wide Range Neural Network Verification in Imandra (Extended)","created":"2022-07-21","cited_by_count":3,"area":null},{"id":"2305.18558","title":"DelBugV: Delta-Debugging Neural Network Verifiers","created":"2023-05-29","cited_by_count":3,"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":"2510.23389","title":"Floating-Point Neural Network Verification at the Software Level","created":"2025-10-27","cited_by_count":2,"area":null},{"id":"2601.21902","title":"Hardware-Triggered Backdoors","created":"2026-01-29","cited_by_count":2,"area":null},{"id":"2604.26505","title":"Quantamination: Dynamic Quantization Leaks Your Data Across the Batch","created":"2026-04-29","cited_by_count":2,"area":null},{"id":"2405.17049","title":"Verifying Properties of Binary Neural Networks Using Sparse Polynomial Optimization","created":"2024-05-27","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":"2510.16028","title":"TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks","created":"2025-10-15","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":"2203.00295","title":"A Domain-Theoretic Framework for Robustness Analysis of Neural Networks","created":"2022-03-01","cited_by_count":0,"area":null},{"id":"2307.00280","title":"SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency","created":"2023-07-01","cited_by_count":0,"area":null},{"id":"2601.13303","title":"On the Extreme Variance of Certified Local Robustness Across Model Seeds","created":"2026-01-19","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":"2605.17153","title":"Stress-Testing Neural Network Verifiers with Provably Robust Instances","created":"2026-05-16","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},{"id":"2607.15328","title":"Lazy Arithmetic using Systolic Arrays for Closing the Verification Gap on Embedded Systems","created":"2026-07-16","cited_by_count":0,"area":null}],"next_cursor":null}