{"total":119,"rows":[{"id":"2103.06624","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","created":"2021-03-11","cited_by_count":228,"area":null},{"id":"2208.05740","title":"General Cutting Planes for Bound-Propagation-Based Neural Network Verification","created":"2022-08-11","cited_by_count":103,"area":null},{"id":"2109.00498","title":"The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results","created":"2021-08-31","cited_by_count":71,"area":null},{"id":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66,"area":null},{"id":"2210.01787","title":"Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective","created":"2022-10-04","cited_by_count":51,"area":null},{"id":"2405.21063","title":"Neural Network Verification with Branch-and-Bound for General Nonlinearities","created":"2024-05-31","cited_by_count":47,"area":null},{"id":"2210.07394","title":"Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation","created":"2022-10-13","cited_by_count":45,"area":null},{"id":"2104.06718","title":"Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition","created":"2021-04-14","cited_by_count":44,"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":"2102.05363","title":"Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons","created":"2021-02-10","cited_by_count":40,"area":null},{"id":"2102.04373","title":"Partition-based formulations for mixed-integer optimization of trained ReLU neural networks","created":"2021-02-08","cited_by_count":39,"area":null},{"id":"2203.14195","title":"How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective","created":"2022-03-27","cited_by_count":39,"area":null},{"id":"2305.00241","title":"When Deep Learning Meets Polyhedral Theory: A Survey","created":"2023-04-29","cited_by_count":39,"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":"2302.01404","title":"Provably Bounding Neural Network Preimages","created":"2023-02-02","cited_by_count":29,"area":null},{"id":"2110.06850","title":"Boosting the Certified Robustness of L-infinity Distance Nets","created":"2021-10-13","cited_by_count":24,"area":null},{"id":"2203.11201","title":"Efficient Neural Network Analysis with Sum-of-Infeasibilities","created":"2022-03-19","cited_by_count":24,"area":null},{"id":"2205.12331","title":"Certified Robustness Against Natural Language Attacks by Causal Intervention","created":"2022-05-24","cited_by_count":24,"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":"2110.01110","title":"Safe Control with Neural Network Dynamic Models","created":"2021-10-03","cited_by_count":22,"area":null},{"id":"2108.04214","title":"Neural Network Repair with Reachability Analysis","created":"2021-08-09","cited_by_count":19,"area":null},{"id":"2109.10696","title":"CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks","created":"2021-09-22","cited_by_count":19,"area":null},{"id":"2303.01713","title":"Convex Bounds on the Softmax Function with Applications to Robustness Verification","created":"2023-03-03","cited_by_count":19,"area":null},{"id":"2102.09479","title":"Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications","created":"2021-02-18","cited_by_count":18,"area":null},{"id":"2102.12002","title":"Adversarial Robustness with Non-uniform Perturbations","created":"2021-02-24","cited_by_count":17,"area":null},{"id":"2103.07224","title":"BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks","created":"2021-03-12","cited_by_count":17,"area":null},{"id":"2205.09927","title":"CertiFair: A Framework for Certified Global Fairness of Neural Networks","created":"2022-05-20","cited_by_count":17,"area":null},{"id":"2206.03669","title":"Toward Certified Robustness Against Real-World Distribution Shifts","created":"2022-06-08","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":"2105.11363","title":"Learning Security Classifiers with Verified Global Robustness Properties","created":"2021-05-24","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":"2206.07311","title":"Can pruning improve certified robustness of neural networks?","created":"2022-06-15","cited_by_count":14,"area":null},{"id":"2203.03034","title":"A Unified View of SDP-based Neural Network Verification through Completely Positive Programming","created":"2022-03-06","cited_by_count":12,"area":null},{"id":"2506.06665","title":"SDP-CROWN: Efficient Bound Propagation for Neural Network Verification with Tightness of Semidefinite Programming","created":"2025-06-07","cited_by_count":12,"area":null},{"id":"2202.07201","title":"Holistic Adversarial Robustness of Deep Learning Models","created":"2022-02-15","cited_by_count":11,"area":null},{"id":"2101.05844","title":"Scaling the Convex Barrier with Sparse Dual Algorithms","created":"2021-01-14","cited_by_count":10,"area":null},{"id":"2109.00542","title":"Shared Certificates for Neural Network Verification","created":"2021-09-01","cited_by_count":10,"area":null},{"id":"2201.13351","title":"LinSyn: Synthesizing Tight Linear Bounds for Arbitrary Neural Network Activation Functions","created":"2022-01-31","cited_by_count":10,"area":null},{"id":"2206.07839","title":"Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness","created":"2022-06-15","cited_by_count":10,"area":null},{"id":"2207.02152","title":"UniCR: Universally Approximated Certified Robustness via Randomized Smoothing","created":"2022-07-05","cited_by_count":10,"area":null},{"id":"2110.07165","title":"Semantically Distributed Robust Optimization for Vision-and-Language Inference","created":"2021-10-14","cited_by_count":9,"area":null},{"id":"2203.07505","title":"Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems","created":"2022-03-14","cited_by_count":9,"area":null},{"id":"2211.08453","title":"Improved techniques for deterministic l2 robustness","created":"2022-11-15","cited_by_count":9,"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":"2106.09117","title":"DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting","created":"2021-06-16","cited_by_count":8,"area":null},{"id":"2111.09293","title":"Fast BATLLNN: Fast Box Analysis of Two-Level Lattice Neural Networks","created":"2021-11-17","cited_by_count":8,"area":null},{"id":"2210.12871","title":"Tighter Abstract Queries in Neural Network Verification","created":"2022-10-23","cited_by_count":8,"area":null},{"id":"2311.04015","title":"Expressivity of ReLU-Networks under Convex Relaxations","created":"2023-11-07","cited_by_count":8,"area":null},{"id":"2103.13598","title":"Recent Advances in Large Margin Learning","created":"2021-03-25","cited_by_count":7,"area":null},{"id":"2211.01498","title":"On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach","created":"2022-11-02","cited_by_count":7,"area":null},{"id":"2306.13614","title":"Adversarial Robustness Certification for Bayesian Neural Networks","created":"2023-06-23","cited_by_count":7,"area":null},{"id":"2408.13140","title":"Verification of Geometric Robustness of Neural Networks via Piecewise Linear Approximation and Lipschitz Optimisation","created":"2024-08-23","cited_by_count":7,"area":null},{"id":"2104.10868","title":"Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting","created":"2021-04-22","cited_by_count":6,"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":"2107.12855","title":"Neural Network Branch-and-Bound for Neural Network Verification","created":"2021-07-27","cited_by_count":6,"area":null},{"id":"2112.08091","title":"Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems","created":"2021-12-15","cited_by_count":6,"area":null},{"id":"2201.01235","title":"On the Minimal Adversarial Perturbation for Deep Neural Networks with Provable Estimation Error","created":"2022-01-04","cited_by_count":6,"area":null},{"id":"2207.05327","title":"Certified Adversarial Robustness via Anisotropic Randomized Smoothing","created":"2022-07-12","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":"2403.13778","title":"Certified Human Trajectory Prediction","created":"2024-03-20","cited_by_count":6,"area":null},{"id":"2102.01208","title":"Fast Training of Provably Robust Neural Networks by SingleProp","created":"2021-02-01","cited_by_count":5,"area":null},{"id":"2103.04436","title":"Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy","created":"2021-03-07","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":"2207.07539","title":"3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models","created":"2022-07-15","cited_by_count":5,"area":null},{"id":"2210.08069","title":"Zonotope Domains for Lagrangian Neural Network Verification","created":"2022-10-14","cited_by_count":5,"area":null},{"id":"2211.14438","title":"BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic","created":"2022-11-22","cited_by_count":5,"area":null},{"id":"2304.02693","title":"A Certified Radius-Guided Attack Framework to Image Segmentation Models","created":"2023-04-05","cited_by_count":5,"area":null},{"id":"2106.12074","title":"Reachability Analysis of Convolutional Neural Networks","created":"2021-06-22","cited_by_count":4,"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":"2107.11445","title":"Self-Correcting Neural Networks For Safe Classification","created":"2021-07-23","cited_by_count":4,"area":null},{"id":"2111.13110","title":"QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking","created":"2021-11-25","cited_by_count":4,"area":null},{"id":"2206.00553","title":"FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks","created":"2022-06-01","cited_by_count":4,"area":null},{"id":"2206.03482","title":"Chordal Sparsity for SDP-based Neural Network Verification","created":"2022-06-07","cited_by_count":4,"area":null},{"id":"2211.07125","title":"Global Performance Guarantees for Neural Network Models of AC Power Flow","created":"2022-11-14","cited_by_count":4,"area":null},{"id":"2306.10617","title":"GPU-Accelerated Verification of Machine Learning Models for Power Systems","created":"2023-06-18","cited_by_count":4,"area":null},{"id":"2402.11168","title":"Trust Regions for Explanations via Black-Box Probabilistic Certification","created":"2024-02-17","cited_by_count":4,"area":null},{"id":"2512.00164","title":"Faster Verified Explanations for Neural Networks","created":"2025-11-28","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":"2111.07683","title":"Reachability analysis of neural networks using mixed monotonicity","created":"2021-11-15","cited_by_count":3,"area":null},{"id":"2210.16940","title":"FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs","created":"2022-10-30","cited_by_count":3,"area":null},{"id":"2211.16187","title":"Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks","created":"2022-11-29","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":"2410.00796","title":"Fast and Reliable $N-k$ Contingency Screening with Input-Convex Neural Networks","created":"2024-10-01","cited_by_count":3,"area":null},{"id":"2410.01617","title":"On Using Certified Training towards Empirical Robustness","created":"2024-10-02","cited_by_count":3,"area":null},{"id":"2104.12418","title":"Fast Falsification of Neural Networks using Property Directed Testing","created":"2021-04-26","cited_by_count":2,"area":null},{"id":"2106.03099","title":"A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification","created":"2021-06-06","cited_by_count":2,"area":null},{"id":"2211.14706","title":"Neural Network Verification as Piecewise Linear Optimization: Formulations for the Composition of Staircase Functions","created":"2022-11-27","cited_by_count":2,"area":null},{"id":"2505.03643","title":"BURNS: Backward Underapproximate Reachability for Neural-Feedback-Loop Systems","created":"2025-05-06","cited_by_count":2,"area":null},{"id":"2102.05096","title":"Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples","created":"2021-02-09","cited_by_count":1,"area":null},{"id":"2112.09279","title":"Robust Upper Bounds for Adversarial Training","created":"2021-12-17","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":"2301.12001","title":"Vertex-based reachability analysis for verifying ReLU deep neural networks","created":"2023-01-27","cited_by_count":1,"area":null},{"id":"2303.06241","title":"Do we need entire training data for adversarial training?","created":"2023-03-10","cited_by_count":1,"area":null},{"id":"2304.07980","title":"RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks","created":"2023-04-17","cited_by_count":1,"area":null},{"id":"2306.06995","title":"How robust accuracy suffers from certified training with convex relaxations","created":"2023-06-12","cited_by_count":1,"area":null},{"id":"2308.03330","title":"Expediting Neural Network Verification via Network Reduction","created":"2023-08-07","cited_by_count":1,"area":null},{"id":"2310.09361","title":"Is Certifying $\\ell_p$ Robustness Still Worthwhile?","created":"2023-10-13","cited_by_count":1,"area":null},{"id":"2402.07498","title":"Accelerated Smoothing: A Scalable Approach to Randomized Smoothing","created":"2024-02-12","cited_by_count":1,"area":null},{"id":"2504.08414","title":"Adversarial Examples in Environment Perception for Automated Driving (Review)","created":"2025-04-11","cited_by_count":1,"area":null},{"id":"2510.19977","title":"Towards Strong Certified Defense with Universal Asymmetric Randomization","created":"2025-10-22","cited_by_count":1,"area":null}],"next_cursor":"100"}