56 results on '"Guy Katz"'
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2. Safe and Reliable Training of Learning-Based Aerospace Controllers.
3. Formal Verification of Object Detection.
4. Verifying the Generalization of Deep Learning to Out-of-Distribution Domains.
5. Verification-Guided Shielding for Deep Reinforcement Learning.
6. Analyzing Adversarial Inputs in Deep Reinforcement Learning.
7. Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates.
8. Local vs. Global Interpretability: A Computational Complexity Perspective.
9. NLP Verification: Towards a General Methodology for Certifying Robustness.
10. Shield Synthesis for LTL Modulo Theories.
11. Marabou 2.0: A Versatile Formal Analyzer of Neural Networks.
12. DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace.
13. Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing.
14. On Augmenting Scenario-Based Modeling with Generative AI.
15. A Certified Proof Checker for Deep Neural Network Verification.
16. DelBugV: Delta-Debugging Neural Network Verifiers.
17. OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks.
18. Towards a Certified Proof Checker for Deep Neural Network Verification.
19. Enhancing Deep Learning with Scenario-Based Override Rules: a Case Study.
20. gRoMA: a Tool for Measuring Deep Neural Networks Global Robustness.
21. DNN Verification, Reachability, and the Exponential Function Problem.
22. On Reducing Undesirable Behavior in Deep Reinforcement Learning Models.
23. Verifying Generalization in Deep Learning.
24. Formally Explaining Neural Networks within Reactive Systems.
25. Neural Network Verification with Proof Production.
26. Efficient Neural Network Analysis with Sum-of-Infeasibilities.
27. Scenario-Assisted Deep Reinforcement Learning.
28. veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System.
29. BBReach: Tight and Scalable Black-Box Reachability Analysis of Deep Reinforcement Learning Systems.
30. Neural Network Verification using Residual Reasoning.
31. Verifying Learning-Based Robotic Navigation Systems.
32. Tighter Abstract Queries in Neural Network Verification.
33. Towards Formal Approximated Minimal Explanations of Neural Networks.
34. Constrained Reinforcement Learning for Robotics via Scenario-Based Programming.
35. On Optimizing Back-Substitution Methods for Neural Network Verification.
36. Verification-Aided Deep Ensemble Selection.
37. Efficiently Finding Adversarial Examples with DNN Preprocessing.
38. An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks.
39. Towards Scalable Verification of RL-Driven Systems.
40. Towards Repairing Scenario-Based Models with Rich Events.
41. Minimal Multi-Layer Modifications of Deep Neural Networks.
42. RoMA: a Method for Neural Network Robustness Measurement and Assessment.
43. Pruning and Slicing Neural Networks using Formal Verification.
44. Parallelization Techniques for Verifying Neural Networks.
45. Guarded Deep Learning using Scenario-Based Modeling.
46. Verifying Recurrent Neural Networks using Invariant Inference.
47. An SMT-Based Approach for Verifying Binarized Neural Networks.
48. Global Optimization of Objective Functions Represented by ReLU Networks.
49. Simplifying Neural Networks with the Marabou Verification Engine.
50. On-the-Fly Construction of Composite Events in Scenario-Based Modeling using Constraint Solvers.
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