neural network verification

Efficient Neural Network Verification via Layer-based Semidefinite Relaxations and Linear Cuts IJCAI 2021 Exploiting Sparsity for Neural Network Verification L4DC 2021 Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers ICLR 2021 Fooling a Complete Neural Network Verifier ICLR 2021 Formal verification of neural networks for safety-critical tasks in deep reinforcement learning UAI 2021 Globally-Robust Neural Networks ICML 2021 Learning Approximate Forward Reachable Sets Using Separating Kernels L4DC 2021 Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications NeurIPS 2021 Overcoming the Convex Barrier for Simplex Inputs NeurIPS 2021 Safety Analysis of Deep Neural Networks IJCAI 2021 Safety Assurance for Systems with Machine Learning Components AAAI 2021 Scalable Verification of Quantized Neural Networks AAAI 2021 Scaling Symbolic Methods using Gradients for Neural Model Explanation ICLR 2021 Scaling the Convex Barrier with Active Sets ICLR 2021 Towards Evaluating and Training Verifiably Robust Neural Networks CVPR 2021 Towards Scalable Complete Verification of Relu Neural Networks via Dependency-based Branching IJCAI 2021 Verification and Repair of Neural Networks AAAI 2021 Adversarial Training and Provable Defenses: Bridging the Gap ICLR 2020 An efficient nonconvex reformulation of stagewise convex optimization problems NeurIPS 2020 Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond NeurIPS 2020 Branch and Bound for Piecewise Linear Neural Network Verification JMLR 2020 Certifying Strategyproof Auction Networks NeurIPS 2020 Efficient Exact Verification of Binarized Neural Networks NeurIPS 2020 Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis AAAI 2020 Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming NeurIPS 2020