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Bound propagation based incomplete neural network verifiers such as CROWN are very efficient and can significantly accelerate branch-and-bound (BaB) based complete verification of neural networks.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Formal verification of piece-wise linear feed-forward neural networks
R. Ehlers · 2017
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Safety verification of deep neural networks
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
G. Katz, C. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer · 2017
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Robust deep reinforcement learning with adversarial attacks
A. Pattanaik, Z. Tang, S. Liu, G. Bommannan, and G. Chowdhary · 2017
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A unified view of piecewise linear neural network verification
R. R. Bunel, I. Turkaslan, P. Torr, P. Kohli, and P. K. Mudigonda · 2018
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Output range analysis for deep feedforward neural networks
S. Dutta, S. Jha, S. Sankaranarayanan, and A. Tiwari · 2018
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A dual approach to scalable verification of deep networks
K. Dvijotham, R. Stanforth, S. Gowal, T. Mann, and P. Kohli · 2018
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Ai2: Safety and robustness certification of neural networks with abstract interpretation
T. Gehr, M. Mirman, D. Drachsler-Cohen, P. Tsankov, S. Chaudhuri, and M. Vechev · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Differentiable abstract interpretation for provably robust neural networks
M. Mirman, T. Gehr, and M. Vechev · 2018
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Semidefinite relaxations for certifying robustness to adversarial examples
A. Raghunathan, J. Steinhardt, and P. S. Liang · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
E. Wong and Z. Kolter · 2018
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Scaling provable adversarial defenses
E. Wong, F. Schmidt, J. H. Metzen, and J. Z. Kolter · 2018
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Efficient neural network robustness certification with general activation functions
H. Zhang, T.-W. Weng, P.-Y. Chen, C.-J. Hsieh, and L. Daniel · 2018
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Optimization and abstraction: A synergistic approach for analyzing neural network robustness
G. Anderson, S. Pailoor, I. Dillig, and S. Chaudhuri · 2019
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On the effectiveness of interval bound propagation for training verifiably robust models
S. Gowal, K. Dvijotham, R. Stanforth, R. Bunel, C. Qin, J. Uesato, T. Mann, and P. Kohli · 2019
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Efficient neural network verification with exactness characterization
K. D. Dvijotham, R. Stanforth, S. Gowal, C. Qin, S. De, and P. Kohli · 2020
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Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming
M. Fazlyab, M. Morari, and G. J. Pappas · 2020
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Vnn comp 2020
C. Liu and T. Johnson · 2020
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Neural network branching for neural network verification
J. Lu and M. P. Kumar · 2020
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Robustness verification for transformers
Z. Shi, H. Zhang, K.-W. Chang, M. Huang, and C.-J. Hsieh · 2020
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The convex relaxation barrier, revisited: Tightened single-neuron relaxations for neural network verification
C. Tjandraatmadja, R. Anderson, J. Huchette, W. Ma, K. Patel, and J. P. Vielma · 2020
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V. R. Royo, R. Calandra, D. M. Stipanovic, and C. Tomlin · 2019
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A convex relaxation barrier to tight robustness verification of neural networks
H. Salman, G. Yang, H. Zhang, C.-J. Hsieh, and P. Zhang · 2019
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Evaluating robustness of neural networks with mixed integer programming
V. Tjeng, K. Xiao, and R. Tedrake · 2019
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Strong convex relaxations and mixed-integer programming formulations for trained neural networks
R. Anderson, J. Huchette, C. Tjandraatmadja, and J. P. Vielma · 2020
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Adversarial training and provable defenses: Bridging the gap
M. Balunovic and M. Vechev · 2020
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Efficient verification of relu-based neural networks via dependency analysis
E. Botoeva, P. Kouvaros, J. Kronqvist, A. Lomuscio, and R. Misener · 2020
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Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
S. Dathathri, K. Dvijotham, A. Kurakin, A. Raghunathan, J. Uesato, R. R. Bunel, S. Shankar, J. Steinhardt, I. Goodfellow, P. S. Liang, et al · 2020
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Automatic perturbation analysis for scalable certified robustness and beyond
K. Xu, Z. Shi, H. Zhang, Y. Wang, K.-W. Chang, M. Huang, B. Kailkhura, X. Lin, and C.-J. Hsieh · 2020
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S. Bak, C. Liu, and T. Johnson · 2021
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Precise multi-neuron abstractions for neural network certification
M. N. Müller, G. Makarchuk, G. Singh, M. Püschel, and M. Vechev · 2021
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Fast certified robust training via better initialization and shorter warmup
Z. Shi, Y. Wang, H. Zhang, J. Yi, and C.-J. Hsieh · 2021
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Fast and complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers
K. Xu, H. Zhang, S. Wang, Y. Wang, S. Jana, X. Lin, and C.-J. Hsieh · 2021
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Robust reinforcement learning on state observations with learned optimal adversary
H. Zhang, H. Chen, D. Boning, and C.-J. Hsieh · 2021
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