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This report summarizes the second International Verification of Neural Networks Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for ML-Enabled Autonomous Systems that was collocated with the 33rd International Conference on Computer-Aided Verification (CAV).
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
Fast polyhedra abstract domain
Gagandeep Singh, Markus Püschel, and Martin Vechev · 2017
Earlier work this paper cites.
A unified view of piecewise linear neural network verification
Rudy Bunel, Ilker Turkaslan, Philip HS Torr, Pushmeet Kohli, and M Pawan Kumar · 2018
Earlier work this paper cites.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Earlier work this paper cites.
A dual approach to scalable verification of deep networks
Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, and Pushmeet Kohli · 2018
Earlier work this paper cites.
The case for learned index structures
Tim Kraska, Alex Beutel, Ed H Chi, Jeffrey Dean, and Neoklis Polyzotis · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Earlier work this paper cites.
Fast and effective robustness certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, and Martin Vechev · 2018
Earlier work this paper cites.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
Earlier work this paper cites.
Output reachable set estimation and verification for multilayer neural networks
W. Xiang, H. Tran, and T. T. Johnson · 2018
Earlier work this paper cites.
Efficient neural network robustness certification with general activation functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
Earlier work this paper cites.
The marabou framework for verification and analysis of deep neural networks
Guy Katz, Derek A Huang, Duligur Ibeling, Kyle Julian, Christopher Lazarus, Rachel Lim, Parth Shah, Shantanu Thakoor, Haoze Wu, Aleksandar Zeljić, et al · 2019
Earlier work this paper cites.
Neuralverification.jl: Algorithms for verifying deep neural networks
Changliu Liu, Tomer Arnon, Christopher Lazarus, and Mykel J Kochenderfer · 2019
Earlier work this paper cites.
A convex relaxation barrier to tight robustness verification of neural networks
Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, and Pengchuan Zhang · 2019
Earlier work this paper cites.
Beyond the single neuron convex barrier for neural network certification
Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, and Martin Vechev · 2019
Earlier work this paper cites.
An abstract domain for certifying neural networks
Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin Vechev · 2019
Earlier work this paper cites.
Boosting robustness certification of neural networks
Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin Vechev · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Y. Xiao, and Russ Tedrake · 2019
Cited alongside, same era.
Parallelizable reachability analysis algorithms for feed-forward neural networks
Hoang-Dung Tran, Patrick Musau, Diego Manzanas Lopez, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, and Taylor T. Johnson · 2019
Cited alongside, same era.
Star-based reachability analysis for deep neural networks
Hoang-Dung Tran, Patrick Musau, Diego Manzanas Lopez, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, and Taylor T. Johnson · 2019
Cited alongside, same era.
An smt-based approach for verifying binarized neural networks
Guy Amir, Haoze Wu, Clark Barrett, and Guy Katz · 2020
Cited alongside, same era.
Parallelization techniques for verifying neural networks
Haoze Wu, Alex Ozdemir, Aleksandar Zeljic, Kyle Julian, Ahmed Irfan, Divya Gopinath, Sadjad Fouladi, Guy Katz, Corina Pasareanu, and Clark Barrett · 2020
Later among the works it cites.
Automatic perturbation analysis for scalable certified robustness and beyond
Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, and Cho-Jui Hsieh · 2020
Later among the works it cites.
nnenum: Verification of relu neural networks with optimized abstraction refinement
Stanley Bak · 2021
Closest in time.
Scaling the convex barrier with active sets
Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, and M. Pawan Kumar · 2021
Closest in time.
Scaling the convex barrier with sparse dual algorithms
Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, and M. Pawan Kumar · 2021
Closest in time.
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Strong mixed-integer programming formulations for trained neural networks
Ross Anderson, Joey Huchette, Will Ma, Christian Tjandraatmadja, and Juan Pablo Vielma · 2020
Cited alongside, same era.
Execution-guided overapproximation (ego) for improving scalability of neural network verification, 2020
Stanley Bak · 2020
Cited alongside, same era.
Improved geometric path enumeration for verifying ReLU neural networks
Stanley Bak, Hoang-Dung Tran, Kerianne Hobbs, and Taylor T. Johnson · 2020
Cited alongside, same era.
Efficient verification of neural networks via dependency analysis
E. Botoeva, P. Kouvaros, J. Kronqvist, A. Lomuscio, and R. Misener · 2020
Cited alongside, same era.
Christopher Brix and Thomas Noll · 2020
Cited alongside, same era.
Lagrangian decomposition for neural network verification
Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip HS Torr, and M Pawan Kumar · 2020
Cited alongside, same era.
Branch and bound for piecewise linear neural network verification
Rudy Bunel, Jingyue Lu, Ilker Turkaslan, P Kohli, P Torr, and M Pawan Kumar · 2020
Cited alongside, same era.
Alessandro De Palma, Rudy Bunel, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip HS Torr, and M Pawan Kumar · 2021
Closest in time.
Deepsplit: An efficient splitting method for neural network verification via indirect effect analysis
P. Henriksen and A. Lomuscio · 2021
Closest in time.
Generating adversarial examples with graph neural networks
Florian Jaeckle and M Pawan Kumar · 2021
Closest in time.
Neural network branch-and-bound for neural network verification
Florian Jaeckle, Jingyue Lu, and M Pawan Kumar · 2021
Closest in time.
Towards scalable complete verification of relu neural networks via dependency-based branching
P. Kouvaros and A. Lomuscio · 2021
Closest in time.
Algorithms for verifying deep neural networks
Changliu Liu, Tomer Arnon, Christopher Lazarus, Christopher Strong, Clark Barrett, and Mykel J. Kochenderfer · 2021
Closest in time.
Prima: Precise and general neural network certification via multi-neuron convex relaxations
Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, and Martin Vechev · 2021
Closest in time.
Scaling polyhedral neural network verification on GPUs
François Serre, Christoph Müller, Gagandeep Singh, Markus Püschel, and Martin Vechev · 2021
Closest in time.
DNNV: A framework for deep neural network verification
David Shriver, Sebastian G. Elbaum, and Matthew B. Dwyer · 2021
Closest in time.
Reducing DNN properties to enable falsification with adversarial attacks
David Shriver, Sebastian G. Elbaum, and Matthew B. Dwyer · 2021
Closest in time.
Reachable polyhedral marching (rpm): A safety verification algorithm for robotic systems with deep neural network components, 2021
Joseph A. Vincent and Mac Schwager · 2021
Closest in time.
Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, and Zico Kolter · 2021
Closest in time.
Fast and Complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers
Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, and Cho-Jui Hsieh · 2021
Closest in time.