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This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV).
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
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.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 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
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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.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 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 Zeljic, David L. Dill, Mykel J. Kochenderfer, and Clark W. Barrett · 2019
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.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Earlier work this paper cites.
Abstraction based output range analysis for neural networks
Pavithra Prabhakar and Zahra Rahimi Afzal · 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 T. Vechev · 2019
Earlier work this paper cites.
Boosting robustness certification of neural networks
Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin T. Vechev · 2019
Earlier work this paper cites.
Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Y. Xiao, and Russ Tedrake · 2019
Earlier work this paper cites.
Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Yuanqing Xiao, and Russ Tedrake · 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.
Towards stable and efficient training of verifiably robust neural networks
Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane Boning, and Cho-Jui Hsieh · 2019
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.
Generating adversarial examples with graph neural networks
Florian Jaeckle and M Pawan Kumar · 2021
Later among the works it cites.
Neural network branch-and-bound for neural network verification
Florian Jaeckle, Jingyue Lu, and M Pawan Kumar · 2021
Later among the works it cites.
Peregrinn: Penalized-relaxation greedy neural network verifier
Haitham Khedr, James Ferlez, and Yasser Shoukry · 2021
Later among the works it cites.
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
Later among the works it cites.
DNNV: A framework for deep neural network verification
David Shriver, Sebastian G. Elbaum, and Matthew B. Dwyer · 2021
Later among the works it cites.
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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.
Efficient neural network verification via adaptive refinement and adversarial search
P. Henriksen and A. Lomuscio · 2020
Cited alongside, same era.
Neural network branching for neural network verification
Jingyue Lu and M Pawan Kumar · 2020
Cited alongside, same era.
Adversarial training and provable defenses: Bridging the gap
Martin Vechev Mislav Balunovic · 2020
Cited alongside, same era.
Verification of deep convolutional neural networks using imagestars
Hoang-Dung Tran, Stanley Bak, Weiming Xiang, and Taylor T. Johnson · 2020
Cited alongside, same era.
International verification of neural networks competition (VNN-COMP)
VNN-COMP · 2020
Cited alongside, same era.
Refutation-based adversarial robustness verification of deep neural networks
Joshua Smith, Jarom Allan, Viswanathan Swaminathan, and Zhen Zhang · 2021
Later among the works it cites.
Robustness verification of semantic segmentation neural networks using relaxed reachability
Hoang-Dung Tran, Neelanjana Pal, Patrick Musau, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Stanley Bak, and Taylor T Johnson · 2021
Later among the works it cites.
Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, and Zico Kolter · 2021
Later among the works it cites.
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
Later among the works it cites.
Fast BATLLNN: fast box analysis of two-level lattice neural networks
James Ferlez, Haitham Khedr, and Yasser Shoukry · 2022
Closest in time.
Complete verification via multi-neuron relaxation guided branch-and-bound
Claudio Ferrari, Mark Niklas Müller, Nikola Jovanovic, and Martin T. Vechev · 2022
Closest in time.
Gurobi Optimizer Reference Manual, 2022
Gurobi Optimization, LLC · 2022
Closest in time.
Characterizing neural network verification for systems with NN4SYSBench
Haoyu He, Tianhao Wei, Huan Zhang, Changliu Liu, and Cheng Tan · 2022
Closest in time.
Case studies for computing density of reachable states for safe autonomous motion planning
Yue Meng, Zeng Qiu, Md Tawhid Bin Waez, and Chuchu Fan · 2022
Closest in time.
Learning density distribution of reachable states for autonomous systems
Yue Meng, Dawei Sun, Zeng Qiu, Md Tawhid Bin Waez, and Chuchu Fan · 2022
Closest in time.
Safe reinforcement learning benchmark environments for aerospace control systems
Umberto J Ravaioli, James Cunningham, John McCarroll, Vardaan Gangal, Kyle Dunlap, and Kerianne L Hobbs · 2022
Closest in time.
Scalable verification of gnn-based job schedulers
Haoze Wu, Clark Barrett, Mahmood Sharif, Nina Narodytska, and Gagandeep Singh · 2022
Closest in time.
Toward certified robustness against real-world distribution shifts
Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George Pappas, Hamed Hassani, Corina Pasareanu, and Clark Barrett · 2022
Closest in time.
Efficient neural network analysis with sum-of-infeasibilities
Haoze Wu, Aleksandar Zeljić, Guy Katz, and Clark Barrett · 2022
Closest in time.
General cutting planes for bound-propagation-based neural network verification
Huan Zhang*, Shiqi Wang*, Kaidi Xu*, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, and J Zico Kolter · 2022
Closest in time.