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This report summarizes the 5th International Verification of Neural Networks Competition (VNN-COMP 2024), held as a part of the 7th International Symposium on AI Verification (SAIV), that was collocated with the 36th International Conference on Computer-Aided Verification (CAV).
Intriguing Properties of Neural Networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
An introduction to CORA 2015
Matthias Althoff · 2015
Earlier work this paper cites.
Binarized Neural Networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 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.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Patient and consumer safety risks when using conversational assistants for medical information: an observational study of siri, alexa, and google assistant
Timothy W Bickmore, Ha Trinh, Stefan Olafsson, Teresa K O’Leary, Reza Asadi, Nathaniel M Rickles, and Ricardo Cruz · 2018
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.
On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, 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.
Efficient neural network robustness certification with general activation functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
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Achieving verified robustness to symbol substitutions via interval bound propagation
Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, and Pushmeet Kohli · 2019
Earlier work this paper cites.
Certified robustness to adversarial word substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang · 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.
Popqorn: Quantifying robustness of recurrent neural networks
Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, and Dahua Lin · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych · 2019
Earlier work this paper cites.
Robustness verification for transformers
Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, and Cho-Jui Hsieh · 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.
Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Y. Xiao, and Russ Tedrake · 2019
Earlier work this paper cites.
Safety verification of cyber-physical systems with reinforcement learning control
Hoang-Dung Tran, Feiyang Cei, Diego Manzanas Lopez, Taylor T. Johnson, and Xenofon Koutsoukos · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Execution-guided overapproximation (ego) for improving scalability of neural network verification, 2020
Stanley Bak · 2020
Earlier work this paper cites.
Improved geometric path enumeration for verifying ReLU neural networks
Stanley Bak, Hoang-Dung Tran, Kerianne Hobbs, and Taylor T. Johnson · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Earlier work this paper cites.
Robustness verification for transformers, 2020
Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, and Cho-Jui Hsieh · 2020
Earlier work this paper cites.
Verification of deep convolutional neural networks using imagestars
Hoang-Dung Tran, Stanley Bak, Weiming Xiang, and Taylor T. Johnson · 2020
Earlier work this paper cites.
NNV: The neural network verification tool for deep neural networks and learning-enabled cyber-physical systems
Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, and Taylor T. Johnson · 2020
Earlier work this paper cites.
Towards verified robustness under text deletion interventions
Johannes Welbl, Po-Sen Huang, Robert Stanforth, Sven Gowal, Krishnamurthy Dj Dvijotham, Martin Szummer, and Pushmeet Kohli · 2020
Earlier work this paper cites.
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
Earlier work this paper 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
Cited alongside, same era.
Lightweight Deep Network for Traffic Sign Classification
Jianming Zhang, Wei Wang, Chaoquan Lu, Jin Wang, and Arun Kumar Sangaiah · 2020
Cited alongside, same era.
nnenum: Verification of relu neural networks with optimized abstraction refinement
Stanley Bak · 2021
Cited alongside, same era.
The second international verification of neural networks competition (vnn-comp 2021): Summary and results, 2021
Stanley Bak, Changliu Liu, and Taylor Johnson · 2021
Cited alongside, same era.
Fast and precise certification of transformers
Gregory Bonaert, Dimitar I Dimitrov, Maximilian Baader, and Martin Vechev · 2021
Cited alongside, same era.
Cert-rnn: Towards certifying the robustness of recurrent neural networks
The fourth international verification of neural networks competition (vnn-comp 2023): Summary and results
Christopher Brix, Stanley Bak, Changliu Liu, and Taylor T. Johnson · 2023
Later among the works it cites.
First three years of the international verification of neural networks competition (vnn-comp), 2023
Christopher Brix, Mark Niklas Müller, Stanley Bak, Taylor T. Johnson, and Changliu Liu · 2023
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Antonio: Towards a systematic method for generating nlp benchmarks for verification
Marco Casadio, Luca Arnaboldi, Matthew L Daggitt, Omri Isac, Tanvi Dinkar, Daniel Kienitz, Verena Rieser, and Ekaterina Komendantskaya · 2023
Later among the works it cites.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
Later among the works it cites.
Supporting standardization of neural networks verification with vnnlib and coconet
Stefano Demarchi, Dario Guidotti, Luca Pulina, and Armando Tacchella · 2023
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Tianyu Du, Shouling Ji, Lujia Shen, Yao Zhang, Jinfeng Li, Jie Shi, Chengfang Fang, Jianwei Yin, Raheem Beyah, and Ting Wang · 2021
Cited alongside, same era.
The rua-robot dataset: Helping avoid chatbot deception by detecting user questions about human or non-human identity
David Gros, Yu Li, and Zhou Yu · 2021
Cited alongside, same era.
pynever: A framework for learning and verification of neural networks
Dario Guidotti, Luca Pulina, and Armando Tacchella · 2021
Cited alongside, same era.
Verification of image-based neural network controllers using generative models, 2021
Sydney M. Katz, Anthony L. Corso, Christopher A. Strong, and Mykel J. Kochenderfer · 2021
Cited alongside, same era.
Eu artificial intelligence act: The european approach to ai, 2021
Mauritz Kop · 2021
Cited alongside, same era.
Evaluating the robustness of neural language models to input perturbations
Milad Moradi and Matthias Samwald · 2021
Cited alongside, same era.
Fast certified robust training with short warmup
Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2021
Cited alongside, same era.
Later among the works it cites.
A DPLL(T) Framework for Verifying Deep Neural Networks, 2023
Hai Duong, Linhan Li, ThanhVu Nguyen, and Matthew Dwyer · 2023
Later among the works it cites.
Formal Methods use for Learning Assurance (ForMuLA)
EASA and Collins Aerospace · 2023
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Benchmark: remaining useful life predictor for aircraft equipment
Dmitrii Kirov and Simone Fulvio Rollini · 2023
Later among the works it cites.
Formal verification of a neural network based prognostics system for aircraft equipment
Dmitrii Kirov, Simone Fulvio Rollini, Luigi Di Guglielmo, and Darren Cofer · 2023
Later among the works it cites.
Open- and closed-loop neural network verification using polynomial zonotopes
Niklas Kochdumper, Christian Schilling, Matthias Althoff, and Stanley Bak · 2023
Later among the works it cites.
Automatic abstraction refinement in neural network verification using sensitivity analysis
Tobias Ladner and Matthias Althoff · 2023
Later among the works it cites.
NNV 2.0: The neural network verification tool
Diego Manzanas Lopez, Sung Woo Choi, Hoang-Dung Tran, and Taylor T. Johnson · 2023
Later among the works it cites.
Architecturing binarized neural networks for traffic sign recognition
Andreea Postovan and Mădălina Eraşcu · 2023
Later among the works it cites.
Verification of recurrent neural networks using star reachability
Hoang Dung Tran, SungWoo Choi, Tomoya Yamaguchi, Bardh Hoxha, and Danil Prokhorov · 2023
Later among the works it cites.
Robustness-aware word embedding improves certified robustness to adversarial word substitutions
Yibin Wang, Yichen Yang, Di He, and Kun He · 2023
Later among the works it cites.
Convex bounds on the softmax function with applications to robustness verification
Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark Barrett, and Eitan Farchi · 2023
Later among the works it cites.
https://https://cora.in.tum.de//
CORA: A Tool for Continuous Reachability Analysis · 2024
Closest in time.
https://pyrat-analyzer.com/
PyRAT Analyzer website · 2024
Closest in time.
Verification of Neural Network Control Systems in Continuous Time
Ali ArjomandBigdeli, Andrew Mata, and Stanley Bak · 2024
Closest in time.
Nlp verification: Towards a general methodology for certifying robustness
Marco Casadio, Tanvi Dinkar, Ekaterina Komendantskaya, Luca Arnaboldi, Omri Isac, Matthew L Daggitt, Guy Katz, Verena Rieser, and Oliver Lemon · 2024
Closest in time.
Never2: Learning and verification of neural networks
Stefano Demarchi, Dario Guidotti, Luca Pulina, and Armando Tacchella · 2024
Closest in time.
Harnessing neuron stability to improve dnn verification
Hai Duong, Dong Xu, ThanhVu Nguyen, and Matthew B Dwyer · 2024
Closest in time.
Set-based training for neural network verification
Lukas Koller, Tobias Ladner, and Matthias Althoff · 2024
Closest in time.
Neural Network Verification with PyRAT
Augustin Lemesle, Julien Lehmann, and Le Gall Tristan · 2024
Closest in time.
Certified training with branch-and-bound: A case study on lyapunov-stable neural control
Zhouxing Shi, Cho-Jui Hsieh, and Huan Zhang · 2024
Closest in time.
Neural network verification with branch-and-bound for general nonlinearities
Zhouxing Shi, Qirui Jin, Zico Kolter, Suman Jana, Cho-Jui Hsieh, and Huan Zhang · 2024
Closest in time.
Marabou 2.0: a versatile formal analyzer of neural networks
Haoze Wu, Omri Isac, Aleksandar Zeljić, Teruhiro Tagomori, Matthew Daggitt, Wen Kokke, Idan Refaeli, Guy Amir, Kyle Julian, Shahaf Bassan, et al · 2024
Closest in time.
Lyapunov-stable neural control for state and output feedback: A novel formulation
Lujie Yang, Hongkai Dai, Zhouxing Shi, Cho-Jui Hsieh, Russ Tedrake, and Huan Zhang · 2024
Closest in time.
Scalable neural network verification with branch-and-bound inferred cutting planes
Duo Zhou, Christopher Brix, Grani A Hanasusanto, and Huan Zhang · 2024
Closest in time.
Testing neural network verifiers: A soundness benchmark with hidden counterexamples
Xingjian Zhou, Hongji Xu, Andy Xu, Zhouxing Shi, Cho-Jui Hsieh, and Huan Zhang · 2024
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