Understand
The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability.
- Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state of the art is limited to small systems.
- In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification.
Built on
An Abstraction-refinement Approach to Verification of Artificial Neural Networks. In Proceedings of the 22nd International Conference on Computer Aided Verification (CAV’10) . Springer-Verlag, Berlin, Heidelberg, 243–257
Luca Pulina and Armando Tacchella. 2010 · 2010
Earlier work this paper cites.
A Domain-Specific Approach to Heterogeneous Parallelism. In Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming (PPoPP ’11) . ACM, New York, NY, USA, 35–46
Hassan Chafi, Arvind K. Sujeeth, Kevin J. Brown, HyoukJoong Lee, Anand R. Atreya, and Kunle Olukotun. 2011 · 2011
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2013 · 2013
Earlier work this paper cites.
Song Han, Huizi Mao, and William J. Dally. 2015 · 2015
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016 · 2016
Earlier work this paper cites.
Similar
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016 · 2016
Cited alongside, same era.
Policy Compression for Aircraft Collision Avoidance Systems. In Digital Avionics Systems Conference (DASC)
Kyle Julian, Jessica Lopez, Jeffrey S. Brush, Michael Owen, and Mykel J. Kochenderfer. 2016 · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2016 · 2016
Cited alongside, same era.
DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices. In 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) . 1–12
N. D. Lane, S. Bhattacharya, P. Georgiev, C. Forlivesi, L. Jiao, L. Qendro, and F. Kawsar. 2016 · 2016
Cited alongside, same era.
Then
Verification of Binarized Neural Networks
Chih-Hong Cheng, Georg Nührenberg, and Harald Ruess. 2017 · 2017
Later among the works it cites.
Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
Rüdiger Ehlers. 2017 · 2017
Later among the works it cites.
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks. In Computer Aided Verification - 29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017, Proceedings, Part I . 97–117
Guy Katz, Clark W. Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer. 2017 · 2017
Later among the works it cites.
Verifying Properties of Binarized Deep Neural Networks
N. Narodytska, S. P. Kasiviswanathan, L. Ryzhyk, M. Sagiv, and T. Walsh. 2017 · 2017
Later among the works it cites.
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