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Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations.
In: Proc. 12th IEEE Int. Conf. on Computer Vision (ICCV)
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In: Proc. 27th Int. Conf. on Machine Learning (ICML)
V. Nair & G. Hinton (2010): Rectified Linear Units Improve Restricted Boltzmann Machines · 2010
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In: Proc. 22nd Int. Conf. on Computer Aided Verification (CAV)
L. Pulina & A. Tacchella (2010): An Abstraction-Refinement Approach to Verification of Artificial Neural Networks · 2010
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In: Proc. 14th Int. Conf. on Artificial Intelligence and Statistics (AISTATS)
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IEEE Signal Processing Magazine
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L. Pulina & A. Tacchella (2012): Challenging SMT Solvers to Verify Neural Networks · 2012
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In: Proc. 30th Int. Conf. on Machine Learning (ICML)
A. Maas, A. Hannun & A. Ng (2013): Rectifier Nonlinearities improve Neural Network Acoustic Models · 2013
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Technical Report. http://arxiv.org/abs/1312.6199
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow & R. Fergus (2013): Intriguing Properties of Neural Networks · 2013
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IEEE Transactions on Robotics
M. Althoff & J. Dolan (2014): Online Verification of Automated Road Vehicles using Reachability Analysis · 2014
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Technical Report. http://arxiv.org/abs/1412.6572
I. Goodfellow, J. Shlens & C. Szegedy (2014): Explaining and Harnessing Adversarial Examples · 2014
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In: Proc. 21st Int. Conf. on Tools and Algorithms for the Construction and Analysis of Systems (TACAS)
J.-B. Jeannin, K Ghorbal, Y. Kouskoulas, R. Gardner, A. Schmidt, E. Zawadzki & A. Platzer (2015): A Formally Verified Hybrid System for the Next-Generation Airborne Collision Avoidance System · 2015
MIT Press
I. Goodfellow, Y. Bengio & A. Courville (2016): Deep Learning · 2016
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Technical Report. http://arxiv.org/abs/1610.06940
X. Huang, M. Kwiatkowska, S. Wang & M. Wu (2016): Safety Verification of Deep Neural Networks · 2016
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In: Proc. 35th Digital Avionics Systems Conf. (DASC)
K. Julian, J. Lopez, J. Brush, M. Owen & M. Kochenderfer (2016): Policy Compression for Aircraft Collision Avoidance Systems · 2016
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Technical Report. http://arxiv.org/abs/1607.02533
A. Kurakin, I. Goodfellow & S. Bengio (2016): Adversarial Examples in the Physical World · 2016
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In: Proc. 38th Symposium on Security and Privacy (SP)
N. Carlini & D. Wagner (2017): Towards Evaluating the Robustness of Neural Networks · 2017
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Cited alongside, same era.
In: Proc. 30th Conf. on Neural Information Processing Systems (NIPS)
O Bastani, Y. Ioannou, L. Lampropoulos, D. Vytiniotis, A. Nori & A. Criminisi (2016): Measuring Neural Net Robustness with Constraints · 2016
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Technical Report. http://arxiv.org/abs/1604.07316
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao & K. Zieba (2016): End to End Learning for Self-Driving Cars · 2016
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In: Proc. 29th Int. Conf. on Computer Aided Verification (CAV)
G. Katz, C. Barrett, D. Dill, K. Julian & M. Kochenderfer (2017): Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks · 2017
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