Verifying Deep-RL-Driven Systems
暂无分享,去创建一个
Michael Schapira | Guy Katz | Clark W. Barrett | Yafim Kazak | Guy Katz | Michael Schapira | Yafim Kazak
[1] Mykel J. Kochenderfer,et al. Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks , 2017, CAV.
[2] Hongzi Mao,et al. Neural Adaptive Video Streaming with Pensieve , 2017, SIGCOMM.
[3] Srikanth Kandula,et al. Resource Management with Deep Reinforcement Learning , 2016, HotNets.
[4] Armando Solar-Lezama,et al. Verifiable Reinforcement Learning via Policy Extraction , 2018, NeurIPS.
[5] Swarat Chaudhuri,et al. AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation , 2018, 2018 IEEE Symposium on Security and Privacy (SP).
[6] Corina S. Pasareanu,et al. DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks , 2017, ArXiv.
[7] Clark W. Barrett,et al. Provably Minimally-Distorted Adversarial Examples , 2017 .
[8] Min Wu,et al. Safety Verification of Deep Neural Networks , 2016, CAV.
[9] Brighten Godfrey,et al. Internet Congestion Control via Deep Reinforcement Learning , 2018, ArXiv.
[10] Adnan Darwiche,et al. Compiling Neural Networks into Tractable Boolean Circuits , 2019 .
[11] Alexander Aiken,et al. From invariant checking to invariant inference using randomized search , 2014, Formal Methods Syst. Des..
[12] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[13] Mykel J. Kochenderfer,et al. Toward Scalable Verification for Safety-Critical Deep Networks , 2018, ArXiv.
[14] Mykel J. Kochenderfer,et al. The Marabou Framework for Verification and Analysis of Deep Neural Networks , 2019, CAV.
[15] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[16] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[17] Pierre Geurts,et al. Tree-Based Batch Mode Reinforcement Learning , 2005, J. Mach. Learn. Res..
[18] Corina S. Pasareanu,et al. DeepSafe: A Data-Driven Approach for Assessing Robustness of Neural Networks , 2018, ATVA.
[19] Mykel J. Kochenderfer,et al. Algorithms for Verifying Deep Neural Networks , 2019, Found. Trends Optim..
[20] Eduardo F. Morales,et al. An Introduction to Reinforcement Learning , 2011 .
[21] Russ Tedrake,et al. Evaluating Robustness of Neural Networks with Mixed Integer Programming , 2017, ICLR.
[22] Ion Stoica,et al. Learning to Optimize Join Queries With Deep Reinforcement Learning , 2018, ArXiv.
[23] Sriram Sankaranarayanan,et al. Reachability analysis for neural feedback systems using regressive polynomial rule inference , 2019, HSCC.
[24] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[25] John Wawrzynek,et al. AutoPhase: Compiler Phase-Ordering for HLS with Deep Reinforcement Learning , 2019, 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM).
[26] Junfeng Yang,et al. Formal Security Analysis of Neural Networks using Symbolic Intervals , 2018, USENIX Security Symposium.
[27] Sergey Levine,et al. Trust Region Policy Optimization , 2015, ICML.
[28] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[29] Armin Biere,et al. Symbolic Model Checking without BDDs , 1999, TACAS.
[30] Mykel J. Kochenderfer,et al. Towards Proving the Adversarial Robustness of Deep Neural Networks , 2017, FVAV@iFM.
[31] Yasser Shoukry,et al. Formal verification of neural network controlled autonomous systems , 2018, HSCC.
[32] Xin Jin,et al. Neural packet classification , 2019, SIGCOMM.
[33] Dafna Shahaf,et al. Learning to Route , 2017, HotNets.