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Researchers have developed neural network verification algorithms motivated by the need to characterize the robustness of deep neural networks.
Miné, A.: Relational abstract domains for the detection of floating-point run-time errors. In: European Symposium on Programming, pp. 3–17, Springer (2004)
2004
Earlier work this paper cites.
Neumaier, A., Shcherbina, O.: Safe bounds in linear and mixed-integer linear programming. Mathematical Programming
2004
Earlier work this paper cites.
IEEE: IEEE standard for floating-point arithmetic. IEEE Std 754-2008 pp. 1–70 (2008)
2008
Earlier work this paper cites.
Rümmer, P., Wahl, T.: An smt-lib theory of binary floating-point arithmetic. In: International Workshop on Satisfiability Modulo Theories (SMT), p. 151 (2010)
2010
Earlier work this paper cites.
Corzilius, F., Loup, U., Junges, S., Ábrahám, E.: Smt-rat: an smt-compliant nonlinear real arithmetic toolbox. In: International Conference on Theory and Applications of Satisfiability Testing, pp. 442–448, Springer (2012)
2012
Earlier work this paper cites.
Steffy, D.E., Wolter, K.: Valid linear programming bounds for exact mixed-integer programming. INFORMS Journal on Computing
2013
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2014
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Szegedy, C., Zaremba, W., Sutskever, I., Estrach, J.B., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. In: ICLR (2014)
2014
Earlier work this paper cites.
Scheibler, K., Winterer, L., Wimmer, R., Becker, B.: Towards verification of artificial neural networks. In: MBMV, pp. 30–40 (2015)
2015
Earlier work this paper cites.
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: NeurIPS, pp. 4107–4115, Curran Associates, Inc. (2016)
2016
Earlier work this paper cites.
Lavin, A., Gray, S.: Fast algorithms for convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4013–4021 (2016)
2016
Earlier work this paper cites.
Boldo, S., Melquiond, G.: Computer Arithmetic and Formal Proofs: Verifying Floating-point Algorithms with the Coq System. Elsevier (2017)
2017
Earlier work this paper cites.
Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 ieee symposium on security and privacy (sp), pp. 39–57, IEEE (2017)
2017
Earlier work this paper cites.
Cheng, C.H., Nührenberg, G., Ruess, H.: Maximum resilience of artificial neural networks. In: International Symposium on Automated Technology for Verification and Analysis, pp. 251–268, Springer (2017)
2017
Earlier work this paper cites.
Ehlers, R.: Formal verification of piece-wise linear feed-forward neural networks. In: International Symposium on Automated Technology for Verification and Analysis, pp. 269–286, Springer (2017)
2017
Earlier work this paper cites.
Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: International Conference on Computer Aided Verification, pp. 3–29, Springer (2017)
2017
Earlier work this paper cites.
Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: An efficient smt solver for verifying deep neural networks. In: International Conference on Computer Aided Verification, pp. 97–117, Springer (2017)
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Abtahi, T., Shea, C., Kulkarni, A., Mohsenin, T.: Accelerating convolutional neural network with FFT on embedded hardware. IEEE Transactions on Very Large Scale Integration (VLSI) Systems
2018
Cited alongside, same era.
Dutta, S., Jha, S., Sankaranarayanan, S., Tiwari, A.: Output range analysis for deep feedforward neural networks. In: NASA Formal Methods Symposium, pp. 121–138, Springer (2018)
2018
Cited alongside, same era.
Zhang, H., Weng, T.W., Chen, P.Y., Hsieh, C.J., Daniel, L.: Efficient neural network robustness certification with general activation functions. In: NeurIPS, pp. 4939–4948, Curran Associates, Inc. (2018)
2018
Later among the works it cites.
Burgess, N., Milanovic, J., Stephens, N., Monachopoulos, K., Mansell, D.: Bfloat16 processing for neural networks. In: 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), pp. 88–91, IEEE (2019)
2019
Later among the works it cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: PyTorch: An imperative style, high-performance deep learning library. In: NeurIPS, pp. 8024–8035, Curran Associates, Inc. (2019)
2019
Later among the works it cites.
Salman, H., Yang, G., Zhang, H., Hsieh, C.J., Zhang, P.: A convex relaxation barrier to tight robustness verification of neural networks. In: NeurIPS, pp. 9832–9842 (2019)
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2018
Cited alongside, same era.
Fischetti, M., Jo, J.: Deep neural networks and mixed integer linear optimization. Constraints
2018
Cited alongside, same era.
Gehr, T., Mirman, M., Drachsler-Cohen, D., Tsankov, P., Chaudhuri, S., Vechev, M.: Ai2: Safety and robustness certification of neural networks with abstract interpretation. In: 2018 IEEE Symposium on Security and Privacy (SP), pp. 3–18, IEEE (2018)
2018
Cited alongside, same era.
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2018)
2018
Cited alongside, same era.
Mirman, M., Gehr, T., Vechev, M.: Differentiable abstract interpretation for provably robust neural networks. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 80, pp. 3578–3586, PMLR, Stockholmsmässan, Stockholm Sweden (10–15 Jul 2018)
2018
Cited alongside, same era.
Narodytska, N., Kasiviswanathan, S., Ryzhyk, L., Sagiv, M., Walsh, T.: Verifying properties of binarized deep neural networks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
2018
Cited alongside, same era.
Raghunathan, A., Steinhardt, J., Liang, P.S.: Semidefinite relaxations for certifying robustness to adversarial examples. In: NeurIPS, pp. 10877–10887, Curran Associates, Inc. (2018)
2018
Cited alongside, same era.
Singh, G., Gehr, T., Mirman, M., Püschel, M., Vechev, M.: Fast and effective robustness certification. In: NeurIPS, pp. 10802–10813, Curran Associates, Inc. (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
Shih, A., Darwiche, A., Choi, A.: Verifying binarized neural networks by angluin-style learning. In: International Conference on Theory and Applications of Satisfiability Testing, pp. 354–370, Springer (2019)
2019
Later among the works it cites.
Singh, G., Gehr, T., Püschel, M., Vechev, M.T.: An abstract domain for certifying neural networks. Proceedings of the ACM on Programming Languages
2019
Later among the works it cites.
Tjeng, V., Xiao, K.Y., Tedrake, R.: Evaluating robustness of neural networks with mixed integer programming. In: ICLR (2019)
2019
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Tramer, F., Boneh, D.: Adversarial training and robustness for multiple perturbations. In: NeurIPS, pp. 5866–5876, Curran Associates, Inc. (2019)
2019
Later among the works it cites.
Xiao, K.Y., Tjeng, V., Shafiullah, N.M.M., Madry, A.: Training for faster adversarial robustness verification via inducing reLU stability. In: ICLR (2019)
2019
Later among the works it cites.
Bunel, R., Lu, J., Turkaslan, I., Kohli, P., Torr, P., Mudigonda, P.: Branch and bound for piecewise linear neural network verification. Journal of Machine Learning Research
2020
Closest in time.
Das, A., Briggs, I., Gopalakrishnan, G., Krishnamoorthy, S., Panchekha, P.: Scalable yet rigorous floating-point error analysis. In: SC20, pp. 1–14, IEEE (2020)
2020
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2020
Closest in time.
Gurobi Optimization, L.: Gurobi optimizer reference manual (2020), URL
2020
Closest in time.
Jia, K., Rinard, M.: Efficient exact verification of binarized neural networks. In: NeurIPS, vol. 33, pp. 1782–1795, Curran Associates, Inc. (2020)
2020
Closest in time.
Raghu, M., Schmidt, E.: A survey of deep learning for scientific discovery. ArXiv
2020
Closest in time.
Wong, E., Rice, L., Kolter, J.Z.: Fast is better than free: Revisiting adversarial training. In: ICLR (2020)
2020
Closest in time.