Formal Specification for Deep Neural Networks

1. Albarghouthi, A., D’Antoni, L., Drews, S., Nori, A.: Fairness as a program property (2016), arXiv:1610.06067

2. Albarghouthi, A., D’Antoni, L., Drews, S., Nori, A.V.: Fairsquare: probabilistic verification of program fairness. In: Proceedings of the ACM on Programming Languages (2017)

   Article  Google Scholar 

3. Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J.: Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015)

   Article  Google Scholar 

4. Amodei, D., Olah, C., Steinhardt, J., Christiano, P.F., Schulman, J., Mané, D.: Concrete problems in AI safety. ArXiV e-prints abs/1606.06565 (2016)

5. Bastani, O., Ioannou, Y., Lampropoulos, L., Vytiniotis, D., Nori, A., Criminisi, A.: Measuring neural net robustness with constraints. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems (NIPS), vol. 29, pp. 2613–2621. MIT Press, Cambridge (2016)

   Google Scholar 

6. Binns, R.: Fairness in machine learning: lessons from political philosophy (2017), arXiv:1712.03586

7. Bojarski, M., et al.: End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)

8. Cai, J., Shin, R., Song, D.: Making neural programming architectures generalize via recursion. arXiv preprint arXiv:1704.06611 (2017)

9. Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: IEEE Symposium on Security and Privacy (SP) (2017)

   Google Scholar 

10. Carpenter, B., et al.: Stan: a probabilistic programming language. J. Stat. Softw. 76(1), 1–32 (2017)

    Article  Google Scholar 

11. Cheng, C.-H., Nührenberg, G., Ruess, H.: Maximum resilience of artificial neural networks. In: D’Souza, D., Narayan Kumar, K. (eds.) ATVA 2017. LNCS, vol. 10482, pp. 251–268. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68167-2_18

    Chapter  Google Scholar 

12. Clarkson, M.R., Schneider, F.B.: Hyperproperties. J. Comput. Secur. 18(6), 1157–1210 (2010)

    Article  Google Scholar 

13. Dahl, G.E., Stokes, J.W., Deng, L., Yu, D.: Large-scale malware classification using random projections and neural networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 3422–3426. IEEE (2013)

    Google Scholar 

14. Dai, J., et al.: Deformable convolutional networks. In: IEEE International Conference on Computer Vision (2017)

    Google Scholar 

15. Dreossi, T., Donzé, A., Seshia, S.A.: Compositional falsification of cyber-physical systems with machine learning components. In: NASA Formal Methods Symposium (2017)

    Google Scholar 

16. Dreossi, T., Donzé, A., Seshia, S.A.: Compositional falsification of cyber-physical systems with machine learning components. In: Barrett, C., Davies, M., Kahsai, T. (eds.) NFM 2017. LNCS, vol. 10227, pp. 357–372. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57288-8_26

    Chapter  Google Scholar 

17. Dreossi, T., Ghosh, S., Yue, X., Keutzer, K., Sangiovanni-Vincentelli, A., Seshia, S.A.: Counterexample-guided data augmentation. In: 27th International Joint Conference on Artificial Intelligence (IJCAI) (2018)

    Google Scholar 

18. Dreossi, T., Jha, S., Seshia, S.A.: Semantic adversarial deep learning. In: 30th International Conference on Computer Aided Verification (CAV) (2018)

    Chapter  Google Scholar 

19. Dutta, S., Jha, S., Sanakaranarayanan, S., Tiwari, A.: Output range analysis for deep neural networks (2017), arXiv:1709.09130

20. Dvijotham, K., Stanforth, R., Gowal, S., Mann, T., Kohli, P.: A dual approach to scalable verification of deep networks (2018), arXiv:1803.06567

21. Fawzi, A., Frossard, P.: Manitest: Are classifiers really invariant? (2017), arXiv:1507.06535

22. Fremont, D., Yue, X., Dreossi, T., Ghosh, S., Sangiovanni-Vincentelli, A.L., Seshia, S.A.: Scenic: Language-based scene generation. Technical report UCB/EECS-2018-8. EECS Department, University of California, Berkeley, April 2018. http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-8.html

23. Friedler, S.A., Scheidegger, C., Venkatasubramanian, S.: On the (im) possibility of fairness (2016), arXiv:1609.07236

24. Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., Roth, D.: A comparative study of fairness-enhancing interventions in machine learning (2018), arXiv:1802.04422

25. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://goodfeli.github.io/dlbook/

26. Goodfellow, I., Lee, H., Le, Q.V., Saxe, A., Ng, A.Y.: Measuring invariances in deep networks. In: Advances in Neural Information Processing Systems (2009)

    Google Scholar 

27. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples (2014), arXiv:1412.6572

28. Goodman, N.D., Mansinghka, V.K., Roy, D., Bonawitz, K., Tenenbaum, J.B.: Church: a language for generative models. In: Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, pp. 220–229. UAI’08 (2008)

    Google Scholar 

29. Gordon, A.D., Henzinger, T.A., Nori, A.V., Rajamani, S.K.: Probabilistic programming. In: FOSE 2014, pp. 167–181. ACM (2014)

    Google Scholar 

30. Graves, A., Wayne, G., Danihelka, I.: Neural turing machines. arXiv preprint arXiv:1410.5401 (2014)

31. Hardt, M., Price, E., Srebro, N., et al.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems (2016)

    Google Scholar 

32. Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: Majumdar, R., Kunčak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 3–29. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63387-9_1

    Chapter  Google Scholar 

33. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

34. Kanbak, C., Moosavi-Dezfooli, S.M., Frossard, P.: Geometric robustness of deep networks: analysis and improvement (2017), arXiv:1711.09115

35. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

36. Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. In: Advances in Neural Information Processing Systems (2017)

    Google Scholar 

37. Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision (1999)

    Google Scholar 

38. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks (2017), arXiv:1706.06083

39. Milch, B., Marthi, B., Russell, S.: Blog: Relational modeling with unknown objects. In: ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields, pp. 67–73 (2004)

    Google Scholar 

40. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529 (2015)

    Article  Google Scholar 

41. NVIDIA: Nvidia tegra drive px: Self-driving car computer (2015), http://www.nvidia.com/object/drive-px.html

42. Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: Proceedings of the 1st IEEE European Symposium on Security and Privacy. arXiv preprint arXiv:1511.07528 (2016)

43. Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. arXiv preprint arXiv:1511.04508 (2015)

44. Pei, K., Cao, Y., Yang, J., Jana, S.: Deepxplore: automated whitebox testing of deep learning systems. In: Proceedings of the 26th Symposium on Operating Systems Principles, pp. 1–18. ACM (2017)

    Google Scholar 

45. Pfeffer, A.: Figaro: an object-oriented probabilistic programming language. Technical report, Charles River Analytics (2009)

    Google Scholar 

46. Rodrigues, P., Costa, J.F., Siegelmann, H.T.: Verifying properties of neural networks. In: Mira, J., Prieto, A. (eds.) IWANN 2001. LNCS, vol. 2084, pp. 158–165. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45720-8_19

    Chapter  Google Scholar 

47. Russell, S., et al.: Letter to the editor: research priorities for robust and beneficial artif icial intelligence: an open letter. AI Mag. 36(4), 3–4 (2015)

    Article  Google Scholar 

48. Sadigh, D., Kim, E.S., Coogan, S., Sastry, S., Seshia, S.A.: A learning based approach to control synthesis of markov decision processes for linear temporal logic specifications. In: Proceedings of the 53rd IEEE Conference on Decision and Control (CDC), pp. 1091–1096, December 2014

    Google Scholar 

49. Selsam, D., Liang, P., Dill, D.L.: Developing bug-free machine learning systems with formal mathematics. In: International Conference on Machine Learning, pp. 3047–3056 (2017)

    Google Scholar 

50. Seshia, S.A.: Compositional verification without compositional specification for learning-based systems. Technical report UCB/EECS-2017-164. EECS Department, University of California, Berkeley, November 2017. http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-164.html

51. Seshia, S.A., et al.: Formal specification for deep neural networks. Technical report UCB/EECS-2018-25. EECS Department, University of California, Berkeley, May 2018. http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-25.html

52. Seshia, S.A., Sadigh, D., Sastry, S.S.: Towards Verified Artificial Intelligence. ArXiv e-prints, July 2016

    Google Scholar 

53. Shin, E.C.R., Song, D., Moazzezi, R.: Recognizing functions in binaries with neural networks. In: 24th USENIX Security Symposium (USENIX Security 15), pp. 611–626 (2015)

    Google Scholar 

54. Szegedy, C., et al.: Intriguing properties of neural networks (2013), arXiv:1312.6199

55. Taylor, B.J., Darrah, M.A.: Rule extraction as a formal method for the verification and validation of neural networks. In: IEEE International Joint Conference on Neural Networks (IJCNN), vol. 5, pp. 2915–2920. IEEE (2005)

    Google Scholar 

56. Terauchi, T., Aiken, A.: Secure information flow as a safety problem. In: Hankin, C., Siveroni, I. (eds.) SAS 2005. LNCS, vol. 3672, pp. 352–367. Springer, Heidelberg (2005). https://doi.org/10.1007/11547662_24

    Chapter  Google Scholar 

57. Wen, M., Ehlers, R., Topcu, U.: Correct-by-synthesis reinforcement learning with temporal logic constraints. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4983–4990 (2015)

    Google Scholar 

58. Weng, T.W., et al.: Evaluating the robustness of neural networks: an extreme value theory approach (2018), arXiv:1801.10578

59. You, S., Ding, D., Canini, K., Pfeifer, J., Gupta, M.: Deep lattice networks and partial monotonic functions. In: Advances in Neural Information Processing Systems (2017)

    Google Scholar 

Download references