참고문헌 (55)

1. 1. B. Amos, I. D. Jimenez Rodriguez, J. Sacks, B. Boots, and J. Z. Kolter, Differentiable MPC for end-to-end planning and control , in Advances in Neural Information Processing Systems, 2018, pp. 8289--8300. 

2. 2. B. Amos and J. Z. Kolter, OptNet: Differentiable optimization as a layer in neural networks , in Proceedings of the 34th International Conference on Machine Learning, PMLR 70, JMLR.org, 2017, pp. 136--145. 

3. 3. S. Anwar, K. Hwang, and W. Sung, Structured pruning of deep convolutional neural networks , ACM J. Emerging Technologies Comput. Systems (JETC), 13 (2017), 32. 

4. 4. A. Askari, G. Negiar, R. Sambharya, and L. El Ghaoui, Lifted Neural Networks , preprint, https://arxiv.org/abs/1805.01532 , 2018. 

5. 5. A. Athalye, N. Carlini, and D. A. Wagner, Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples , in Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsma？ssan, Stockholm, Sweden, 2018, Proc. Mach. Learn. Res. 80, PMLR, 2018, pp. 274--283. 

6. 6. S. Bai, J. Z. Kolter, and V. Koltun, Deep Equilibrium Models , preprint, https://arxiv.org/abs/1909.01377 , 2019. 

7. 7. S. Bai, V. Koltun, and J. Z. Kolter, Multiscale deep equilibrium models , in Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020, pp. 5238--5250. 

8. 8. N. Carlini and D. A. Wagner, Adversarial examples are not easily detected: Bypassing ten detection methods , in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, AISec'17, Dallas, TX, 2017, ACM, 2017, pp. 3--14. 

9. 9. M. Carreira-Perpinan and W. Wang, Distributed optimization of deeply nested systems , in Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, S. Kaski and J. Corander, eds., Reykjavik, Iceland, 2014, Proc. Mach. Learn. Res. 33, PMLR, pp. 10--19, http://proceedings.mlr.press/v33/carreira-perpinan14.html . 

10. 10. S. Changpinyo, M. Sandler, and A. Zhmoginov, The Power of Sparsity in Convolutional Neural Networks , preprint, https://arxiv.org/abs/1702.06257 , 2017. 

11. 11. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, Neural ordinary differential equations , in Advances in Neural Information Processing Systems 31, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, eds., Curran Associates, Inc., 2018, pp. 6571--6583, http://papers.nips.cc/paper/7892-neural-ordinary-differential-equations.pdf . 

12. 12. J. M. Cohen, E. Rosenfeld, and J. Z. Kolter, Certified Adversarial Robustness via Randomized Smoothing , preprint, https://arxiv.org/abs/arXiv:1902.02918 , 2019. 

13. 13. M. A. Dahleh and I. J. Diaz-Bobillo, Control of Uncertain Systems: A Linear Programming Approach , Prentice-Hall, Inc., 1994. 

14. 14. F. de Avila Belbute-Peres, K. Smith, K. Allen, J. Tenenbaum, and J. Z. Kolter, End-to-end differentiable physics for learning and control , in Advances in Neural Information Processing Systems 31, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, eds., Curran Associates, Inc., 2018, pp. 7178--7189, http://papers.nips.cc/paper/7948-end-to-end-differentiable-physics-for-learning-and-control.pdf . 

15. 15. P. Donti, B. Amos, and J. Z. Kolter, Task-based end-to-end model learning in stochastic optimization , in Advances in Neural Information Processing Systems, 2017, pp. 5484--5494. 

16. 16. U. Evci, F. Pedregosa, A. Gomez, and E. Elsen, The Difficulty of Training Sparse Neural Networks , preprint, https://arxiv.org/abs/1906.10732 , 2019. 

17. 17. V. Ganapathiraman, Z. Shi, X. Zhang, and Y. Yu, Inductive two-layer modeling with parametric Bregman transfer , in International Conference on Machine Learning, PMLR, 2018, pp. 1636--1645. 

18. 18. B. Gao and L. Pavel, On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning , preprint, https://arxiv.org/abs/1704.00805 , 2017. 

19. 19. I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples , in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, 2015, Conference Track Proceedings; available online at http://arxiv.org/abs/1412.6572 . 

20. 20. S. Gowal, K. Dvijotham, R. Stanforth, R. Bunel, C. Qin, J. Uesato, R. Arandjelovic, T. A. Mann, and P. Kohli, On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models , preprint, http://arxiv.org/abs/1810.12715 , 2018. 

21. 21. F. Gu, A. Askari, and L. El Ghaoui, Fenchel lifted networks: A Lagrange relaxation of neural network training , in International Conference on Artificial Intelligence and Statistics, PMLR, 2020, pp. 3362--3371. 

22. 22. F. Gu, H. Chang, W. Zhu, S. Sojoudi, and L. El Ghaoui, Implicit graph neural networks , in Advances in Neural Information Processing Systems 33, 2020. 

23. 23. S. Han, H. Mao, and W. J. Dally, Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding , preprint, https://arxiv.org/abs/1510.00149 , 2015. 

24. 24. B. Hassibi, D. G. Stork, and G. J. Wolff, Optimal Brain Surgeon and general network pruning , in IEEE International Conference on Neural Networks, IEEE, 1993, pp. 293--299. 

25. 25. K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition , in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770--778. 

26. 26. M. H. Khammash, Stability and Performance Robustness of Discrete-Time Systems with Structured Uncertainty , Ph.D. thesis, Rice University, Houston, TX, 1990. 

27. 27. J. Kolter, private communication with A. Askari , 2019. 

28. 28. A. Kurakin, I. J. Goodfellow, and S. Bengio, Adversarial examples in the physical world , in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 2017, Workshop Track Proceedings, OpenReview.net, 2017, https://openreview.net/forum?id=HJGU3Rodl . 

29. 29. A. Kurakin, I. J. Goodfellow, and S. Bengio, Adversarial Machine Learning at Scale , preprint, https://arxiv.org/abs/1611.01236 , 2017. 

30. 30. V. Lebedev and V. Lempitsky, Fast ConvNets using group-wise brain damage , in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2554--2564. 

31. 31. Y. LeCun, J. S. Denker, and S. A. Solla, Optimal brain damage , in Advances in Neural Information Processing Systems, 1990, pp. 598--605. 

32. 32. J. Li, C. Fang, and Z. Lin, Lifted proximal operator machines , in Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 4181--4188. 

33. 33. Y. Liu, X. Chen, C. Liu, and D. Song, Delving into transferable adversarial examples and black-box attacks , in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 2017, Conference Track Proceedings, OpenReview.net, 2017, https://openreview.net/forum?id=Sys6GJqxl . 

34. 34. C. Louizos, M. Welling, and D. P. Kingma, Learning Sparse Neural Networks through $ L_0 $ Regularization , preprint, https://arxiv.org/abs/1712.01312 , 2017. 

35. 35. A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, Towards deep learning models resistant to adversarial attacks , in International Conference on Learning Representations, 2018, https://openreview.net/forum?id=rJzIBfZAb . 

36. 36. C. D. Meyer, Matrix Analysis and Applied Linear Algebra , SIAM, Philadelphia, 2000. 

37. 37. S. Narang, E. Elsen, G. Diamos, and S. Sengupta, Exploring Sparsity in Recurrent Neural Networks , preprint, https://arxiv.org/abs/1704.05119 , 2017. 

38. 38. N. Papernot, P. D. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, The limitations of deep learning in adversarial settings , in IEEE European Symposium on Security and Privacy, EuroS&P 2016, Saarbru？cken, Germany, 2016, IEEE, 2016, pp. 372--387, https://doi.org/10.1109/EuroSP.2016.36 . 

    

39. 39. N. Papernot, P. D. McDaniel, X. Wu, S. Jha, and A. Swami, Distillation as a defense to adversarial perturbations against deep neural networks , in IEEE Symposium on Security and Privacy, SP 2016, San Jose, CA, 2016, IEEE Computer Society, 2016, pp. 582--597, https://doi.org/10.1109/SP.2016.41 . 

    

40. 40. A. Raghunathan, J. Steinhardt, and P. S. Liang, Semidefinite relaxations for certifying robustness to adversarial examples , in Advances in Neural Information Processing Systems, 2018, pp. 10877--10887. 

41. 41. S. Sastry, Nonlinear Systems: Analysis, Stability, and Control , Interdiscip. Appl. Math. 10, Springer Science & Business Media, 2013. 

42. 42. A. See, M.-T. Luong, and C. D. Manning, Compression of Neural Machine Translation Models via Pruning , preprint, https://arxiv.org/abs/1606.09274 , 2016. 

43. 43. U. Shaham, Y. Yamada, and S. Negahban, Understanding adversarial training: Increasing local stability of neural nets through robust optimization , Neurocomputing, 307 (2018), pp. 195--204, https://doi.org/10.1016/j.neucom.2018.04.027 . 

    상세보기

44. 44. K. Simonyan, A. Vedaldi, and A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps , in 2nd International Conference on Learning Representations, ICLR 2014, Banff, Canada, 2014, Y. Bengio and Y. LeCun, eds., Workshop Track Proceedings; available online at http://arxiv.org/abs/1312.6034 . 

45. 45. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting , J. Mach, Learn. Res., 15 (2014), pp. 1929--1958. 

46. 46. G. Taylor, R. Burmeister, Z. Xu, B. Singh, A. Patel, and T. Goldstein, Training neural networks without gradients: A scalable ADMM approach , in International Conference on Machine Learning, 2016, pp. 2722--2731. 

47. 47. B. Travacca, L. El Ghaoui, and S. Moura, Implicit optimization: Models and methods , in 2020 59th IEEE Conference on Decision and Control (CDC), 2020, pp. 408--415, https://doi.org/10.1109/CDC42340.2020.9304169 . 

    

48. 48. P.-W. Wang, P. Donti, B. Wilder, and Z. Kolter, SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver , in Proceedings of the 36th International Conference on Machine Learning, K. Chaudhuri and R. Salakhutdinov, eds., Proc. Mach. Learn. Res. 97, Long Beach, CA, 2019, PMLR, pp. 6545--6554, http://proceedings.mlr.press/v97/wang19e.html . 

49. 49. E. Winston and J. Z. Kolter, Monotone operator equilibrium networks , in Advances in Neural Information Processing Systems 33, 2020, pp. 10718--10728. 

50. 50. E. Wong and J. Z. Kolter, Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope , preprint, https://arxiv.org/abs/1711.00851 , 2017. 

51. 51. H. Yin, P. Seiler, and M. Arcak, Stability Analysis Using Quadratic Constraints for Systems with Neural Network Controllers , preprint, https://arxiv.org/abs/2006.07579 , 2020. 

52. 52. H. Yin, P. Seiler, M. Jin, and M. Arcak, Imitation Learning with Stability and Safety Guarantees , preprint, https://arxiv.org/abs/2012.09293 , 2020. 

53. 53. J. Zeng, T. T.-K. Lau, S. Lin, and Y. Yao, Global Convergence of Block Coordinate Descent in Deep Learning , preprint, https://arxiv.org/abs/1803.00225 , 2018. 

54. 54. Z. Zhang and M. Brand, Convergent block coordinate descent for training Tikhonov regularized deep neural networks , in Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, Curran Associates Inc., 2017, pp. 1719--1728. 

55. 55. M. Zhu and S. Gupta, To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression , preprint, https://arxiv.org/abs/1710.01878 , 2017.