He, Okay., Zhang, X., Ren, S. & Sun, J. Deep residual studying for picture recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27–30, 2016, 770–778 (IEEE, 2016).Saxe, A. M. et al. On random weights and unsupervised function studying. In Proceedings of the twenty eighth International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28–July 2, 2011, (eds Getoor, L. & Scheffer, T.) 1089–1096 (Omnipress, 2011).LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based studying utilized to doc recognition. Proc. IEEE 86, 2278–2324 (1998).Article
Google Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).Article
Google Scholar
Szegedy, C. et al. Going deeper with convolutions. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7–12, 2015, 1–9 (IEEE, 2015).Liu, H., Simonyan, Okay. & Yang, Y. DARTS: differentiable structure search. seventh International Conference on Learning Representations,ICLR 2019, New Orleans, LA, USA, May 6–9, 2019 (eds. Levine, S. M.,Okay. Livescu)(OpenReview.internet,2019).Dong, X. & Yang, Y. Nas-bench-201: extending the scope of reproducible neural structure search. eighth International Conference on Learning Representations, {ICLR} 2020, Addis Ababa, Ethiopia, April 26–30, 2020. (eds. D. Song, M. W., Okay. Cho) (OpenReview.internet,2019).Xie, S., Kirillov, A., Girshick, R. B. & He, Okay. Exploring randomly wired neural networks for picture recognition. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27–November 2, 2019, 1284–1293 (IEEE, 2019).You, J., Leskovec, J., He, Okay. & Xie, S. Graph construction of neural networks. In Proceedings of the thirty seventh International Conference on Machine Learning, ICML 2020, 13–18 July 2020, Virtual Event, Vol. 119 of Proceedings of Machine Learning Research, 10881–10891 (2020).Zhang, W. et al. Pasca: A graph neural structure search system beneath the scalable paradigm. In Proceedings of the ACM Web Conference 2022 (eds Laforest, F. et al.) 1817–1828 (2022).Ding, Y. et al. Nap: neural structure search with pruning. Neurocomputing 477, 85–95 (2022).Article
Google Scholar
Lu, Z. et al. Neural structure switch. IEEE Trans. Pattern Anal. Mach. Intell. 43, 2971–2989 (2021).Article
Google Scholar
Dera, D., Bouaynaya, N. C., Rasool, G., Shterenberg, R. & Fathallah-Shaykh, H. M. Premium-cnn: propagating uncertainty in the direction of robust convolutional neural networks. IEEE Trans. Signal Processing 69, 4669–4684 (2021).Article
MathSciWeb
MATH
Google Scholar
Ahmed, S. et al. Failure detection in deep neural networks for medical imaging. Front. Med. Technol. 4, 919046 (2022).Flatow, D. & Penner, D. On the Robustness of Convnets to Training on Noisy Labels. Technical report (Stanford University, 2017).Wang, F. et al. The satan of face recognition is within the noise. In Computer Vision – ECCV 2018 – fifteenth European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part IX, Vol. 11213 of Lecture Notes in Computer Science (eds Ferrari, V., Hebert, M., Sminchisescu, C. & Weiss, Y.) 780–795 (Springer, 2018).Bar, O., Drory, A. & Giryes, R. A spectral perspective of DNN robustness to label noise. In International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28–30 March 2022, Virtual Event, Vol. 151 of Proceedings of Machine Learning Research (eds Camps-Valls, G., Ruiz, F. J. R. & Valera, I.) 3732–3752 (PMLR, 2022).Szegedy, C. et al. Intriguing properties of neural networks. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (eds Bengio, Y. & LeCun, Y.) (2014).Apruzzese, G., Andreolini, M., Ferretti, L., Marchetti, M. & Colajanni, M. Modeling lifelike adversarial assaults towards community intrusion detection methods. Digit. Threat.: Res. Pract. 3, 31 (2021).Pierazzi, F., Pendlebury, F., Cortellazzi, J. & Cavallaro, L. Intriguing properties of adversarial ml assaults in the issue house. In 2020 IEEE Symposium on Security and Privacy (SP), 1308–1325 (IEEE Computer Society, 2020).Carlini, N. et al. On evaluating adversarial robustness. Preprint at https://arxiv.org/abs/1902.06705 (2019).Petitcolas, F. A. P. in Kerckhoffs’ precept Encyclopedia of Cryptography and Security, 2nd Ed 675 (eds van Tilborg, H. C. A. & Jajodia, S.) (Springer, 2011).Barabási, A.-L. et al. Network Science (Cambridge University Press, 2016).Tannenbaum, A. et al. Ricci curvature and robustness of most cancers networks. Preprint at https://arxiv.org/abs/1502.04512 (2015).Sandhu, R. et al. Graph curvature for differentiating most cancers networks. Sci. Rep. 5, 1–13 (2015).Article
Google Scholar
Sandhu, R. S., Georgiou, T. T. & Tannenbaum, A. R. Ricci curvature: an financial indicator for market fragility and systemic danger. Sci. Adv. 2, e1501495 (2016).Article
Google Scholar
Farooq, H., Chen, Y., Georgiou, T. T., Tannenbaum, A. & Lenglet, C. Network curvature as an indicator of mind structural connectivity. Nat. Commun. 10, 1–11 (2019).Article
Google Scholar
Farooq, H., Lenglet, C. & Nelson, F. Robustness of mind structural networks is affected in cognitively impaired ms sufferers. Front. Neurol. 11, 1542 (2020).Article
Google Scholar
Freitas, S., Yang, D., Kumar, S., Tong, H. & Chau, D. H. Graph vulnerability and robustness: a survey. In IEEE Transactions on Knowledge and Data Engineering (2022).Xiao, X., Chen, H. & Bogdan, P. Deciphering the producing guidelines and functionalities of complicated networks. Sci. Rep. 11, 1–15 (2021).Article
Google Scholar
Rendón de la Torre, S., Kalda, J., Kitt, R. & Engelbrecht, J. Fractal and multifractal evaluation of complicated networks: Estonian community of funds. European Physical Journal B 90, 1–8 (2017).Article
MathSciWeb
Google Scholar
Ratnayake, P., Weragoda, S., Wansapura, J., Kasthurirathna, D. & Piraveenan, M. Quantifying the robustness of complicated networks with heterogeneous nodes. Mathematics 9, 2769 (2021).Sia, J., Jonckheere, E. & Bogdan, P. Ollivier-Ricci curvature-based methodology to group detection in complicated networks. Sci. Rep. 9, 1–12 (2019).Article
Google Scholar
Vecchio, D. A. et al. Spanning community gels from nanoparticles and graph theoretical evaluation of their construction and properties. Adv. Mater. 34, 2201313 (2022).Elsken, T., Metzen, J. H. & Hutter, F. Neural structure search: a survey. J. Machine Learning Res. 20, 1997–2017 (2019).MathSciWeb
MATH
Google Scholar
Radosavovic, I., Johnson, J., Xie, S., Lo, W. & Dollár, P. On community design areas for visible recognition. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27–November 2, 2019, 1882–1890 (IEEE, 2019).Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).Article
MATH
Google Scholar
Sporns, O. Neuroscience Databases (ed. Kötter, R.) 171–185 (Springer, 2003).Bassett, D. S. & Bullmore, E. Small-world mind networks. Neuroscientist 12, 512–523 (2006).Article
Google Scholar
Erdős, P. & Rényi, A. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci 5, 17–60 (1960).MathSciWeb
MATH
Google Scholar
Albert, R. & Barabási, A.-L. Statistical mechanics of complicated networks. Rev. Mod. Phys. 74, 47 (2002).Article
MathSciWeb
MATH
Google Scholar
Krizhevsky, A. Learning a number of layers of options from tiny photos. University of Toronto. http://www.cs.toronto.edu/kriz/cifar.html, final accessed 2022 (2012).Kaggle. Tiny imagenet. https://www.kaggle.com/c/tiny-imagenet/overview. Accessed: 02-21-2022 (2021).Russakovsky, O. et al. Imagenet massive scale visible recognition problem. Int. J. Comput. Vis. 115, 211–252 (2015).Article
MathSciWeb
Google Scholar
Goodfellow, I. J., Shlens, J. & Szegedy, C. Explaining and harnessing adversarial examples. third International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (eds Bengio, Y. & LeCun, Y.) (2015).Madry, A., Makelov, A., Schmidt, L., Tsipras, D. & Vladu, A. Towards deep studying fashions immune to adversarial assaults. sixth International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 – May 3, 2018, Conference Track Proceedings (eds I. Murray, O. V., M. Ranzato) (Open-Review.internet, 2018).Carlini, N. & Wagner, D. A. Towards evaluating the robustness of neural networks. In 2017 IEEE Symposium on Security and Privacy, SP 2017, San Jose, CA, USA, May 22–26, 2017, 39–57 (2017).Zhang, R., Chaudhuri, Okay. & Salakhutdinov, R. Making convolutional networks shift-invariant once more. In Proceedings of the thirty sixth International Conference on Machine Learning, ICML 2019, 9–15 June 2019, Long Beach, California, USA, Vol. 97 of Proceedings of Machine Learning Research (eds Chaudhuri, Okay. & Salakhutdinov, R.) 7324–7334 (PMLR, 2019)Bahri, M., Salutari, F., Putina, A. & Sozio, M. Automl: state-of-the-art with a deal with anomaly detection, challenges, and analysis instructions. Int. J. Data Sci. Anal. 14, 1–14 (2022).Zhou, Okay., Liu, Z., Duan, Okay. & Hu, X. Graph Neural Networks: AutoML 371–389 (Springer Nature Singapore, 2022).Zhao, X. et al. Expressive 1-lipschitz neural networks for robust a number of graph studying towards adversarial assaults. In Proceedings of the thirty eighth International Conference on Machine Learning, ICML 2021, 18–24 July 2021, Virtual Event, Vol. 139 of Proceedings of Machine Learning Research (eds Meila, M. & Zhang, T.) 12719–12735 (PMLR, 2021).Omar, M., Choi, S., Nyang, D. & Mohaisen, D. Robust pure language processing: current advances, challenges, and future instructions. IEEE Access 10, 86038–86056 (2022).Goel, Okay. et al. Robustness fitness center: Unifying the NLP analysis panorama. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations, NAACL-HLT 2021, Online, June 6–11, 2021, (eds Sil, A. & Lin, X. V.) 42–55 (Association for Computational Linguistics, 2021).Ko, C. et al. POPQORN: quantifying robustness of recurrent neural networks. In Proceedings of the thirty sixth International Conference on Machine Learning, ICML 2019, 9–15 June 2019, Long Beach, California, USA, Vol. 97 of Proceedings of Machine Learning Research, (eds Chaudhuri, Okay. & Salakhutdinov, R.) 3468–3477 (PMLR, 2019).Klyuchnikov, N. et al. Nas-bench-nlp: neural structure search benchmark for pure language processing. IEEE Access 10, 45736–45747 (2022).Article
Google Scholar
Zügner, D., Borchert, O., Akbarnejad, A. & Günnemann, S. Adversarial assaults on graph neural networks: perturbations and their patterns. ACM Trans. Knowledge Discovery from Data (TKDD) 14, 1–31 (2020).Article
Google Scholar
Jin, W. et al. Adversarial assaults and defenses on graphs. ACM SIGKDD Explorations Newsletter 22, 19–34 (2021).Article
Google Scholar
Tao, S., Shen, H., Cao, Q., Hou, L. & Cheng, X. Adversarial immunization for certifiable robustness on graphs. In WSDM ’21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8–12, 2021 (eds Lewin-Eytan, L., Carmel, D., Yom-Tov, E., Agichtein, E. & Gabrilovich, E.) 698–706 (ACM, 2021).Khoshavi, N., Sargolzaei, S., Bi, Y. & Roohi, A. Entropy-based modeling for estimating adversarial bit-flip assault influence on binarized neural community. In ASPDAC ’21: twenty sixth Asia and South Pacific Design Automation Conference, Tokyo, Japan, 493–498 (ACM, 2021).Shi, Z., Zhang, H., Chang, Okay., Huang, M. & Hsieh, C. Robustness verification for transformers. In eighth International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26–30, 2020 (OpenReview.internet, 2020).Zhou, D. et al. Understanding the robustness in imaginative and prescient transformers. International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, Vol. 162 of Proceedings of Machine Learning Research, (eds Chaudhuri, Okay. et al.) 27378–27394 (PMLR, 2022).Kang, M. & Petrášek, Z. Random graphs: concept and functions from nature to society to the mind. Internationale Mathematische Nachrichten 227, 1–24 (2014).MATH
Google Scholar
Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364 (2017).Article
Google Scholar
Mijalkov, M. et al. Braph: a graph concept software program for the evaluation of mind connectivity. PLoS One 12, e0178798 (2017).Article
Google Scholar
Stam, C. Connected brains: introduction to graph concept. Department of Clinical Neurophysiology, VU University Medical Centre. https://home.kpn.nl/stam7883/graph_introduction.html (2013).Masuda, N., Sakaki, M., Ezaki, T. & Watanabe, T. Clustering coefficients for correlation networks. Front. Neuroinform. 12, 7 (2018).Article
Google Scholar
Chen, Y., Georgiou, T., Pavon, M. & Tannenbaum, A. Robust transport over networks. IEEE Trans. Automatic Control 62, 4675–4682 (2016).Article
MathSciWeb
MATH
Google Scholar
Ollivier, Y. Ricci curvature of metric areas. Compt. R. Math. 345, 643–646 (2007).MathSciWeb
MATH
Google Scholar
Ollivier, Y. Ricci curvature of Markov chains on metric areas. J. Funct. Anal. 256, 810–864 (2009).Article
MathSciWeb
MATH
Google Scholar
Do Carmo, M. P. & Flaherty Francis, J. Riemannian Geometry Vol. 6 (Springer, 1992).Demetrius, L. A. Boltzmann, darwin and directionality concept. Phys. Rep. 530, 1–85 (2013).Article
MathSciWeb
MATH
Google Scholar
Demetrius, L. A. & Wolf, C. Directionality concept and the second regulation of thermodynamics. Physica A: Stat. Mech. Appl. 598, 127325 (2022).Article
MathSciWeb
MATH
Google Scholar
Demetrius, L., Gundlach, V. M. & Ochs, G. Complexity and demographic stability in inhabitants fashions. Theor. Popul. Biol. 65, 211–225 (2004).Article
MATH
Google Scholar
https://news.google.com/__i/rss/rd/articles/CBMiMmh0dHBzOi8vd3d3Lm5hdHVyZS5jb20vYXJ0aWNsZXMvczQ0MTcyLTAyMi0wMDA0My0y0gEA?oc=5