{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T16:29:04Z","timestamp":1768321744954,"version":"3.49.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031703584","type":"print"},{"value":"9783031703591","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70359-1_3","type":"book-chapter","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T04:02:43Z","timestamp":1724904163000},"page":"38-55","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Walking Noise: On Layer-Specific Robustness of\u00a0Neural Architectures Against Noisy Computations and\u00a0Associated Characteristic Learning Dynamics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2411-2416","authenticated-orcid":false,"given":"Hendrik","family":"Borras","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0497-5748","authenticated-orcid":false,"given":"Bernhard","family":"Klein","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9562-0680","authenticated-orcid":false,"given":"Holger","family":"Fr\u00f6ning","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Ash-Saki, A., Alam, M., Ghosh, S.: QURE: Qubit re-allocation in noisy intermediate-scale quantum computers. In: Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3316781.3317888","DOI":"10.1145\/3316781.3317888"},{"key":"3_CR2","unstructured":"Banbury, C.R., et al.: Benchmarking TinyML systems: challenges and direction (2020)"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Br\u00fcckerhoff-Pl\u00fcckelmann, F., et al.: Probabilistic photonic computing with chaotic light (2024)","DOI":"10.1038\/s41467-024-54931-6"},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Cappelli, A., Ohana, R., Launay, J., Meunier, L., Poli, I., Krzakala, F.: Adversarial robustness by design through analog computing and synthetic gradients. In: 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (2022). https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9746671","DOI":"10.1109\/ICASSP43922.2022.9746671"},{"key":"3_CR5","unstructured":"Chen, R.T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.K.: Neural ordinary differential equations. In: Advances in Neural Information Processing Systems, vol.\u00a031. Curran Associates, Inc. (2018)"},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Cowan, G., Melville, R., Tsividis, Y.: A VLSI analog computer\/digital computer accelerator. IEEE J. Solid-State Circ. 41(1) (2006). https:\/\/doi.org\/10.1109\/JSSC.2005.858618","DOI":"10.1109\/JSSC.2005.858618"},{"key":"3_CR7","unstructured":"Amodei, D., Hernandez, D.: AI and compute (2018). https:\/\/openai.com\/blog\/ai-and-compute\/. Accessed 23 Apr 2023"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Du, Y., et al.: Exploring the impact of random telegraph noise-induced accuracy loss on resistive ram-based deep neural network. IEEE Trans. Electron Devices 67(8) (2020). https:\/\/doi.org\/10.1109\/TED.2020.3002736","DOI":"10.1109\/TED.2020.3002736"},{"key":"3_CR9","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv e-prints arXiv:1412.6572, December 2014"},{"key":"3_CR10","doi-asserted-by":"publisher","unstructured":"Grandvalet, Y., Canu, S., Boucheron, S.: Noise injection: theoretical prospects. Neural Comput. 9(5) (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.5.1093","DOI":"10.1162\/neco.1997.9.5.1093"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"He, Z., Rakin, A.S., Fan, D.: Parametric noise injection: trainable randomness to improve deep neural network robustness against adversarial attack. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2019). https:\/\/doi.ieeecomputersociety.org\/10.1109\/CVPR.2019.00068","DOI":"10.1109\/CVPR.2019.00068"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00286"},{"key":"3_CR13","doi-asserted-by":"publisher","unstructured":"Jiang, Y., Zur, R.M., Pesce, L.L., Drukker, K.: A study of the effect of noise injection on the training of artificial neural networks. In: 2009 International Joint Conference on Neural Networks (2009). https:\/\/doi.org\/10.1109\/IJCNN.2009.5178981","DOI":"10.1109\/IJCNN.2009.5178981"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Joshi, V., et al.: Accurate deep neural network inference using computational phase-change memory. Nat. Commun. 11(1) (2020)","DOI":"10.1038\/s41467-020-16108-9"},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"Klein, B., et al.: Towards addressing noise and static variations of analog computations using efficient retraining. In: Kamp, M., et al. (eds.) Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021. CCIS, vol. 1524, pp. 409\u2013420. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-93736-2_32","DOI":"10.1007\/978-3-030-93736-2_32"},{"key":"3_CR16","unstructured":"Krizhevsky, A., Nair, V., Hinton, G.: CIFAR-10 (Canadian Institute for Advanced Research) (2009)"},{"key":"3_CR17","doi-asserted-by":"publisher","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11) (1998). https:\/\/doi.org\/10.1109\/5.726791","DOI":"10.1109\/5.726791"},{"key":"3_CR18","unstructured":"LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, S., Hu, X., Yang, J.: Understanding the disharmony between dropout and batch normalization by variance shift. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00279","DOI":"10.1109\/CVPR.2019.00279"},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Lin, X., et\u00a0al.: All-optical machine learning using diffractive deep neural networks. Science 361(6406) (2018). https:\/\/doi.org\/10.1126\/science.aat8084","DOI":"10.1126\/science.aat8084"},{"key":"3_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/978-3-030-01234-2_23","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Liu","year":"2018","unstructured":"Liu, X., Cheng, M., Zhang, H., Hsieh, C.-J.: Towards robust neural networks via random self-ensemble. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 381\u2013397. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_23"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Liu, X., Xiao, T., Si, S., Cao, Q., Kumar, S., Hsieh, C.J.: How does noise help robustness? Explanation and exploration under the neural SDE framework. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00036","DOI":"10.1109\/CVPR42600.2020.00036"},{"key":"3_CR23","doi-asserted-by":"publisher","unstructured":"Murmann, B.: Mixed-signal computing for deep neural network inference. IEEE Trans. Very Large Scale Integr. Syst. 29(1) (2021). https:\/\/doi.org\/10.1109\/TVLSI.2020.3020286","DOI":"10.1109\/TVLSI.2020.3020286"},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Murray, A., Edwards, P.: Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training. IEEE Trans. Neural Netw. 5(5) (1994). https:\/\/doi.org\/10.1109\/72.317730","DOI":"10.1109\/72.317730"},{"key":"3_CR25","unstructured":"Netzer, Y., Wang, T., Adam\u00a0Coates, A.B., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011). http:\/\/ufldl.stanford.edu\/housenumbers\/nips2011_housenumbers.pdf. Accessed 14 Jan 2023"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Rekhi, A.S., et al.: Analog\/mixed-signal hardware error modeling for deep learning inference. In: Proceedings of the 56th Annual Design Automation Conference 2019. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3316781.3317770","DOI":"10.1145\/3316781.3317770"},{"key":"3_CR27","unstructured":"Santurkar, S., Tsipras, D., Ilyas, A., Madry, A.: How does batch normalization help optimization? In: Advances in Neural Information Processing Systems, vol.\u00a031. Curran Associates, Inc. (2018)"},{"key":"3_CR28","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-030-91741-8_6","volume-title":"Analog Circuits for Machine Learning, Current\/Voltage\/Temperature Sensors, and High-speed Communication","author":"J Schemmel","year":"2022","unstructured":"Schemmel, J., Billaudelle, S., Dauer, P., Weis, J.: Accelerated analog neuromorphic computing. In: Harpe, P., Makinwa, K.A.A., Baschirotto, A. (eds.) Analog Circuits for Machine Learning, Current\/Voltage\/Temperature Sensors, and High-speed Communication, pp. 83\u2013102. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-91741-8_6"},{"key":"3_CR29","doi-asserted-by":"publisher","unstructured":"Shen, Y., et\u00a0al.: Deep learning with coherent nanophotonic circuits. Nat. Photonics 11(7) (2017). https:\/\/doi.org\/10.1038\/nphoton.2017.93","DOI":"10.1038\/nphoton.2017.93"},{"key":"3_CR30","unstructured":"Thompson, N.C., Greenewald, K.H., Lee, K., Manso, G.F.: The computational limits of deep learning (2020). https:\/\/arxiv.org\/abs\/2007.05558"},{"key":"3_CR31","unstructured":"Warden, P.: Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition, April 2018. https:\/\/arxiv.org\/abs\/1804.03209"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Wu, C., et al.: Harnessing optoelectronic noises in a photonic generative network. Sci. Adv. 8(3) (2022). https:\/\/www.science.org\/doi\/abs\/10.1126\/sciadv.abm2956","DOI":"10.1126\/sciadv.abm2956"},{"key":"3_CR33","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms (2017)"},{"key":"3_CR34","unstructured":"Zhang, Y., Suda, N., Lai, L., Chandra, V.: Hello Edge: Keyword Spotting on Microcontroller (2017)"},{"key":"3_CR35","unstructured":"Zhou, C., Kadambi, P., Mattina, M., Whatmough, P.N.: Noisy machines: understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation (2020). https:\/\/arxiv.org\/abs\/2001.04974"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70359-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T07:27:41Z","timestamp":1768202861000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70359-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703584","9783031703591"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70359-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}