{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T04:49:04Z","timestamp":1778302144927,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":67,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS-2211301"],"award-info":[{"award-number":["CNS-2211301"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS-2303115"],"award-info":[{"award-number":["CNS-2303115"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS-2312089"],"award-info":[{"award-number":["CNS-2312089"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004351","name":"Cisco Systems","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004351","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,4]]},"DOI":"10.1145\/3636534.3690684","type":"proceedings-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T23:13:18Z","timestamp":1733353998000},"page":"1147-1161","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3721-5118","authenticated-orcid":false,"given":"Hossein","family":"Khalili","sequence":"first","affiliation":[{"name":"University of California, Los Angeles (UCLA), Los Angeles, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4455-0949","authenticated-orcid":false,"given":"Seongbin","family":"Park","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles (UCLA), Los Angeles, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7136-2946","authenticated-orcid":false,"given":"Vincent","family":"Li","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles (UCLA), Los Angeles, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7089-1875","authenticated-orcid":false,"given":"Brandan","family":"Bright","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles (UCLA), Los Angeles, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4054-2958","authenticated-orcid":false,"given":"Ali","family":"Payani","sequence":"additional","affiliation":[{"name":"Cisco Systems, San Jose, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7559-8997","authenticated-orcid":false,"given":"Ramana Rao","family":"Kompella","sequence":"additional","affiliation":[{"name":"Cisco Systems, San Jose, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7181-2258","authenticated-orcid":false,"given":"Nader","family":"Sehatbakhsh","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles (UCLA), Los Angeles, US"}]}],"member":"320","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International conference on machine learning. PMLR, 274--283","author":"Athalye Anish","year":"2018","unstructured":"Anish Athalye, Nicholas Carlini, and David Wagner. 2018. Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In International conference on machine learning. PMLR, 274--283."},{"key":"e_1_3_2_1_2_1","volume-title":"Recent Advances in Adversarial Training for Adversarial Robustness. arXiv preprint arXiv:2102.01356","author":"Bai Tao","year":"2021","unstructured":"Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang. 2021. Recent Advances in Adversarial Training for Adversarial Robustness. arXiv preprint arXiv:2102.01356 (2021)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CISS.2018.8362326"},{"key":"e_1_3_2_1_4_1","volume-title":"Adversarial patch. arXiv preprint arXiv:1712.09665","author":"Brown Tom B","year":"2017","unstructured":"Tom B Brown, Dandelion Man\u00e9, Aurko Roy, Mart\u00edn Abadi, and Justin Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 (2017)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00076"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3339815"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57.","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_8_1","volume-title":"20th USENIX security symposium (USENIX Security 11).","author":"Checkoway Stephen","unstructured":"Stephen Checkoway, Damon McCoy, Brian Kantor, Danny Anderson, Hovav Shacham, Stefan Savage, Karl Koscher, Alexei Czeskis, Franziska Roesner, and Tadayoshi Kohno. 2011. Comprehensive experimental analyses of automotive attack surfaces. In 20th USENIX security symposium (USENIX Security 11)."},{"key":"e_1_3_2_1_9_1","unstructured":"chenyaofo. 2021. PyTorch CIFAR models. https:\/\/github.com\/chenyaofo\/pytorch-cifar-models."},{"key":"e_1_3_2_1_10_1","volume-title":"RobustBench: a standardized adversarial robustness benchmark. arXiv preprint arXiv:2010.09670","author":"Croce Francesco","year":"2020","unstructured":"Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein. 2020. RobustBench: a standardized adversarial robustness benchmark. arXiv preprint arXiv:2010.09670 (2020)."},{"key":"e_1_3_2_1_11_1","volume-title":"International conference on machine learning. PMLR, 2206--2216","author":"Croce Francesco","year":"2020","unstructured":"Francesco Croce and Matthias Hein. 2020. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning. PMLR, 2206--2216."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_13_1","volume-title":"European Conference on Computer Vision. Springer, 467--483","author":"Dolatabadi Hadi M","year":"2022","unstructured":"Hadi M Dolatabadi, Sarah Erfani, and Christopher Leckie. 2022. l\u221e-robustness and beyond: Unleashing efficient adversarial training. In European Conference on Computer Vision. Springer, 467--483."},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability. Univ of California Press, 403","author":"Feller W","year":"1949","unstructured":"W Feller. 1949. On the Theory of Stochastic Processes, With Particular Reference to Applications. In Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability. Univ of California Press, 403."},{"key":"e_1_3_2_1_15_1","volume-title":"Science, technology and the future of small autonomous drones. nature 521, 7553","author":"Floreano Dario","year":"2015","unstructured":"Dario Floreano and Robert J Wood. 2015. Science, technology and the future of small autonomous drones. nature 521, 7553 (2015), 460--466."},{"key":"e_1_3_2_1_16_1","volume-title":"Generative adversarial nets. Advances in neural information processing systems 27","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems 27 (2014)."},{"key":"e_1_3_2_1_17_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_3_2_1_18_1","first-page":"4218","article-title":"Improving robustness using generated data","volume":"34","author":"Gowal Sven","year":"2021","unstructured":"Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian, and Timothy A Mann. 2021. Improving robustness using generated data. Advances in Neural Information Processing Systems 34 (2021), 4218--4233.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems -","volume":"2","author":"Han Song","year":"2015","unstructured":"Song Han, Jeff Pool, John Tran, and William Dally. 2015. Learning both weights and connections for efficient neural network. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2. 1135--1143."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00068"},{"key":"e_1_3_2_1_21_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_1_22_1","volume-title":"Denoising diffusion probabilistic models. Advances in neural information processing systems 33","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020), 6840--6851."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2703172"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01304"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01426"},{"key":"e_1_3_2_1_28_1","volume-title":"Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. In International Conference on Learning Representations (ICLR).","author":"Laidlaw C","year":"2021","unstructured":"C Laidlaw, S Singla, and S Feizi. 2021. Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_29_1","unstructured":"Y. Le and X. Yang. 2015. Tiny ImageNet Visual Recognition Challenge. CS 231N Course Project Report Stanford University. http:\/\/cs231n.stanford.edu\/reports\/2015\/pdfs\/LeXie.pdf"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00019"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011028"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3043716"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2915983"},{"key":"e_1_3_2_1_35_1","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"Lovisotto Giulio","year":"2021","unstructured":"Giulio Lovisotto, Henry Turner, Ivo Sluganovic, Martin Strohmeier, and Ivan Martinovic. 2021. {SLAP}: Improving physical adversarial examples with {Short-Lived} adversarial perturbations. In 30th USENIX Security Symposium (USENIX Security 21). 1865--1882."},{"key":"e_1_3_2_1_36_1","volume-title":"Interpretation and Generalization of Score Matching. In The International Conference on Uncertainty in Artificial Intelligence (UAI). Montreal, QC, Canada.","author":"Lyu Siwei","year":"2009","unstructured":"Siwei Lyu. 2009. Interpretation and Generalization of Score Matching. In The International Conference on Uncertainty in Artificial Intelligence (UAI). Montreal, QC, Canada."},{"key":"e_1_3_2_1_37_1","volume-title":"International Conference on Learning Representations.","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134057"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3032227"},{"key":"e_1_3_2_1_40_1","volume-title":"International conference on machine learning. PMLR, 8162--8171","author":"Nichol Alexander Quinn","year":"2021","unstructured":"Alexander Quinn Nichol and Prafulla Dhariwal. 2021. Improved denoising diffusion probabilistic models. In International conference on machine learning. PMLR, 8162--8171."},{"key":"e_1_3_2_1_41_1","volume-title":"Diffusion Models for Adversarial Purification. In International Conference on Machine Learning. PMLR, 16805--16827","author":"Nie Weili","year":"2022","unstructured":"Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Animashree Anandkumar. 2022. Diffusion Models for Adversarial Purification. In International Conference on Machine Learning. PMLR, 16805--16827."},{"key":"e_1_3_2_1_42_1","volume-title":"ACM Asia Conference on Computer and Communications Security.","author":"Papernot Nicolas","year":"2016","unstructured":"Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2016. Practical black-box attacks against machine learning, 2017. In ACM Asia Conference on Computer and Communications Security."},{"key":"e_1_3_2_1_43_1","volume-title":"International Conference on Machine Learning. PMLR, 7717--7727","author":"Pinot Rafael","year":"2020","unstructured":"Rafael Pinot, Raphael Ettedgui, Geovani Rizk, Yann Chevaleyre, and Jamal Atif. 2020. Randomization matters how to defend against strong adversarial attacks. In International Conference on Machine Learning. PMLR, 7717--7727."},{"key":"e_1_3_2_1_44_1","first-page":"29935","article-title":"Data augmentation can improve robustness","volume":"34","author":"Rebuffi Sylvestre-Alvise","year":"2021","unstructured":"Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, and Timothy A Mann. 2021. Data augmentation can improve robustness. Advances in Neural Information Processing Systems 34 (2021), 29935--29948.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_45_1","volume-title":"High-Resolution Image Synthesis with Latent Diffusion Models. arXiv preprint arXiv:2112.10752","author":"Rombach Robin","year":"2022","unstructured":"Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj\u00f6rn Ommer. 2022. High-Resolution Image Synthesis with Latent Diffusion Models. arXiv preprint arXiv:2112.10752 (2022)."},{"key":"e_1_3_2_1_46_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5--9, 2015, proceedings, part III 18. Springer, 234--241."},{"key":"e_1_3_2_1_47_1","volume-title":"International Conference on Learning Representations.","author":"Samangouei Pouya","year":"2018","unstructured":"Pouya Samangouei, Maya Kabkab, and Rama Chellappa. 2018. Defense-GAN: Protecting classifiers against adversarial attacks using generative models. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3592532"},{"key":"e_1_3_2_1_49_1","volume-title":"International conference on machine learning. PMLR, 2256--2265","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning. PMLR, 2256--2265."},{"key":"e_1_3_2_1_50_1","volume-title":"12th USENIX workshop on offensive technologies (WOOT 18)","author":"Song Dawn","year":"2018","unstructured":"Dawn Song, Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramer, Atul Prakash, and Tadayoshi Kohno. 2018. Physical adversarial examples for object detectors. In 12th USENIX workshop on offensive technologies (WOOT 18)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3356250.3360025"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3469032"},{"key":"e_1_3_2_1_53_1","volume-title":"International Conference on Learning Representations.","author":"Song Yang","year":"2018","unstructured":"Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman. 2018. Pixeldefend: Leveraging generative models to understand and defend against adversarial examples. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_54_1","volume-title":"2nd International Conference on Learning Representations, ICLR","author":"Szegedy Christian","year":"2014","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In 2nd International Conference on Learning Representations, ICLR 2014."},{"key":"e_1_3_2_1_55_1","volume-title":"On adaptive attacks to adversarial example defenses. Advances in neural information processing systems 33","author":"Tramer Florian","year":"2020","unstructured":"Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry. 2020. On adaptive attacks to adversarial example defenses. Advances in neural information processing systems 33 (2020), 1633--1645."},{"key":"e_1_3_2_1_56_1","volume-title":"Image quality assessment: from error visibility to structural similarity","author":"Wang Zhou","year":"2004","unstructured":"Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13, 4 (2004), 600--612."},{"key":"e_1_3_2_1_57_1","volume-title":"International Conference on Learning Representations.","author":"Wong Eric","year":"2019","unstructured":"Eric Wong, Leslie Rice, and J Zico Kolter. 2019. Fast is better than free: Revisiting adversarial training. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_58_1","volume-title":"Tackling the Generative Learning Trilemma with Denoising Diffusion GANs. In International Conference on Learning Representations (ICLR).","author":"Xiao Zhisheng","year":"2022","unstructured":"Zhisheng Xiao, Karsten Kreis, and Arash Vahdat. 2022. Tackling the Generative Learning Trilemma with Denoising Diffusion GANs. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_59_1","volume-title":"International Conference on Machine Learning. PMLR, 12062--12072","author":"Yoon Jongmin","year":"2021","unstructured":"Jongmin Yoon, Sung Ju Hwang, and Juho Lee. 2021. Adversarial purification with score-based generative models. In International Conference on Machine Learning. PMLR, 12062--12072."},{"key":"e_1_3_2_1_60_1","volume-title":"32nd USENIX Security Symposium (USENIX Security 23)","author":"Zhang Jiawei","year":"2023","unstructured":"Jiawei Zhang, Zhongzhu Chen, Huan Zhang, Chaowei Xiao, and Bo Li. 2023. {DiffSmooth}: Certifiably robust learning via diffusion models and local smoothing. In 32nd USENIX Security Symposium (USENIX Security 23). 4787--4804."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568539"},{"key":"e_1_3_2_1_62_1","volume-title":"Loss functions for neural networks for image processing. arXiv preprint arXiv:1511.08861","author":"Zhao Hang","year":"2015","unstructured":"Hang Zhao, Orazio Gallo, Iuri Frosio, and Jan Kautz. 2015. Loss functions for neural networks for image processing. arXiv preprint arXiv:1511.08861 (2015)."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_3_2_1_64_1","volume-title":"Tpatch: A triggered physical adversarial patch. arXiv preprint arXiv:2401.00148","author":"Zhu Wenjun","year":"2023","unstructured":"Wenjun Zhu, Xiaoyu Ji, Yushi Cheng, Shibo Zhang, and Wenyuan Xu. 2023. Tpatch: A triggered physical adversarial patch. arXiv preprint arXiv:2401.00148 (2023)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485935"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3616661"},{"key":"e_1_3_2_1_67_1","volume-title":"International Conference on Machine Learning. PMLR, 11639--11649","author":"Zhuang Juntang","year":"2020","unstructured":"Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, and James Duncan. 2020. Adaptive checkpoint adjoint method for gradient estimation in neural ode. In International Conference on Machine Learning. PMLR, 11639--11649."}],"event":{"name":"ACM MobiCom '24: 30th Annual International Conference on Mobile Computing and Networking","location":"Washington D.C. DC USA","acronym":"ACM MobiCom '24","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"]},"container-title":["Proceedings of the 30th Annual International Conference on Mobile Computing and Networking"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3636534.3690684","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3636534.3690684","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3636534.3690684","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:36Z","timestamp":1750295856000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3636534.3690684"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"references-count":67,"alternative-id":["10.1145\/3636534.3690684","10.1145\/3636534"],"URL":"https:\/\/doi.org\/10.1145\/3636534.3690684","relation":{},"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"2024-12-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}