{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:06:18Z","timestamp":1784995578138,"version":"3.55.0"},"reference-count":62,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276175"],"award-info":[{"award-number":["62276175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research and Develop Program of West China Hospital of Stomatology Sichuan University","award":["RD-03-202403"],"award-info":[{"award-number":["RD-03-202403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1109\/tkde.2025.3543503","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T18:26:02Z","timestamp":1739903162000},"page":"2888-2902","source":"Crossref","is-referenced-by-count":5,"title":["REP: An Interpretable Robustness Enhanced Plugin for Differentiable Neural Architecture Search"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7956-8183","authenticated-orcid":false,"given":"Yuqi","family":"Feng","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6374-1429","authenticated-orcid":false,"given":"Yanan","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8851-5348","authenticated-orcid":false,"given":"Gary G.","family":"Yen","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6802-2463","authenticated-orcid":false,"given":"Kay Chen","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_3"},{"key":"ref3","article-title":"Neural architecture search with reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zoph"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3239842"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3178153"},{"key":"ref6","first-page":"2902","article-title":"Large-scale evolution of image classifiers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Real"},{"key":"ref7","article-title":"DARTS: Differentiable architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00022"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01210"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2025.3542350"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00613"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i1.25118"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3116248"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3250264"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01396-x"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3127346"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01213"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00660"},{"key":"ref19","article-title":"Intriguing properties of neural networks","author":"Szegedy","year":"2013"},{"key":"ref20","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goodfellow"},{"key":"ref21","article-title":"Towards deep learning models resistant to adversarial attacks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Madry"},{"key":"ref22","first-page":"2206","article-title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Croce"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref24","article-title":"Adversarial attacks on graph neural networks via meta learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Z\u00fcgner"},{"key":"ref25","first-page":"695","article-title":"Adversarial attacks on node embeddings via graph poisoning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bojchevski"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140444"},{"key":"ref27","first-page":"1310","article-title":"Certified adversarial robustness via randomized smoothing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cohen"},{"key":"ref28","first-page":"854","article-title":"Parseval networks: Improving robustness to adversarial examples","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cisse"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_32"},{"key":"ref30","article-title":"Ensemble adversarial training: Attacks and defenses","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Tram\u00e8r"},{"key":"ref31","article-title":"Fast is better than free: Revisiting adversarial training","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wong"},{"key":"ref32","first-page":"552","article-title":"Search to aggregate neighborhood for graph neural network","volume-title":"Proc. IEEE Int. Conf. Data Eng.","author":"Huan"},{"key":"ref33","article-title":"Understanding and robustifying differentiable architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zela"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01967"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00787"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29246"},{"key":"ref37","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref42","article-title":"PC-DARTS: Partial channel connections for memory-efficient architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu"},{"key":"ref43","first-page":"1554","article-title":"Stabilizing differentiable architecture search via perturbation-based regularization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00071"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref46","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Veli\u010dkovi\u0107"},{"key":"ref47","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref48","article-title":"AutoGL: A library for automated graph learning","volume-title":"Proc. Int. Conf. Learn. Representations Workshop","author":"Guan"},{"key":"ref49","article-title":"Bag of tricks for adversarial training","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Pang"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref51","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref52","first-page":"8093","article-title":"Overfitting in adversarially robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rice"},{"key":"ref53","article-title":"Improved regularization of convolutional neural networks with cutout","author":"DeVries","year":"2017"},{"key":"ref54","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref55","article-title":"Fast graph representation learning with pytorch geometric","author":"Fey","year":"2019"},{"key":"ref56","article-title":"Torchattacks: A pytorch repository for adversarial attacks","author":"Kim","year":"2020"},{"key":"ref57","article-title":"DeepRobust: A pytorch library for adversarial attacks and defenses","author":"Li","year":"2020"},{"key":"ref58","first-page":"7105","article-title":"NAS-Bench-101: Towards reproducible neural architecture search","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ying"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054824"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"ref61","first-page":"11278","article-title":"Attacks which do not kill training make adversarial learning stronger","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3201243"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/10948402\/10892073.pdf?arnumber=10892073","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T03:24:13Z","timestamp":1743996253000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10892073\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":62,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2025.3543503","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}