{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T15:00:34Z","timestamp":1776956434271,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>This paper presents a simple yet principled approach to boosting the robustness of the residual network (ResNet) that is motivated by a dynamical systems perspective. Namely, a deep neural network can be interpreted using a partial differential equation, which naturally inspires us to characterize ResNet based on an explicit Euler method. This consequently allows us to exploit the step factor h in the Euler method to control the robustness of ResNet in both its training and generalization. In particular, we prove that a small step factor h can benefit its training and generalization robustness during backpropagation and forward propagation, respectively. Empirical evaluation on real-world datasets corroborates our analytical findings that a small h can indeed improve both its training and generalization robustness.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/595","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"4285-4291","source":"Crossref","is-referenced-by-count":9,"title":["Towards Robust ResNet: A Small Step but a Giant Leap"],"prefix":"10.24963","author":[{"given":"Jingfeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science, National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Han","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Wynter","sequence":"additional","affiliation":[{"name":"IBM Research, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bryan Kian Hsiang","family":"Low","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohan","family":"Kankanhalli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:50:22Z","timestamp":1564300222000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/595"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/595","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}