{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:23:13Z","timestamp":1767846193785,"version":"3.49.0"},"reference-count":94,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&#x0026;D Program of China","award":["2022YFB4701400\/4701402"],"award-info":[{"award-number":["2022YFB4701400\/4701402"]}]},{"name":"Shenzhen Science and Technology Innovation Bureau","award":["KJZD20230923115106012"],"award-info":[{"award-number":["KJZD20230923115106012"]}]},{"name":"Shenzhen Science and Technology Innovation Bureau","award":["KJZD20230923114916032"],"award-info":[{"award-number":["KJZD20230923114916032"]}]},{"name":"Beijing Key Lab of Networked Multimedia"},{"name":"National Key R&#x0026;D Program of China","award":["2022ZD0115901"],"award-info":[{"award-number":["2022ZD0115901"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62177007"],"award-info":[{"award-number":["62177007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China-Central Eastern European Countries High Education Joint Education Project","award":["202012"],"award-info":[{"award-number":["202012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tpami.2025.3590342","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T17:45:29Z","timestamp":1752860729000},"page":"9876-9893","source":"Crossref","is-referenced-by-count":1,"title":["Accelerating Zero-Shot NAS With Feature Map-Based Proxy and Operation Scoring Function"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2578-0595","authenticated-orcid":false,"given":"Tangyu","family":"Jiang","sequence":"first","affiliation":[{"name":"Graduate School at Shenzhen, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5453-4126","authenticated-orcid":false,"given":"Haodi","family":"Wang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong and Lab of AI-Powered FINTECH, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9971-7698","authenticated-orcid":false,"given":"Rongfang","family":"Bie","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3590-6676","authenticated-orcid":false,"given":"Chun","family":"Yuan","sequence":"additional","affiliation":[{"name":"Graduate School at Shenzhen, Tsinghua University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i13.26797"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i13.27007"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01298"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26076"},{"key":"ref6","article-title":"Neural architecture search with reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zoph"},{"key":"ref7","article-title":"DARTS: Differentiable architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref8","article-title":"Pc-darts: Partial channel connections for memory-efficient architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01099"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00139"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26243"},{"key":"ref12","article-title":"Neural architecture search on imagenet in four GPU hours: A theoretically inspired perspective","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref13","first-page":"7588","article-title":"Neural architecture search without training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mellor"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00138"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108186"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00578"},{"key":"ref17","article-title":"Zero-cost proxies for lightweight NAS","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Abdelfattah"},{"key":"ref18","article-title":"Zico: Zero-shot NAS via inverse coefficient of variation on gradients","volume-title":"Proc. Eleventh Int. Conf. Learn. Representations","author":"Li"},{"key":"ref19","first-page":"61020","article-title":"Meco: Zero-shot NAS with one data and single forward pass via minimum eigenvalue of correlation","volume-title":"Proc. Thirty-seventh Conf. Neural Inf. Process. Syst.","author":"Jiang"},{"key":"ref20","article-title":"Searching for efficient multi-scale architectures for dense image prediction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref21","article-title":"Evaluating the search phase of neural architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.102989"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6877"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.00355"},{"key":"ref25","article-title":"Neural architecture search with Bayesian optimisation and optimal transport","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kandasamy"},{"key":"ref26","first-page":"8084","article-title":"Optimal transport kernels for sequential and parallel neural architecture search","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nguyen"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01154"},{"key":"ref28","first-page":"4095","article-title":"Efficient neural architecture search via parameters sharing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pham"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.13140\/RG.2.2.18893.74727"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i06.6554"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00207"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01160"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01149"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00544"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00546"},{"key":"ref38","first-page":"70983","article-title":"Operation-level early stopping for robustifying differentiable NAS","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","author":"Jiang"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01160"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"ref41","first-page":"367","article-title":"Random search and reproducibility for neural architecture search","volume-title":"Proc. Uncertainty Artif. Intell.","author":"Li"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00040"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00169"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00544"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.154"},{"key":"ref46","first-page":"2902","article-title":"Large-scale evolution of image classifiers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Real"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487629"},{"key":"ref48","first-page":"5976","article-title":"Deep active learning with a neural architecture search","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Geifman"},{"key":"ref49","article-title":"Efficient multi-objective neural architecture search via lamarckian evolution","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Elsken"},{"key":"ref50","first-page":"24254","article-title":"LiteTransformerSearch: Training-free neural architecture search for efficient language models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Javaheripi"},{"key":"ref51","first-page":"18456","article-title":"UDC: Unified DNAS for compressible TinyML models for neural processing units","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fedorov"},{"key":"ref52","first-page":"12868","article-title":"ZARTS: On zero-order optimization for neural architecture search","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref53","article-title":"Snip: Single-shot network pruning based on connection sensitivity","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lee"},{"key":"ref54","article-title":"Picking winning tickets before training by preserving gradient flow","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref55","first-page":"6377","article-title":"Pruning neural networks without any data by iteratively conserving synaptic flow","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tanaka"},{"key":"ref56","article-title":"Blockswap: Fisher-guided block substitution for network compression on a budget","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Turner"},{"key":"ref57","first-page":"33001","article-title":"Unifying and boosting gradient-based training-free neural architecture search","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shu"},{"key":"ref58","first-page":"23551","article-title":"Generalization properties of nas under activation and skip connection search","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhu"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00040"},{"key":"ref60","first-page":"8580","article-title":"Neural tangent kernel: Convergence and generalization in neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jacot"},{"key":"ref61","first-page":"8141","article-title":"On exact computation with an infinitely wide neural net","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Arora"},{"key":"ref62","first-page":"8572","article-title":"Wide neural networks of any depth evolve as linear models under gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lee"},{"key":"ref63","article-title":"Gradient descent provably optimizes over-parameterized neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Du"},{"key":"ref64","article-title":"Finite depth and width corrections to the neural tangent kernel","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hanin"},{"key":"ref65","first-page":"1675","article-title":"Gradient descent finds global minima of deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Du"},{"key":"ref66","first-page":"2055","article-title":"An improved analysis of training over-parameterized deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zou"},{"key":"ref67","first-page":"11961","article-title":"Global convergence of deep networks with one wide layer followed by pyramidal topology","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Nguyen"},{"key":"ref68","first-page":"6391","article-title":"Visualizing the loss landscape of neural nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139020411"},{"key":"ref70","first-page":"10836","article-title":"Generalization bounds of stochastic gradient descent for wide and deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cao"},{"key":"ref71","article-title":"Rethinking architecture selection in differentiable NAS","volume-title":"Proc. Int. Conf. Learn. Representation","author":"Wang"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054824"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00391"},{"key":"ref74","article-title":"Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zela"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00521"},{"key":"ref76","first-page":"7105","article-title":"NAS-bench-101: Towards reproducible neural architecture search","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ying"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01205"},{"key":"ref78","article-title":"Once-for-all: Train one network and specialize it for efficient deployment","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cai"},{"key":"ref79","article-title":"ProxylessNAS: Direct neural architecture search on target task and hardware","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cai"},{"key":"ref80","first-page":"11613","article-title":"KNAS: Green neural architecture search","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref81","article-title":"NASI: Label- and data-agnostic neural architecture search at initialization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Shu"},{"key":"ref82","article-title":"Gradsign: Model performance inference with theoretical insights","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang"},{"key":"ref83","article-title":"SWAP-NAS: Sample-wise activation patterns for ultra-fast NAS","volume-title":"Proc. Twelfth Int. Conf. Learn. Representations","author":"Peng"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054824"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.5555\/2188385.2188395"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"ref87","first-page":"1437","article-title":"BOHB: Robust and efficient hyperparameter optimization at scale","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Falkner"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00186"},{"key":"ref89","first-page":"1554","article-title":"Stabilizing differentiable architecture search via perturbation-based regularization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref90","article-title":"SNAS: Stochastic neural architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xie"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref92","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_2"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11192800\/11084843.pdf?arnumber=11084843","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T17:36:30Z","timestamp":1759772190000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11084843\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":94,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3590342","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}