{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T08:51:06Z","timestamp":1751619066549,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":62,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671712","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"4536-4547","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SiGeo: Sub-One-Shot NAS via Geometry of Loss Landscape"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9555-7132","authenticated-orcid":false,"given":"Hua","family":"Zheng","sequence":"first","affiliation":[{"name":"Northeastern University, Boston, MA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4508-8525","authenticated-orcid":false,"given":"Kuang-Hung","family":"Liu","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8204-9515","authenticated-orcid":false,"given":"Igor","family":"Fedorov","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0784-2038","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9371-5642","authenticated-orcid":false,"given":"Wen-Yen","family":"Chen","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0027-4821","authenticated-orcid":false,"given":"Wei","family":"Wen","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"volume-title":"Zero-Cost Proxies for Lightweight NAS. In International Conference on Learning Representations (ICLR).","author":"Abdelfattah Mohamed S.","key":"e_1_3_2_2_1_1","unstructured":"Mohamed S. Abdelfattah, Abhinav Mehrotra, \u0141ukasz Dudziak, and Nicholas D. Lane. 2021. Zero-Cost Proxies for Lightweight NAS. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_2_1","volume-title":"Natural gradient works efficiently in learning. Neural computation","author":"Amari Shun-Ichi","year":"1998","unstructured":"Shun-Ichi Amari. 1998. Natural gradient works efficiently in learning. Neural computation, Vol. 10, 2 (1998), 251--276."},{"key":"e_1_3_2_2_3_1","volume-title":"Restructurable activation networks. arXiv preprint arXiv:2208.08562","author":"Bhardwaj Kartikeya","year":"2022","unstructured":"Kartikeya Bhardwaj, James Ward, Caleb Tung, Dibakar Gope, Lingchuan Meng, Igor Fedorov, Alex Chalfin, Paul Whatmough, and Danny Loh. 2022. Restructurable activation networks. arXiv preprint arXiv:2208.08562 (2022)."},{"key":"e_1_3_2_2_4_1","volume-title":"On-line learning for very large data sets. Applied stochastic models in business and industry","author":"Bottou L\u00e9on","year":"2005","unstructured":"L\u00e9on Bottou and Yann Le Cun. 2005. On-line learning for very large data sets. Applied stochastic models in business and industry, Vol. 21, 2 (2005), 137--151."},{"key":"e_1_3_2_2_5_1","volume-title":"International Conference on Learning Representations.","author":"Brock Andrew","year":"2018","unstructured":"Andrew Brock, Theo Lim, J.M. Ritchie, and Nick Weston. 2018. SMASH: One-Shot Model Architecture Search through HyperNetworks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_6_1","volume-title":"International Conference on Learning Representations.","author":"Cai Han","year":"2020","unstructured":"Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han. 2020. Once for All: Train One Network and Specialize it for Efficient Deployment. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_7_1","volume-title":"ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. In International Conference on Learning Representations.","author":"Cai Han","year":"2019","unstructured":"Han Cai, Ligeng Zhu, and Song Han. 2019. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_8_1","volume-title":"Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective. In International Conference on Learning Representations.","author":"Chen Wuyang","year":"2021","unstructured":"Wuyang Chen, Xinyu Gong, and Zhangyang Wang. 2021. Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00138"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01202"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00186"},{"key":"e_1_3_2_2_12_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Dong Xuanyi","year":"2020","unstructured":"Xuanyi Dong and Yi Yang. 2020. NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00521"},{"key":"e_1_3_2_2_14_1","volume-title":"Conference on Learning Theory. PMLR, 1192--1234","author":"Fang Cong","year":"2019","unstructured":"Cong Fang, Zhouchen Lin, and Tong Zhang. 2019. Sharp analysis for nonconvex sgd escaping from saddle points. In Conference on Learning Theory. PMLR, 1192--1234."},{"key":"e_1_3_2_2_15_1","volume-title":"Oh (Eds.)","volume":"35","author":"Fedorov Igor","year":"2022","unstructured":"Igor Fedorov, Ramon Matas, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, and Paul Whatmough. 2022. UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 18456--18471."},{"key":"e_1_3_2_2_16_1","volume-title":"Wortman Vaughan (Eds.)","volume":"34","author":"Gao Chen","year":"2021","unstructured":"Chen Gao, Yinfeng Li, Quanming Yao, Depeng Jin, and Yong Li. 2021. Progressive Feature Interaction Search for Deep Sparse Network. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 392--403."},{"key":"e_1_3_2_2_17_1","volume-title":"Handbook of convergence theorems for (stochastic) gradient methods. arXiv preprint arXiv:2301.11235","author":"Garrigos Guillaume","year":"2023","unstructured":"Guillaume Garrigos and Robert M Gower. 2023. Handbook of convergence theorems for (stochastic) gradient methods. arXiv preprint arXiv:2301.11235 (2023)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_2_19_1","volume-title":"Flat minima. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Flat minima. Neural computation, Vol. 9, 1 (1997), 1--42."},{"volume-title":"Advances in Neural Information Processing Systems","author":"Jiang Tangyu","key":"e_1_3_2_2_20_1","unstructured":"Tangyu Jiang, Haodi Wang, and Rongfang Bie. 2023. MeCo: Zero-Shot NAS with One Data and Single Forward Pass via Minimum Eigenvalue of Correlation. In Advances in Neural Information Processing Systems, A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds.), Vol. 36. Curran Associates, Inc., 61020--61047."},{"key":"e_1_3_2_2_21_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1032--1041","author":"Karakida Ryo","year":"2019","unstructured":"Ryo Karakida, Shotaro Akaho, and Shun-ichi Amari. 2019. Universal statistics of fisher information in deep neural networks: Mean field approach. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1032--1041."},{"key":"e_1_3_2_2_22_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Keskar Nitish Shirish","year":"2017","unstructured":"Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. 2017. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net."},{"key":"e_1_3_2_2_23_1","volume-title":"Differentiable NAS Framework and Application to Ads CTR Prediction. arXiv preprint arXiv:2110.14812","author":"Krishna Ravi","year":"2021","unstructured":"Ravi Krishna, Aravind Kalaiah, Bichen Wu, Maxim Naumov, Dheevatsa Mudigere, Misha Smelyanskiy, and Kurt Keutzer. 2021. Differentiable NAS Framework and Application to Ads CTR Prediction. arXiv preprint arXiv:2110.14812 (2021)."},{"key":"e_1_3_2_2_24_1","volume-title":"Oh (Eds.)","volume":"35","author":"Krishnakumar Arjun","year":"2022","unstructured":"Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, and Frank Hutter. 2022. NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 28037--28051."},{"key":"e_1_3_2_2_25_1","volume-title":"A simpler approach to obtaining an O (1\/t) convergence rate for the projected stochastic subgradient method. arXiv preprint arXiv:1212.2002","author":"Lacoste-Julien Simon","year":"2012","unstructured":"Simon Lacoste-Julien, Mark Schmidt, and Francis Bach. 2012. A simpler approach to obtaining an O (1\/t) convergence rate for the projected stochastic subgradient method. arXiv preprint arXiv:1212.2002 (2012)."},{"key":"e_1_3_2_2_26_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Lee Namhoon","year":"2019","unstructured":"Namhoon Lee, Thalaiyasingam Ajanthan, and Philip H. S. Torr. 2019. Snip: single-Shot Network Pruning based on Connection sensitivity. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019. OpenReview.net."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00206"},{"key":"e_1_3_2_2_28_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Li Guihong","year":"2023","unstructured":"Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, and Radu Marculescu. 2023. ZiCo: Zero-shot NAS via inverse Coefficient of Variation on Gradients. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220023"},{"key":"e_1_3_2_2_30_1","volume-title":"The 22nd international conference on artificial intelligence and statistics. PMLR, 888--896","author":"Liang Tengyuan","year":"2019","unstructured":"Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, and James Stokes. 2019. Fisher-rao metric, geometry, and complexity of neural networks. In The 22nd international conference on artificial intelligence and statistics. PMLR, 888--896."},{"key":"e_1_3_2_2_31_1","volume-title":"Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition. In 2021 IEEE\/CVF International Conference on Computer Vision, ICCV","author":"Lin Ming","year":"2021","unstructured":"Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin. 2021. Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition. In 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00040"},{"key":"e_1_3_2_2_33_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Liu Hanxiao","year":"2018","unstructured":"Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2018. Darts: Differentiable architecture search. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86383-8_44"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455862"},{"key":"e_1_3_2_2_36_1","volume-title":"International Conference on Machine Learning. PMLR, 7588--7598","author":"Mellor Joe","year":"2021","unstructured":"Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley. 2021. Neural architecture search without training. In International Conference on Machine Learning. PMLR, 7588--7598."},{"key":"e_1_3_2_2_37_1","volume-title":"Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G Azzolini, et al.","author":"Naumov Maxim","year":"2019","unstructured":"Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G Azzolini, et al. 2019. Deep learning recommendation model for personalization and recommendation systems. arXiv preprint arXiv:1906.00091 (2019)."},{"key":"e_1_3_2_2_38_1","volume-title":"Wortman Vaughan (Eds.)","volume":"34","author":"Ning Xuefei","year":"2021","unstructured":"Xuefei Ning, Changcheng Tang, Wenshuo Li, Zixuan Zhou, Shuang Liang, Huazhong Yang, and Yu Wang. 2021. Evaluating Efficient Performance Estimators of Neural Architectures. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 12265--12277."},{"key":"e_1_3_2_2_39_1","volume-title":"Le","author":"Real Esteban","year":"2019","unstructured":"Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le. 2019. Regularized Evolution for Image Classifier Architecture Search. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (Honolulu, Hawaii, USA) (AAAI'19\/IAAI'19\/EAAI'19). AAAI Press, Article 587, 10 pages."},{"key":"e_1_3_2_2_40_1","volume-title":"International conference on machine learning. PMLR, 2902--2911","author":"Real Esteban","year":"2017","unstructured":"Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V Le, and Alexey Kurakin. 2017. Large-scale evolution of image classifiers. In International conference on machine learning. PMLR, 2902--2911."},{"key":"e_1_3_2_2_41_1","volume-title":"Fast curvature matrix-vector products for second-order gradient descent. Neural computation","author":"Schraudolph Nicol N","year":"2002","unstructured":"Nicol N Schraudolph. 2002. Fast curvature matrix-vector products for second-order gradient descent. Neural computation, Vol. 14, 7 (2002), 1723--1738."},{"key":"e_1_3_2_2_42_1","volume-title":"International conference on machine learning. PMLR, 5877--5886","author":"So David","year":"2019","unstructured":"David So, Quoc Le, and Chen Liang. 2019. The evolved transformer. In International conference on machine learning. PMLR, 5877--5886."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403137"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357925"},{"key":"e_1_3_2_2_45_1","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 481--497","author":"Stamoulis Dimitrios","year":"2019","unstructured":"Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, and Diana Marculescu. 2019. Single-path nas: Designing hardware-efficient convnets in less than 4 hours. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 481--497."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2924461"},{"key":"e_1_3_2_2_47_1","volume-title":"Lin (Eds.)","volume":"33","author":"Tanaka Hidenori","year":"2020","unstructured":"Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli. 2020. Pruning neural networks without any data by iteratively conserving synaptic flow. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 6377--6389."},{"key":"e_1_3_2_2_48_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 3503--3513","author":"Thomas Valentin","year":"2020","unstructured":"Valentin Thomas, Fabian Pedregosa, Bart Merri\u00ebnboer, Pierre-Antoine Manzagol, Yoshua Bengio, and Nicolas Le Roux. 2020. On the interplay between noise and curvature and its effect on optimization and generalization. In International Conference on Artificial Intelligence and Statistics. PMLR, 3503--3513."},{"key":"e_1_3_2_2_49_1","volume-title":"International Conference on Learning Representations.","author":"Turner Jack","year":"2020","unstructured":"Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey, and Gavin Gray. 2020. BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_50_1","volume-title":"International Conference on Learning Representations.","author":"Wang Chaoqi","year":"2020","unstructured":"Chaoqi Wang, Guodong Zhang, and Roger Grosse. 2020. Picking Winning Tickets Before Training by Preserving Gradient Flow. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_51_1","volume-title":"PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search. In International Conference on Machine Learning (ICML).","author":"Wang Haibin","year":"2023","unstructured":"Haibin Wang, Ce Ge, Hesen Chen, and Xiuyu Sun. 2023. PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_2_52_1","volume-title":"HAT: Hardware-Aware Transformers for Efficient Natural Language Processing. In Annual Conference of the Association for Computational Linguistics.","author":"Wang Hanrui","year":"2020","unstructured":"Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, and Song Han. 2020. HAT: Hardware-Aware Transformers for Efficient Natural Language Processing. In Annual Conference of the Association for Computational Linguistics."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450078"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3151160"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_39"},{"key":"e_1_3_2_2_56_1","volume-title":"A deeper look at zero-cost proxies for lightweight nas. ICLR Blog Track","author":"White Colin","year":"2022","unstructured":"Colin White, Mikhail Khodak, Renbo Tu, Shital Shah, S\u00e9bastien Bubeck, and Debadeepta Dey. 2022. A deeper look at zero-cost proxies for lightweight nas. ICLR Blog Track (2022)."},{"key":"e_1_3_2_2_57_1","unstructured":"Lei Wu Zhanxing Zhu et al. 2017. Towards understanding generalization of deep learning: Perspective of loss landscapes. arXiv preprint arXiv:1706.10239 (2017)."},{"volume-title":"Advances in Neural Information Processing Systems","author":"Xu Yi","key":"e_1_3_2_2_58_1","unstructured":"Yi Xu, Rong Jin, and Tianbao Yang. 2018. First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc."},{"volume-title":"Advances in Neural Information Processing Systems","author":"Yao Zhewei","key":"e_1_3_2_2_59_1","unstructured":"Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, and Michael W Mahoney. 2018. Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc."},{"key":"e_1_3_2_2_60_1","volume-title":"International conference on machine learning. PMLR, 7105--7114","author":"Ying Chris","year":"2019","unstructured":"Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, and Frank Hutter. 2019. Nas-bench-101: Towards reproducible neural architecture search. In International conference on machine learning. PMLR, 7105--7114."},{"key":"e_1_3_2_2_61_1","volume-title":"International Conference on Learning Representations.","author":"Zela Arber","year":"2021","unstructured":"Arber Zela, Julien Siems, Lucas Zimmer, Jovita Lukasik, Margret Keuper, and Frank Hutter. 2021. Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583446"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Barcelona Spain","acronym":"KDD '24"},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671712","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671712","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:00Z","timestamp":1750291560000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671712"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":62,"alternative-id":["10.1145\/3637528.3671712","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671712","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}