{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T11:40:22Z","timestamp":1778931622178,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":72,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3551349.3556906","type":"proceedings-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T20:43:54Z","timestamp":1672951434000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3490-4131","authenticated-orcid":false,"given":"Jie","family":"Zhu","sequence":"first","affiliation":[{"name":"Peking University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7627-8485","authenticated-orcid":false,"given":"Leye","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1331-0860","authenticated-orcid":false,"given":"Xiao","family":"Han","sequence":"additional","affiliation":[{"name":"Shanghai University of Finance and Economics, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_1_2_1","volume-title":"Do deep nets really need to be deep?Advances in neural information processing systems 27","author":"Ba Jimmy","year":"2014","unstructured":"Jimmy Ba and Rich Caruana. 2014. Do deep nets really need to be deep?Advances in neural information processing systems 27 (2014)."},{"key":"e_1_3_2_1_3_1","volume-title":"Test-driven Development - by example","author":"Beck L.","unstructured":"Kent\u00a0L. Beck. 2003. Test-driven Development - by example. In The Addison-Wesley signature series."},{"key":"e_1_3_2_1_4_1","volume-title":"Representation learning: A review and new perspectives","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35, 8(2013), 1798\u20131828."},{"key":"e_1_3_2_1_5_1","volume-title":"Language models are few-shot learners. Advances in neural information processing systems 33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared\u00a0D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877\u20131901."},{"key":"e_1_3_2_1_6_1","volume-title":"28th USENIX Security Symposium (USENIX Security 19)","author":"Carlini Nicholas","year":"2019","unstructured":"Nicholas Carlini, Chang Liu, \u00dalfar Erlingsson, Jernej Kos, and Dawn Song. 2019. The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th USENIX Security Symposium (USENIX Security 19). 267\u2013284."},{"key":"e_1_3_2_1_7_1","volume-title":"Chasing sparsity in vision transformers: An end-to-end exploration. Advances in Neural Information Processing Systems 34","author":"Chen Tianlong","year":"2021","unstructured":"Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, and Zhangyang Wang. 2021. Chasing sparsity in vision transformers: An end-to-end exploration. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_1_8_1","volume-title":"International conference on machine learning. PMLR, 2285\u20132294","author":"Chen Wenlin","year":"2015","unstructured":"Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen. 2015. Compressing neural networks with the hashing trick. In International conference on machine learning. PMLR, 2285\u20132294."},{"key":"e_1_3_2_1_9_1","unstructured":"Elliot\u00a0J. Crowley Gavin Gray and Amos\u00a0J Storkey. 2018. Moonshine: Distilling with Cheap Convolutions. In Advances in Neural Information Processing Systems S.\u00a0Bengio H.\u00a0Wallach H.\u00a0Larochelle K.\u00a0Grauman N.\u00a0Cesa-Bianchi and R.\u00a0Garnett (Eds.). Vol.\u00a031. Curran Associates Inc."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2019.2914438"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.2976475"},{"key":"e_1_3_2_1_12_1","unstructured":"Tim Dettmers and Luke Zettlemoyer. 2019. Sparse networks from scratch: Faster training without losing performance. arXiv preprint arXiv:1907.04840(2019)."},{"key":"e_1_3_2_1_13_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018).","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018)."},{"key":"e_1_3_2_1_14_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018).","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018)."},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Learning Representations (ICLR)","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. in International Conference on Learning Representations (ICLR) (2021)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1180475.1180478"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Jens Ernst William\u00a0S. Evans Christopher\u00a0W. Fraser Todd\u00a0A. Proebsting and Steven\u00a0E. Lucco. 1997. Code compression. In PLDI \u201997.","DOI":"10.1145\/258915.258947"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Machine Learning. PMLR, 2943\u20132952","author":"Evci Utku","year":"2020","unstructured":"Utku Evci, Trevor Gale, Jacob Menick, Pablo\u00a0Samuel Castro, and Erich Elsen. 2020. Rigging the lottery: Making all tickets winners. In International Conference on Machine Learning. PMLR, 2943\u20132952."},{"key":"e_1_3_2_1_20_1","unstructured":"Song Han Huizi Mao and William\u00a0J Dally. 2016. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. (2016)."},{"key":"e_1_3_2_1_21_1","volume-title":"Learning both weights and connections for efficient neural network. Advances in neural information processing systems 28","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. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2019.2934096"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_24_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2, 7","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, Jeff Dean, 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2, 7 (2015)."},{"key":"e_1_3_2_1_25_1","volume-title":"Binarized neural networks. Advances in neural information processing systems 29","author":"Hubara Itay","year":"2016","unstructured":"Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized neural networks. Advances in neural information processing systems 29 (2016)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_3_2_1_27_1","first-page":"20744","article-title":"Top-kast: Top-k always sparse training","volume":"33","author":"Jayakumar Siddhant","year":"2020","unstructured":"Siddhant Jayakumar, Razvan Pascanu, Jack Rae, Simon Osindero, and Erich Elsen. 2020. Top-kast: Top-k always sparse training. Advances in Neural Information Processing Systems 33 (2020), 20744\u201320754.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_28_1","volume-title":"28th USENIX Security Symposium (USENIX Security 19)","author":"Jayaraman Bargav","year":"2019","unstructured":"Bargav Jayaraman and David Evans. 2019. Evaluating differentially private machine learning in practice. In 28th USENIX Security Symposium (USENIX Security 19). 1895\u20131912."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363201"},{"key":"e_1_3_2_1_30_1","volume-title":"Tinybert: Distilling bert for natural language understanding. arXiv preprint arXiv:1909.10351(2019).","author":"Jiao Xiaoqi","year":"2019","unstructured":"Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2019. Tinybert: Distilling bert for natural language understanding. arXiv preprint arXiv:1909.10351(2019)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1218772110"},{"key":"e_1_3_2_1_32_1","unstructured":"Alex Krizhevsky Geoffrey Hinton 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_33_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey\u00a0E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012)."},{"key":"e_1_3_2_1_34_1","unstructured":"Agostina Larrazabal Cesar Martinez Jose Dolz and Enzo Ferrante. 2021. Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation. arXiv preprint arXiv:2112.12218(2021)."},{"key":"e_1_3_2_1_35_1","volume-title":"Tiny imagenet visual recognition challenge. CS 231N 7, 7","author":"Le Ya","year":"2015","unstructured":"Ya Le and Xuan Yang. 2015. Tiny imagenet visual recognition challenge. CS 231N 7, 7 (2015), 3."},{"key":"e_1_3_2_1_36_1","unstructured":"Fengfu Li Bo Zhang and Bin Liu. 2016. Ternary weight networks. arXiv preprint arXiv:1605.04711(2016)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05136-7"},{"key":"e_1_3_2_1_38_1","volume-title":"International Conference on Machine Learning. PMLR, 6989\u20137000","author":"Liu Shiwei","year":"2021","unstructured":"Shiwei Liu, Lu Yin, Decebal\u00a0Constantin Mocanu, and Mykola Pechenizkiy. 2021. Do we actually need dense over-parameterization? in-time over-parameterization in sparse training. In International Conference on Machine Learning. PMLR, 6989\u20137000."},{"key":"e_1_3_2_1_39_1","first-page":"9540","article-title":"Ensemble learning-based technique for force classifications in piezoelectric touch panels","volume":"20","author":"Liu Yong","year":"2020","unstructured":"Yong Liu, Shuo Gao, Anbiao Huang, Jie Zhu, Lijun Xu, and Arokia Nathan. 2020. Ensemble learning-based technique for force classifications in piezoelectric touch panels. IEEE Sensors Journal 20, 16 (2020), 9540\u20139549.","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_2_1_40_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692(2019).","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692(2019)."},{"key":"e_1_3_2_1_41_1","unstructured":"Yugeng Liu Rui Wen Xinlei He Ahmed Salem Zhikun Zhang Michael Backes Emiliano De\u00a0Cristofaro Mario Fritz and Yang Zhang. 2021. ML-Doctor: Holistic risk assessment of inference attacks against machine learning models. arXiv preprint arXiv:2102.02551(2021)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449855"},{"key":"e_1_3_2_1_45_1","unstructured":"Decebal\u00a0Constantin Mocanu 2017. Network computations in artificial intelligence. Technische Universiteit Eindhoven."},{"key":"e_1_3_2_1_46_1","volume-title":"Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature communications 9, 1","author":"Mocanu Decebal\u00a0Constantin","year":"2018","unstructured":"Decebal\u00a0Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong\u00a0H Nguyen, Madeleine Gibescu, and Antonio Liotta. 2018. Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature communications 9, 1 (2018), 1\u201312."},{"key":"e_1_3_2_1_47_1","volume-title":"Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature communications 9, 1","author":"Mocanu Decebal\u00a0Constantin","year":"2018","unstructured":"Decebal\u00a0Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong\u00a0H Nguyen, Madeleine Gibescu, and Antonio Liotta. 2018. Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature communications 9, 1 (2018), 1\u201312."},{"key":"e_1_3_2_1_48_1","volume-title":"International Conference on Machine Learning. PMLR, 4646\u20134655","author":"Mostafa Hesham","year":"2019","unstructured":"Hesham Mostafa and Xin Wang. 2019. Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization. In International Conference on Machine Learning. PMLR, 4646\u20134655."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243855"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"e_1_3_2_1_51_1","unstructured":"Gabriel Pereyra George Tucker Jan Chorowski \u0141ukasz Kaiser and Geoffrey Hinton. 2017. Regularizing neural networks by penalizing confident output distributions. arXiv preprint arXiv:1701.06548(2017)."},{"key":"e_1_3_2_1_52_1","unstructured":"William\u00a0W. Pugh. 1999. Compressing Java class files. In PLDI \u201999."},{"key":"e_1_3_2_1_53_1","first-page":"61","article-title":"Membership Inference Attack against Differentially Private Deep Learning","volume":"11","author":"Rahman Md\u00a0Atiqur","year":"2018","unstructured":"Md\u00a0Atiqur Rahman, Tanzila Rahman, Robert Lagani\u00e8re, Noman Mohammed, and Yang Wang. 2018. Membership Inference Attack against Differentially Private Deep Learning Model.Trans. Data Priv. 11, 1 (2018), 61\u201379.","journal-title":"Model.Trans. Data Priv."},{"key":"e_1_3_2_1_54_1","first-page":"15625","article-title":"Sparse weight activation training","volume":"33","author":"Raihan Md\u00a0Aamir","year":"2020","unstructured":"Md\u00a0Aamir Raihan and Tor Aamodt. 2020. Sparse weight activation training. Advances in Neural Information Processing Systems 33 (2020), 15625\u201315638.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_55_1","volume-title":"Scaling vision with sparse mixture of experts. Advances in Neural Information Processing Systems 34","author":"Riquelme Carlos","year":"2021","unstructured":"Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, Andr\u00e9 Susano\u00a0Pinto, Daniel Keysers, and Neil Houlsby. 2021. Scaling vision with sparse mixture of experts. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_1_56_1","volume-title":"International Conference on Machine Learning. PMLR, 5558\u20135567","author":"Sablayrolles Alexandre","year":"2019","unstructured":"Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Herv\u00e9 J\u00e9gou. 2019. White-box vs black-box: Bayes optimal strategies for membership inference. In International Conference on Machine Learning. PMLR, 5558\u20135567."},{"key":"e_1_3_2_1_57_1","volume-title":"In Network and Distributed System Symposium.","author":"Salem Ahmed","year":"2019","unstructured":"Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2019. In Network and Distributed System Symposium."},{"key":"e_1_3_2_1_58_1","volume-title":"Membership privacy for machine learning models through knowledge transfer. AAAI","author":"Shejwalkar Virat","year":"2021","unstructured":"Virat Shejwalkar and Amir Houmansadr. 2021. Membership privacy for machine learning models through knowledge transfer. AAAI (2021)."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"e_1_3_2_1_60_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs\/1409.1556(2015)."},{"key":"e_1_3_2_1_61_1","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"Song Liwei","year":"2021","unstructured":"Liwei Song and Prateek Mittal. 2021. Systematic evaluation of privacy risks of machine learning models. In 30th USENIX Security Symposium (USENIX Security 21). 2615\u20132632."},{"key":"e_1_3_2_1_62_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929\u20131958."},{"key":"e_1_3_2_1_63_1","volume-title":"GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461(2018).","author":"Wang Alex","year":"2018","unstructured":"Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel\u00a0R Bowman. 2018. GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461(2018)."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/432"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"crossref","unstructured":"Andrew Wolfe and Alex Chanin. 1992. Executing compressed programs on an embedded RISC architecture. In MICRO 25.","DOI":"10.1145\/144965.145003"},{"key":"e_1_3_2_1_66_1","volume-title":"Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st computer security foundations symposium (CSF)","author":"Yeom Samuel","unstructured":"Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018. Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st computer security foundations symposium (CSF). IEEE, 268\u2013282."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17284"},{"key":"e_1_3_2_1_68_1","unstructured":"Xiaoyong Yuan and Lan Zhang. 2022. Membership Inference Attacks and Defenses in Neural Network Pruning. arXiv preprint arXiv:2202.03335(2022)."},{"key":"e_1_3_2_1_69_1","volume-title":"Character-level convolutional networks for text classification. Advances in neural information processing systems 28","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_3_2_1_70_1","volume-title":"Character-level convolutional networks for text classification. Advances in neural information processing systems 28","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_3_2_1_71_1","volume-title":"44th International Conference on Software Engineering (ICSE).","author":"Zhang Ziqi","year":"2022","unstructured":"Ziqi Zhang, Yuanchun Li, Jindong Wang, Bingyan Liu, Ding Li, Xiangqun Chen, Yao Guo, and Yunxin Liu. 2022. ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing. In 44th International Conference on Software Engineering (ICSE)."},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.04.082"}],"event":{"name":"ASE '22: 37th IEEE\/ACM International Conference on Automated Software Engineering","location":"Rochester MI USA","acronym":"ASE '22"},"container-title":["Proceedings of the 37th IEEE\/ACM International Conference on Automated Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551349.3556906","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3551349.3556906","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T07:53:45Z","timestamp":1755849225000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551349.3556906"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":72,"alternative-id":["10.1145\/3551349.3556906","10.1145\/3551349"],"URL":"https:\/\/doi.org\/10.1145\/3551349.3556906","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2023-01-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}