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Surv."],"published-print":{"date-parts":[[2026,10,31]]},"abstract":"<jats:p>Noise, traditionally considered a nuisance in computational systems, is reconsidered for its unexpected and counter-intuitive benefits across a wide spectrum of domains, including nonlinear information processing, signal processing, image processing, machine learning, network science, and natural language processing. Through a comprehensive review of both historical and contemporary research, this survey presents a dual perspective on noise, acknowledging its potential to both disrupt and enhance performance. Particularly, we highlight how noise-enhanced training strategies can lead to models that better generalize from noisy data, positioning noise not just as a challenge to overcome but as a strategic tool for improvement. This work calls for a shift in how we perceive noise, proposing that it can be a spark for innovation and advancement in the information era.<\/jats:p>","DOI":"10.1145\/3811813","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:14:41Z","timestamp":1778152481000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Harnessing the Power of Noise: A Survey of Techniques and Applications"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7330-0589","authenticated-orcid":false,"given":"Reyhaneh","family":"Abdolazimi","sequence":"first","affiliation":[{"name":"EECS Department, Syracuse University","place":["Syracuse, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2882-5437","authenticated-orcid":false,"given":"Shengmin","family":"Jin","sequence":"additional","affiliation":[{"name":"Amazon.com Inc","place":["New York, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4504-5088","authenticated-orcid":false,"given":"Pramod","family":"Varshney","sequence":"additional","affiliation":[{"name":"EECS Department, Syracuse University","place":["Syracuse, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0352-848X","authenticated-orcid":false,"given":"Reza","family":"Zafarani","sequence":"additional","affiliation":[{"name":"Syracuse University","place":["Syracuse, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1145\/3372923.3404788","volume-title":"Proceedings of the 31st ACM Conference on Hypertext and Social Media","author":"Abdolazimi Reyhaneh","year":"2020","unstructured":"Reyhaneh Abdolazimi, Shengmin Jin, and Reza Zafarani. 2020. 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Retrieved from https:\/\/arxiv.org\/abs\/2106.07971"},{"key":"e_1_3_1_31_2","volume-title":"Deep Learning","author":"Goodfellow Ian","year":"2016","unstructured":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press."},{"key":"e_1_3_1_32_2","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems 27 (2014), 2672\u20132680.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_33_2","unstructured":"Brett R. Gordon Robert Moakler and Florian Zettelmeyer. 2023. Predictive incrementality by experimentation (pie) for ad measurement. arXiv:2304.06828. Retrieved from https:\/\/arxiv.org\/abs\/2304.06828"},{"key":"e_1_3_1_34_2","first-page":"6645","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing","author":"Graves Alex","year":"2013","unstructured":"Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 6645\u20136649."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/18.720541"},{"issue":"20","key":"e_1_3_1_36_2","doi-asserted-by":"crossref","first-page":"4773","DOI":"10.1103\/PhysRevLett.84.4773","article-title":"Stochastic resonance enhances the electrosensory information available to paddlefish for prey capture","volume":"84","author":"Greenwood Priscilla E.","year":"2000","unstructured":"Priscilla E. Greenwood, Lawrence M. Ward, David F. Russell, Alexander Neiman, and Frank Moss. 2000. 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DOI:10.1145\/168304.168306"},{"issue":"7","key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1049\/el:20060180","article-title":"Constructive action of noise for impulsive noise removal in scalar images","volume":"42","author":"Histace Aymeric","year":"2006","unstructured":"Aymeric Histace and David Rousseau. 2006. Constructive action of noise for impulsive noise removal in scalar images. Electronics Letters 42, 7 (2006), 393\u2013395.","journal-title":"Electronics Letters"},{"issue":"1","key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/72.105415","article-title":"Using additive noise in back-propagation training","volume":"3","author":"Holmstrom Lasse","year":"1992","unstructured":"Lasse Holmstrom and Petri Koistinen. 1992. Using additive noise in back-propagation training. IEEE Transactions on Neural Networks 3, 1 (1992), 24\u201338.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"9","key":"e_1_3_1_42_2","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/TPAMI.2003.1227982","article-title":"The resonant retina: Exploiting vibration noise to optimally detect edges in an image","volume":"25","author":"Hongler M.-O.","year":"2003","unstructured":"M.-O. Hongler, Yuri L\u00f3pez de Meneses, Antoine Beyeler, and Jacques Jacot. 2003. The resonant retina: Exploiting vibration noise to optimally detect edges in an image. 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Retrieved from https:\/\/arxiv.org\/abs\/2310.05914"},{"issue":"6","key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1002\/j.1538-7305.1972.tb02653.x","article-title":"The application of dither to the quantization of speech signals","volume":"51","author":"Jayant N. S.","year":"1972","unstructured":"N. S. Jayant and L. R. Rabiner. 1972. The application of dither to the quantization of speech signals. Bell System Technical Journal 51, 6 (1972), 1293\u20131304.","journal-title":"Bell System Technical Journal"},{"issue":"6","key":"e_1_3_1_46_2","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/72.548170","article-title":"An analysis of noise in recurrent neural networks: convergence and generalization","volume":"7","author":"Jim Kam-Chuen","year":"1996","unstructured":"Kam-Chuen Jim, C. Lee Giles, and Bill G. Horne. 1996. An analysis of noise in recurrent neural networks: convergence and generalization. IEEE Transactions on Neural Networks 7, 6 (1996), 1424\u20131438.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"e_1_3_1_47_2","volume-title":"Proceedings of the ICLR (Poster)","author":"Jordon James","year":"2019","unstructured":"James Jordon, Jinsung Yoon, and Mihaela van der Schaar. 2019. PATE-GAN: Generating synthetic data with differential privacy guarantees. In Proceedings of the ICLR (Poster). Retrieved from http:\/\/dblp.uni-trier.de\/db\/conf\/iclr\/iclr2019.html#JordonYS19a"},{"issue":"1","key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/97.809511","article-title":"Can detectability be improved by adding noise?","volume":"7","author":"Kay Steven","year":"2000","unstructured":"Steven Kay. 2000. Can detectability be improved by adding noise? 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Retrieved from https:\/\/arxiv.org\/abs\/1312.6114"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"key":"e_1_3_1_53_2","first-page":"60","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Kong Kezhi","year":"2022","unstructured":"Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein. 2022. Robust optimization as data augmentation for large-scale graphs. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 60\u201369."},{"key":"e_1_3_1_54_2","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.neunet.2020.04.004","article-title":"Noise can speed backpropagation learning and deep bidirectional pretraining","volume":"129","author":"Kosko Bart","year":"2020","unstructured":"Bart Kosko, Kartik Audhkhasi, and Osonde Osoba. 2020. 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Advances in Neural Information Processing Systems 33 (2020), 9459\u20139474.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103855"},{"key":"e_1_3_1_59_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Liu Songtao","year":"2022","unstructured":"Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, and Dinghao Wu. 2022. Local augmentation for graph neural networks. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_60_2","unstructured":"Xiaogeng Liu Haoyu Wang Yechao Zhang Fangzhou Wu and Shengshan Hu. 2022. Towards efficient data-centric robust machine learning with noise-based augmentation. arXiv:2203.03810. Retrieved from https:\/\/arxiv.org\/abs\/2203.03810"},{"key":"e_1_3_1_61_2","first-page":"2145","volume-title":"Proceedings of the 29th ACM International Conference on Information and Knowledge Management","author":"Mansoury Masoud","year":"2020","unstructured":"Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2020. Feedback loop and bias amplification in recommender systems. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 2145\u20132148."},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511535239"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000348"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.2307\/2965732"},{"key":"e_1_3_1_65_2","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1109\/SSCI.2018.8628917","volume-title":"Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI)","author":"Moreno-Barea Francisco J.","year":"2018","unstructured":"Francisco J. Moreno-Barea, Fiammetta Strazzera, Jos\u00e9 M. Jerez, Daniel Urda, and Leonardo Franco. 2018. Forward noise adjustment scheme for data augmentation. In Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 728\u2013734."},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.317730"},{"issue":"5","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1109\/72.317730","article-title":"Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training","volume":"5","author":"Murray Alan F.","year":"1994","unstructured":"Alan F. Murray and Peter J. Edwards. 1994. Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training. IEEE Transactions on Neural Networks 5, 5 (1994), 792\u2013802.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"e_1_3_1_68_2","unstructured":"Markus Nagel Rana Ali Amjad Mart van Baalen Christos Louizos and Tijmen Blankevoort. 2020. Up or Down? Adaptive Rounding for Post-Training Quantization. arXiv:2004.10568. Retrieved from https:\/\/arxiv.org\/abs\/2004.10568"},{"key":"e_1_3_1_69_2","first-page":"1493","volume-title":"Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP)","author":"Nataraj L.","year":"2009","unstructured":"L. Nataraj, A. Sarkar, and B. S. Manjunath. 2009. Adding gaussian noise to \u201cdenoise\u201d JPEG for detecting image resizing. In Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP). 1493\u20131496."},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-5014-9_12"},{"key":"e_1_3_1_71_2","unstructured":"Arvind Neelakantan Luke Vilnis Quoc V. Le Ilya Sutskever Lukasz Kaiser Karol Kurach and James Martens. 2015. Adding Gradient Noise Improves Learning for Very Deep Networks. arXiv:1511.06807. Retrieved from https:\/\/arxiv.org\/abs\/1511.06807"},{"key":"e_1_3_1_72_2","unstructured":"Arvind Neelakantan Luke Vilnis Quoc V. 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Retrieved from https:\/\/arxiv.org\/abs\/1406.1831"},{"issue":"2","key":"e_1_3_1_81_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/5.18626","article-title":"A tutorial on hidden Markov models and selected applications in speech recognition","volume":"77","author":"Rabiner Lawrence R.","year":"1989","unstructured":"Lawrence R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 2 (1989), 257\u2013286.","journal-title":"Proceedings of the IEEE"},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2008.02.001"},{"issue":"4","key":"e_1_3_1_83_2","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.compmedimag.2008.02.001","article-title":"Enhancement of ultrasound images using stochastic resonance-based wavelet transform","volume":"32","author":"Rallabandi V. P. Subramanyam","year":"2008","unstructured":"V. P. Subramanyam Rallabandi. 2008. 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IRE Transactions on Information Theory 8, 2 (1962), 145\u2013154.","journal-title":"IRE Transactions on Information Theory"},{"key":"e_1_3_1_86_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. DropEdge: Towards deep graph convolutional networks on node classification. In Proceedings of the International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=Hkx1qkrKPr"},{"issue":"3","key":"e_1_3_1_87_2","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.sigpro.2004.11.002","article-title":"Constructive role of noise in signal detection from parallel arrays of quantizers","volume":"85","author":"Rousseau David","year":"2005","unstructured":"David Rousseau and Fran\u00e7ois Chapeau-Blondeau. 2005. Constructive role of noise in signal detection from parallel arrays of quantizers. 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Digital Signal Processing 15, 1 (2005), 19\u201332.","journal-title":"Digital Signal Processing"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2010.09.008"},{"issue":"2","key":"e_1_3_1_90_2","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neulet.2006.08.054","article-title":"Effect of noise on the contrast detection threshold in visual perception","volume":"408","author":"Sasaki Hitoshi","year":"2006","unstructured":"Hitoshi Sasaki, Masayoshi Todorokihara, Takuya Ishida, Junichiro Miyachi, Tahei Kitamura, and Ryozo Aoki. 2006. Effect of noise on the contrast detection threshold in visual perception. Neuroscience Letters 408, 2 (2006), 94\u201397.","journal-title":"Neuroscience Letters"},{"key":"e_1_3_1_91_2","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1137\/1.9781611976700.38","volume-title":"Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)","author":"Sato Ryoma","year":"2021","unstructured":"Ryoma Sato, Makoto Yamada, and Hisashi Kashima. 2021. Random features strengthen graph neural networks. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). SIAM, 333\u2013341."},{"issue":"4","key":"e_1_3_1_92_2","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TCOM.1964.1088973","article-title":"Dither signals and their effect on quantization noise","volume":"12","author":"Schuchman Leonard","year":"1964","unstructured":"Leonard Schuchman. 1964. Dither signals and their effect on quantization noise. IEEE Transactions on Communication Technology 12, 4 (1964), 162\u2013165.","journal-title":"IEEE Transactions on Communication Technology"},{"key":"e_1_3_1_93_2","first-page":"703","volume-title":"Proceedings of the 1990 IJCNN International Joint Conference on Neural Networks","author":"Sequin Carlo H.","year":"1990","unstructured":"Carlo H. Sequin and R. D. Clay. 1990. Fault tolerance in artificial neural networks. In Proceedings of the 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 703\u2013708."},{"key":"e_1_3_1_94_2","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781107298019","volume-title":"Understanding Machine Learning: From Theory to Algorithms","author":"Shalev-Shwartz Shai","year":"2014","unstructured":"Shai Shalev-Shwartz and Shai Ben-David. 2014. Understanding Machine Learning: From Theory to Algorithms. 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Journal of Machine Learning Research 15, 56 (2014), 1929\u20131958. Retrieved from http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html","journal-title":"Journal of Machine Learning Research"},{"issue":"6","key":"e_1_3_1_100_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/79.543973","article-title":"Genetic algorithms and their applications","volume":"13","author":"Tang Kit-Sang","year":"1996","unstructured":"Kit-Sang Tang, Kim-Fung Man, Sam Kwong, and Qun He. 1996. Genetic algorithms and their applications. IEEE Signal Processing Magazine 13, 6 (1996), 22\u201337.","journal-title":"IEEE Signal Processing Magazine"},{"issue":"20","key":"e_1_3_1_101_2","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1109\/TSP.2015.2457394","article-title":"Bayes risk reduction of estimators using artificial observation noise","volume":"63","author":"Uhlich Stefan","year":"2015","unstructured":"Stefan Uhlich. 2015. 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Journal of Machine Learning Research 11, 12 (2010), 3371\u20133408.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_104_2","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1145\/3383313.3412225","volume-title":"Proceedings of the 14th ACM Conference on Recommender Systems","author":"Wang Yixin","year":"2020","unstructured":"Yixin Wang, Dawen Liang, Laurent Charlin, and David M. Blei. 2020. Causal inference for recommender systems. In Proceedings of the 14th ACM Conference on Recommender Systems. 426\u2013431."},{"issue":"1","key":"e_1_3_1_105_2","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1103\/PhysRevE.61.233","article-title":"Stochastic resonance as dithering","volume":"61","author":"Wannamaker Robert A.","year":"2000","unstructured":"Robert A. Wannamaker, Stanley P. Lipshitz, and John Vanderkooy. 2000. Stochastic resonance as dithering. 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Retrieved from https:\/\/arxiv.org\/abs\/1901.11196"},{"issue":"6509","key":"e_1_3_1_108_2","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/373033a0","article-title":"Stochastic resonance and the benefits of noise: From ice ages to crayfish and SQUIDs","volume":"373","author":"Wiesenfeld Kurt","year":"1995","unstructured":"Kurt Wiesenfeld and Frank Moss. 1995. Stochastic resonance and the benefits of noise: From ice ages to crayfish and SQUIDs. Nature 373, 6509 (1995), 33\u201336.","journal-title":"Nature"},{"issue":"11","key":"e_1_3_1_109_2","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1109\/29.103088","article-title":"Automatic recognition of keywords in unconstrained speech using hidden Markov models","volume":"38","author":"Wilpon Jay G.","year":"1990","unstructured":"Jay G. Wilpon, Lawrence R. Rabiner, C.-H. Lee, and E. R. Goldman. 1990. Automatic recognition of keywords in unconstrained speech using hidden Markov models. 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ICIP\u201904.","author":"Ye Qinghua","year":"2004","unstructured":"Qinghua Ye, Haining Huang, and Chunhua Zhang. 2004. Image enhancement using stochastic resonance [sonar image processing applications]. In Proceedings of the 2004 International Conference on Image Processing, 2004. ICIP\u201904. IEEE, 263\u2013266."},{"key":"e_1_3_1_113_2","first-page":"I\u2013849","volume-title":"Proceedings of the 2003 International Conference on Image Processing (Cat. No. 03CH37429)","author":"Ye Qinghua","year":"2003","unstructured":"Qinghua Ye, Raining Huang, Xinyi He, and Chimhua Zhang. 2003. A SR-based radon transform to extract weak lines from noise images. In Proceedings of the 2003 International Conference on Image Processing (Cat. No. 03CH37429). IEEE, I\u2013849."},{"key":"e_1_3_1_114_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3103980"},{"key":"e_1_3_1_115_2","unstructured":"Hongyi Zhang Moustapha Cisse Yann N. Dauphin and David Lopez-Paz. 2017. Mixup: Beyond empirical risk minimization. arXiv:1710.09412. Retrieved from https:\/\/arxiv.org\/abs\/1710.09412"},{"issue":"4","key":"e_1_3_1_116_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3134428","article-title":"Privbayes: Private data release via bayesian networks","volume":"42","author":"Zhang Jun","year":"2017","unstructured":"Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao. 2017. Privbayes: Private data release via bayesian networks. ACM Transactions on Database Systems 42, 4 (2017), 1\u201341.","journal-title":"ACM Transactions on Database Systems"},{"key":"e_1_3_1_117_2","first-page":"11015","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhao Tong","year":"2021","unstructured":"Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah. 2021. Data augmentation for graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence. 11015\u201311023."},{"key":"e_1_3_1_118_2","doi-asserted-by":"crossref","unstructured":"Maxim Zhelnin Viktor Moskvoretskii Egor Shvetsov Egor Venediktov Mariya Krylova Aleksandr Zuev and Evgeny Burnaev. 2024. GIFT-SW: Gaussian noise injected fine-tuning of salient weights for LLMs. arXiv:2408.15300. Retrieved from https:\/\/arxiv.org\/abs\/2408.15300","DOI":"10.18653\/v1\/2025.acl-long.324"},{"issue":"21","key":"e_1_3_1_119_2","doi-asserted-by":"crossref","first-page":"4967","DOI":"10.1364\/OL.41.004967","article-title":"Object extraction from underwater images through logical stochastic resonance","volume":"41","author":"Zheng Bing","year":"2016","unstructured":"Bing Zheng, Nan Wang, Haiyong Zheng, Zhibin Yu, and Jinpeng Wang. 2016. Object extraction from underwater images through logical stochastic resonance. 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