{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:14:28Z","timestamp":1762377268060,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":49,"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\/3503161.3547887","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:42:35Z","timestamp":1665416555000},"page":"5065-5073","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Numerical DEs Perspective on Unfolded Linearized ADMM Networks for Inverse Problems"],"prefix":"10.1145","author":[{"given":"Weixin","family":"An","sequence":"first","affiliation":[{"name":"Xidian University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Yue","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fanhua","family":"Shang","sequence":"additional","affiliation":[{"name":"Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongying","family":"Liu","sequence":"additional","affiliation":[{"name":"Xidian University & Peng Cheng Laboratory, Xi'an & Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Ravi P Agarwal and Donal O'Regan. 2008. An Introduction to Ordinary Differential Equations. (2008).  Ravi P Agarwal and Donal O'Regan. 2008. An Introduction to Ordinary Differential Equations. (2008).","DOI":"10.1007\/978-0-387-71276-5"},{"volume-title":"Hamiltonian and gradient flows, algorithms and control","author":"Bloch Anthony","key":"e_1_3_2_2_2_1","unstructured":"Anthony Bloch . 1994. Hamiltonian and gradient flows, algorithms and control . Vol. 3 . American Mathematical Soc . Anthony Bloch. 1994. Hamiltonian and gradient flows, algorithms and control. Vol. 3. American Mathematical Soc."},{"volume-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","author":"Boyd Stephen","key":"e_1_3_2_2_3_1","unstructured":"Stephen Boyd , Neal Parikh , and Eric Chu . 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers . Now Publishers Inc . Stephen Boyd, Neal Parikh, and Eric Chu. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3221338.3221917"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_31"},{"key":"e_1_3_2_2_6_1","volume-title":"Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds. Advances in neural information processing systems","author":"Chen Xiaohan","year":"2018","unstructured":"Xiaohan Chen , Jialin Liu , Zhangyang Wang , and Wotao Yin . 2018. Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds. Advances in neural information processing systems ( 2018 ). Xiaohan Chen, Jialin Liu, Zhangyang Wang, and Wotao Yin. 2018. Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds. Advances in neural information processing systems (2018)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10915-016-0318-2"},{"key":"e_1_3_2_2_8_1","volume-title":"Denoising prior driven deep neural network for image restoration","author":"Dong Weisheng","year":"2018","unstructured":"Weisheng Dong , Peiyao Wang , Wotao Yin , Guangming Shi , Fangfang Wu , and Xiaotong Lu. 2018. Denoising prior driven deep neural network for image restoration . IEEE transactions on pattern analysis and machine intelligence 41, 10 ( 2018 ), 2305--2318. Weisheng Dong, Peiyao Wang, Wotao Yin, Guangming Shi, Fangfang Wu, and Xiaotong Lu. 2018. Denoising prior driven deep neural network for image restoration. IEEE transactions on pattern analysis and machine intelligence 41, 10 (2018), 2305--2318."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.09.035"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/MMSP53017.2021.9733691"},{"key":"e_1_3_2_2_11_1","volume-title":"A nonsmooth dynamical systems perspective on accelerated extensions of ADMM","author":"Fran\u00e7a G","year":"2018","unstructured":"G Fran\u00e7a , DP Robinson , and R Vidal . 1808. A nonsmooth dynamical systems perspective on accelerated extensions of ADMM ( 2018 ). Preprint . arXiv (1808). G Fran\u00e7a, DP Robinson, and R Vidal. 1808. A nonsmooth dynamical systems perspective on accelerated extensions of ADMM (2018). Preprint. arXiv (1808)."},{"key":"e_1_3_2_2_12_1","volume-title":"International Conference on Machine Learning. PMLR, 1559--1567","author":"Franca Guilherme","year":"2018","unstructured":"Guilherme Franca , Daniel Robinson , and Rene Vidal . 2018 . ADMM and accelerated ADMM as continuous dynamical systems . In International Conference on Machine Learning. PMLR, 1559--1567 . Guilherme Franca, Daniel Robinson, and Rene Vidal. 2018. ADMM and accelerated ADMM as continuous dynamical systems. In International Conference on Machine Learning. PMLR, 1559--1567."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2019.2948732"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1137\/120896219"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104374"},{"key":"e_1_3_2_2_16_1","volume-title":"Ha Q Nguyen, Michael T McCann, and Michael Unser.","author":"Gupta Harshit","year":"2018","unstructured":"Harshit Gupta , Kyong Hwan Jin , Ha Q Nguyen, Michael T McCann, and Michael Unser. 2018 . CNN-based projected gradient descent for consistent CT image reconstruction. IEEE transactions on medical imaging 37, 6 (2018), 1440--1453. Harshit Gupta, Kyong Hwan Jin, Ha Q Nguyen, Michael T McCann, and Michael Unser. 2018. CNN-based projected gradient descent for consistent CT image reconstruction. IEEE transactions on medical imaging 37, 6 (2018), 1440--1453."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"volume-title":"Optimization and dynamical systems","author":"Helmke Uwe","key":"e_1_3_2_2_18_1","unstructured":"Uwe Helmke and John B Moore . 2012. Optimization and dynamical systems . Springer Science & Business Media . Uwe Helmke and John B Moore. 2012. Optimization and dynamical systems. Springer Science & Business Media."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.23919\/Eusipco47968.2020.9287399"},{"key":"e_1_3_2_2_20_1","volume-title":"ADMM-DAD net: a deep unfolding network for analysis compressed sensing. arXiv preprint arXiv:2110.06986","author":"Kouni Vasiliki","year":"2021","unstructured":"Vasiliki Kouni , Georgios Paraskevopoulos , Holger Rauhut , and George C Alexandropoulos . 2021. ADMM-DAD net: a deep unfolding network for analysis compressed sensing. arXiv preprint arXiv:2110.06986 ( 2021 ). Vasiliki Kouni, Georgios Paraskevopoulos, Holger Rauhut, and George C Alexandropoulos. 2021. ADMM-DAD net: a deep unfolding network for analysis compressed sensing. arXiv preprint arXiv:2110.06986 (2021)."},{"key":"e_1_3_2_2_21_1","volume-title":"International Conference on Multimedia and Network Information System. Springer, 322--331","author":"Li Honggui","year":"2018","unstructured":"Honggui Li and Maria Trocan . 2018 . Sparse solution to inverse problem of nonlinear dimensionality reduction . In International Conference on Multimedia and Network Information System. Springer, 322--331 . Honggui Li and Maria Trocan. 2018. Sparse solution to inverse problem of nonlinear dimensionality reduction. In International Conference on Multimedia and Network Information System. Springer, 322--331."},{"key":"e_1_3_2_2_22_1","volume-title":"Learning to optimize. arXiv preprint arXiv:1606.01885","author":"Li Ke","year":"2016","unstructured":"Ke Li and Jitendra Malik . 2016. Learning to optimize. arXiv preprint arXiv:1606.01885 ( 2016 ). Ke Li and Jitendra Malik. 2016. Learning to optimize. arXiv preprint arXiv:1606.01885 (2016)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14040901"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17032"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/2986459.2986528"},{"key":"e_1_3_2_2_26_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Liu Jialin","year":"2019","unstructured":"Jialin Liu and Xiaohan Chen . 2019 . ALISTA: Analytic weights are as good as learned weights in LISTA . In International Conference on Learning Representations (ICLR). Jialin Liu and Xiaohan Chen. 2019. ALISTA: Analytic weights are as good as learned weights in LISTA. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/3015812.3015922"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.3016905"},{"key":"e_1_3_2_2_29_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Moreau Thomas","year":"2019","unstructured":"Thomas Moreau and Joan Bruna . 2019 . Understanding trainable sparse coding via matrix factorization . In 5th International Conference on Learning Representations, ICLR 2017. Thomas Moreau and Joan Bruna. 2019. Understanding trainable sparse coding via matrix factorization. In 5th International Conference on Learning Representations, ICLR 2017."},{"key":"e_1_3_2_2_30_1","volume-title":"International Conference on Machine Learning. PMLR, 80--88","author":"Ouyang Hua","year":"2013","unstructured":"Hua Ouyang , Niao He , Long Tran , and Alexander Gray . 2013 . Stochastic alternating direction method of multipliers . In International Conference on Machine Learning. PMLR, 80--88 . Hua Ouyang, Niao He, Long Tran, and Alexander Gray. 2013. Stochastic alternating direction method of multipliers. In International Conference on Machine Learning. PMLR, 80--88."},{"key":"e_1_3_2_2_31_1","volume-title":"Jos\u00e9 Ignacio Mart\u00ednez Torre, and Henry Arguello Fuentes","author":"Ram\u00edrez Juan Marcos","year":"2021","unstructured":"Juan Marcos Ram\u00edrez , Jos\u00e9 Ignacio Mart\u00ednez Torre, and Henry Arguello Fuentes . 2021 . LADMM-Net: An Unrolled Deep Network For Spectral Image Fusion From Compressive Data . arXiv preprint arXiv:2103.00940 (2021). Juan Marcos Ram\u00edrez, Jos\u00e9 Ignacio Mart\u00ednez Torre, and Henry Arguello Fuentes. 2021. LADMM-Net: An Unrolled Deep Network For Spectral Image Fusion From Compressive Data. arXiv preprint arXiv:2103.00940 (2021)."},{"key":"e_1_3_2_2_32_1","volume-title":"International Conference on Machine Learning. PMLR, 9276--9287","author":"Sander Michael E","year":"2021","unstructured":"Michael E Sander , Pierre Ablin , Mathieu Blondel , and Gabriel Peyr\u00e9 . 2021 . Momentum residual neural networks . In International Conference on Machine Learning. PMLR, 9276--9287 . Michael E Sander, Pierre Ablin, Mathieu Blondel, and Gabriel Peyr\u00e9. 2021. Momentum residual neural networks. In International Conference on Machine Learning. PMLR, 9276--9287."},{"key":"e_1_3_2_2_33_1","volume-title":"A dynamical systems approach to constrained minimization. Numerical functional analysis and optimization 21, 3--4","author":"Schropp Johannes","year":"2000","unstructured":"Johannes Schropp and I Singer . 2000. A dynamical systems approach to constrained minimization. Numerical functional analysis and optimization 21, 3--4 ( 2000 ), 537--551. Johannes Schropp and I Singer. 2000. A dynamical systems approach to constrained minimization. Numerical functional analysis and optimization 21, 3--4 (2000), 537--551."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01257"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.3053435"},{"key":"e_1_3_2_2_36_1","unstructured":"Jian Sun Huibin Li Zongben Xu etal 2016. Deep ADMM-Net for compressive sensing MRI. Advances in neural information processing systems 29 (2016).  Jian Sun Huibin Li Zongben Xu et al. 2016. Deep ADMM-Net for compressive sensing MRI. Advances in neural information processing systems 29 (2016)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3023629"},{"key":"e_1_3_2_2_38_1","volume-title":"Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing. arXiv preprint arXiv:2106.03686","author":"Thanthrige Udaya SKP","year":"2021","unstructured":"Udaya SKP Thanthrige , Peter Jung , and Aydin Sezgin . 2021. Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing. arXiv preprint arXiv:2106.03686 ( 2021 ). Udaya SKP Thanthrige, Peter Jung, and Aydin Sezgin. 2021. Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing. arXiv preprint arXiv:2106.03686 (2021)."},{"key":"e_1_3_2_2_39_1","volume-title":"InverseNet: Solving Inverse Problems of Multimedia Data with Splitting Networks. In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1324--1329","author":"Wei Qi","year":"2019","unstructured":"Qi Wei , Kai Fan , Wenlin Wang , Tianhang Zheng , Chakraborty Amit , Katherine Heller , Changyou Chen , and Kui Ren . 2019 . InverseNet: Solving Inverse Problems of Multimedia Data with Splitting Networks. In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1324--1329 . Qi Wei, Kai Fan, Wenlin Wang, Tianhang Zheng, Chakraborty Amit, Katherine Heller, Changyou Chen, and Kui Ren. 2019. InverseNet: Solving Inverse Problems of Multimedia Data with Splitting Networks. In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1324--1329."},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Learning Representations.","author":"Wu Kailun","year":"2019","unstructured":"Kailun Wu , Yiwen Guo , Ziang Li , and Changshui Zhang . 2019 . Sparse coding with gated learned ISTA . In International Conference on Learning Representations. Kailun Wu, Yiwen Guo, Ziang Li, and Changshui Zhang. 2019. Sparse coding with gated learned ISTA. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_41_1","volume-title":"International Conference on Machine Learning. PMLR, 6902--6911","author":"Xie Xingyu","year":"2019","unstructured":"Xingyu Xie , Jianlong Wu , Guangcan Liu , Zhisheng Zhong , and Zhouchen Lin . 2019 . Differentiable linearized admm . In International Conference on Machine Learning. PMLR, 6902--6911 . Xingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, and Zhouchen Lin. 2019. Differentiable linearized admm. In International Conference on Machine Learning. PMLR, 6902--6911."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.3934\/ipi.2016012"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2883941"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME51207.2021.9428249"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3091834"},{"key":"e_1_3_2_2_46_1","volume-title":"International Conference on Machine Learning. PMLR, 7232--7241","author":"Yuan Huizhuo","year":"2019","unstructured":"Huizhuo Yuan , Yuren Zhou , Chris Junchi Li , and Qingyun Sun . 2019 . Differential inclusions for modeling nonsmooth ADMM variants: A continuous limit theory . In International Conference on Machine Learning. PMLR, 7232--7241 . Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, and Qingyun Sun. 2019. Differential inclusions for modeling nonsmooth ADMM variants: A continuous limit theory. In International Conference on Machine Learning. PMLR, 7232--7241."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00196"},{"key":"e_1_3_2_2_48_1","volume-title":"Kwok","author":"Zheng Shuai","year":"2016","unstructured":"Shuai Zheng and James T . Kwok . 2016 . Fast-and-Light Stochastic ADMM. In IJCAI. 2407--2613. Shuai Zheng and James T. Kwok. 2016. Fast-and-Light Stochastic ADMM. In IJCAI. 2407--2613."},{"key":"e_1_3_2_2_49_1","volume-title":"Chris Junchi Li, and Qingyun Sun","author":"Zhou Xiang","year":"2020","unstructured":"Xiang Zhou , Huizhuo Yuan , Chris Junchi Li, and Qingyun Sun . 2020 . Stochastic Modified Equations for Continuous Limit of Stochastic ADMM. arXiv preprint arXiv:2003.03532 (2020). Xiang Zhou, Huizhuo Yuan, Chris Junchi Li, and Qingyun Sun. 2020. Stochastic Modified Equations for Continuous Limit of Stochastic ADMM. arXiv preprint arXiv:2003.03532 (2020)."}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Lisboa Portugal","acronym":"MM '22"},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547887","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3547887","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:30Z","timestamp":1750186830000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547887"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":49,"alternative-id":["10.1145\/3503161.3547887","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3547887","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}