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ORCIDYuanyuan Liu 0001
Person information
- affiliation: Xidian University, School of Artificial Intelligence, MOE Key Laboratory of Intelligent Perception and Image Understanding, Xi'an, China
- affiliation (2013 - 2014): Chinese University of Hong Kong, Department of Computer Science and Engineering / Department of Systems Engineering and Engineering Management, Honk Kong
- affiliation (PhD 2013): Xidian University, Key Laboratory of Intelligent Perception and Image Understanding, Xi'an, China
- not to be confused with: Yuanyuan Liu 0002
Other persons with the same name
- Yuanyuan Liu — disambiguation page
- Yuanyuan Liu 0002
![[0000-0002-1839-4728]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-1839-4728")
— Chinese University of Hong Kong, Department of Electronic Engineering, Hong Kong - Yuanyuan Liu 0003 — Linyi University, Department of Mathematics, Shandong, China
- Yuanyuan Liu 0004 — China University of Geosciences, Faculty of Information Engineering, Wuhan, China
- Yuanyuan Liu 0005 — Chang'an University, School of Geology Engineering and Geomatics, Xian, China
- Yuanyuan Liu 0006 — Chongqing University of Posts and Telecommunications, College of Computer Science and Technology, China
- Yuanyuan Liu 0007 — Jilin University, College of Computer Science and Technology, China
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2020 – today
- 2024
[j26]Hongying Liu, Zekun Li, Fanhua Shang, Yuanyuan Liu, Liang Wang, Wei Feng, Radu Timofte:
Arbitrary-scale Super-resolution via Deep Learning: A Comprehensive Survey. Inf. Fusion 102: 102015 (2024)- [c31]Zekun Li, Hongying Liu, Fanhua Shang, Yuanyuan Liu, Liang Wan, Wei Feng:
SAVSR: Arbitrary-Scale Video Super-Resolution via a Learned Scale-Adaptive Network. AAAI 2024: 3288-3296 - 2023
- [j25]Fanjie Shang, Hongying Liu, Wanhao Ma, Yuanyuan Liu, Licheng Jiao, Fanhua Shang, Lijun Wang, Zhenyu Zhou:
Lightweight Super-Resolution with Self-Calibrated Convolution for Panoramic Videos. Sensors 23(1): 392 (2023) - [c30]Linlin Yang, Hongying Liu, Fanhua Shang, Yuanyuan Liu:
Adaptive Non-Local Generative Adversarial Networks for Low-Dose CT Image Denoising. ICASSP 2023: 1-5 - [c29]Zhijin Ge, Fanhua Shang, Hongying Liu, Yuanyuan Liu, Liang Wan, Wei Feng, Xiaosen Wang:
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer. ACM Multimedia 2023: 4440-4449 - [c28]Yuanyuan Liu, Fanhua Shang, Weixin An, Junhao Liu, Hongying Liu, Zhouchen Lin:
A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization. NeurIPS 2023 - [c27]Zhijin Ge, Xiaosen Wang, Hongying Liu, Fanhua Shang, Yuanyuan Liu:
Boosting Adversarial Transferability by Achieving Flat Local Maxima. NeurIPS 2023 - [i21]Zhijin Ge, Fanhua Shang, Hongying Liu, Yuanyuan Liu, Xiaosen Wang:
Boosting Adversarial Transferability by Achieving Flat Local Maxima. CoRR abs/2306.05225 (2023) - [i20]Zhijin Ge, Fanhua Shang, Hongying Liu, Yuanyuan Liu, Liang Wan, Wei Feng, Xiaosen Wang:
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer. CoRR abs/2308.10601 (2023) - 2022
- [j24]Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan Liu, Linlin Yang, Radu Timofte:
Video super-resolution based on deep learning: a comprehensive survey. Artif. Intell. Rev. 55(8): 5981-6035 (2022) - [j23]Yuanyuan Liu, Jiacheng Geng, Fanhua Shang, Weixin An, Hongying Liu, Qi Zhu:
Loopless Variance Reduced Stochastic ADMM for Equality Constrained Problems in IoT Applications. IEEE Internet Things J. 9(3): 2293-2303 (2022) - [j22]Yuanyuan Liu, Jiacheng Geng, Fanhua Shang, Weixin An, Hongying Liu, Qi Zhu, Wei Feng:
Laplacian Smoothing Stochastic ADMMs With Differential Privacy Guarantees. IEEE Trans. Inf. Forensics Secur. 17: 1814-1826 (2022) - [j21]Fanhua Shang, Hua Huang, Jun Fan, Yuanyuan Liu, Hongying Liu, Jianhui Liu:
Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning. IEEE Trans. Knowl. Data Eng. 34(12): 5636-5648 (2022) - [j20]Fanhua Shang, Bingkun Wei, Hongying Liu, Yuanyuan Liu, Pan Zhou, Maoguo Gong:
Efficient Gradient Support Pursuit With Less Hard Thresholding for Cardinality-Constrained Learning. IEEE Trans. Neural Networks Learn. Syst. 33(12): 7806-7817 (2022) - [c26]Lin Kong, Wei Sun, Fanhua Shang, Yuanyuan Liu, Hongying Liu:
HNO: High-Order Numerical Architecture for ODE-Inspired Deep Unfolding Networks. AAAI 2022: 7220-7228 - [c25]Linlin Yang, Hongying Liu, Yiming Li, Wenhao Zhou, Yuanyuan Liu, Xiaobiao Di, Lei Wang, Chuanwen Li:
Multi Recursive Residual Dense Attention GAN for Perceptual Image Super Resolution. IFIP TC12 ICIS 2022: 363-377 - [c24]Yuanyuan Liu, Fanhua Shang, Weixin An, Hongying Liu, Zhouchen Lin:
Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots. ICML 2022: 14008-14035 - [c23]Dong Wang, Tao Xu, Huatian Zhang, Fanhua Shang, Hongying Liu, Yuanyuan Liu, Shengmei Shen:
PWPROP: A Progressive Weighted Adaptive Method for Training Deep Neural Networks. ICTAI 2022: 508-515 - [c22]Weixin An, Yingjie Yue, Yuanyuan Liu, Fanhua Shang, Hongying Liu:
A Numerical DEs Perspective on Unfolded Linearized ADMM Networks for Inverse Problems. ACM Multimedia 2022: 5065-5073 - [c21]Dong Wang, Yicheng Liu, Liangji Fang, Fanhua Shang, Yuanyuan Liu, Hongying Liu:
Balanced Gradient Penalty Improves Deep Long-Tailed Learning. ACM Multimedia 2022: 5093-5101 - 2021
- [j19]Chaolong Zhang, Yuanyuan Liu, Fanhua Shang, Yangyang Li, Hongying Liu:
A Novel Learned Primal-Dual Network for Image Compressive Sensing. IEEE Access 9: 26041-26050 (2021) - [j18]Fanhua Shang, Zhihui Zhang, Yuanyuan Liu, Hongying Liu, Jing Xu:
Efficient Asynchronous Semi-Stochastic Block Coordinate Descent Methods for Large-Scale SVD. IEEE Access 9: 126159-126171 (2021) - [j17]Yuanyuan Liu, Fanhua Shang, Hongying Liu, Lin Kong, Licheng Jiao, Zhouchen Lin:
Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(12): 4242-4255 (2021) - [j16]Hongying Liu, Derong Xu, Tianwen Zhu, Fanhua Shang, Yuanyuan Liu, Jianhua Lu, Ri Yang:
Graph Convolutional Networks by Architecture Search for PolSAR Image Classification. Remote. Sens. 13(7): 1404 (2021) - [j15]Hongying Liu, Tianwen Zhu, Fanhua Shang, Yuanyuan Liu, Derui Lv, Shuyuan Yang:
Deep Fuzzy Graph Convolutional Networks for PolSAR Imagery Pixelwise Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 504-514 (2021) - [j14]Fanhua Shang, Tao Xu, Yuanyuan Liu, Hongying Liu, Longjie Shen, Maoguo Gong:
Differentially Private ADMM Algorithms for Machine Learning. IEEE Trans. Inf. Forensics Secur. 16: 4733-4745 (2021) - [c20]Hongying Liu, Peng Zhao, Zhubo Ruan, Fanhua Shang, Yuanyuan Liu:
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling. AAAI 2021: 2127-2135 - [c19]Yangyang Li, Lin Kong, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Zhouchen Lin:
Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding. AAAI 2021: 8501-8509 - [c18]Hua Huang, Fanhua Shang, Yuanyuan Liu, Hongying Liu:
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning. IJCAI 2021: 2556-2562 - [c17]Hongying Liu, Ruyi Luo, Fanhua Shang, Mantang Niu, Yuanyuan Liu:
Progressive Semantic Matching for Video-Text Retrieval. ACM Multimedia 2021: 5083-5091 - [c16]Fanhua Shang, Zhihui Zhang, Tao Xu, Yuanyuan Liu, Hongying Liu:
Principal component analysis in the stochastic differential privacy model. UAI 2021: 1110-1119 - [i19]Hongying Liu, Peng Zhao, Zhubo Ruan, Fanhua Shang, Yuanyuan Liu:
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling. CoRR abs/2103.11744 (2021) - [i18]Lin Kong, Wei Sun, Fanhua Shang, Yuanyuan Liu, Hongying Liu:
Learned Interpretable Residual Extragradient ISTA for Sparse Coding. CoRR abs/2106.11970 (2021) - [i17]Hua Huang, Fanhua Shang, Yuanyuan Liu, Hongying Liu:
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning. CoRR abs/2106.12300 (2021) - 2020
- [j13]Fanhua Shang, Bingkun Wei, Yuanyuan Liu, Hongying Liu, Shuang Wang, Licheng Jiao:
Stochastic Recursive Gradient Support Pursuit and Its Sparse Representation Applications. Sensors 20(17): 4902 (2020) - [i16]Hongying Liu, Zhubo Ruan, Peng Zhao, Fanhua Shang, Linlin Yang, Yuanyuan Liu:
Video Super Resolution Based on Deep Learning: A comprehensive survey. CoRR abs/2007.12928 (2020) - [i15]Hongying Liu, Zhubo Ruan, Chaowei Fang, Peng Zhao, Fanhua Shang, Yuanyuan Liu, Lijun Wang:
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution. CoRR abs/2008.10320 (2020) - [i14]Hongying Liu, Zhenyu Zhou, Fanhua Shang, Xiaoyu Qi, Yuanyuan Liu, Licheng Jiao:
Boosting Gradient for White-Box Adversarial Attacks. CoRR abs/2010.10712 (2020) - [i13]Tao Xu, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Longjie Shen, Maoguo Gong:
Differentially Private ADMM Algorithms for Machine Learning. CoRR abs/2011.00164 (2020)
2010 – 2019
- 2019
- [c15]Lin Kong, Xiaying Bai, Yang Hu, Fanhua Shang, Yuanyuan Liu, Hongying Liu:
A Stochastic Variance Reduced Extragradient Method for Sparse Machine Learning Problems. ICDM Workshops 2019: 155-164 - [c14]Yuanyuan Liu, Fanhua Shang, Licheng Jiao:
Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor. IJCAI 2019: 3045-3051 - [i12]Fanhua Shang, Bingkun Wei, Hongying Liu, Yuanyuan Liu, Jiacheng Zhuo:
Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Non-convex Optimization. CoRR abs/1912.00858 (2019) - 2018
- [j12]Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, Zhouchen Lin:
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications. IEEE Trans. Pattern Anal. Mach. Intell. 40(9): 2066-2080 (2018) - [j11]Fanhua Shang, Yuanyuan Liu, James Cheng, Da Yan:
Fuzzy Double Trace Norm Minimization for Recommendation Systems. IEEE Trans. Fuzzy Syst. 26(4): 2039-2049 (2018) - [c13]Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida:
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. AISTATS 2018: 1027-1036 - [i11]Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida:
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. CoRR abs/1802.09933 (2018) - [i10]Fanhua Shang, Yuanyuan Liu, James Cheng:
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. CoRR abs/1803.00420 (2018) - [i9]Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, Zhouchen Lin:
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications. CoRR abs/1810.05186 (2018) - 2017
- [c12]Yuanyuan Liu, Fanhua Shang, James Cheng:
Accelerated Variance Reduced Stochastic ADMM. AAAI 2017: 2287-2293 - [c11]Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, Licheng Jiao:
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds. NIPS 2017: 4868-4877 - [i8]Fanhua Shang, Yuanyuan Liu, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida:
Variance Reduced Stochastic Gradient Descent with Sufficient Decrease. CoRR abs/1703.06807 (2017) - [i7]Fanhua Shang, Yuanyuan Liu, James Cheng, Jiacheng Zhuo:
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning. CoRR abs/1703.07948 (2017) - [i6]Yuanyuan Liu, Fanhua Shang, James Cheng:
Accelerated Variance Reduced Stochastic ADMM. CoRR abs/1707.03190 (2017) - 2016
- [j10]Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng:
Generalized Higher Order Orthogonal Iteration for Tensor Learning and Decomposition. IEEE Trans. Neural Networks Learn. Syst. 27(12): 2551-2563 (2016) - [c10]Fanhua Shang, Yuanyuan Liu, James Cheng:
Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization. AAAI 2016: 2016-2022 - [c9]Fanhua Shang, Yuanyuan Liu, James Cheng:
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. AISTATS 2016: 620-629 - [i5]Fanhua Shang, Yuanyuan Liu, James Cheng:
Unified Scalable Equivalent Formulations for Schatten Quasi-Norms. CoRR abs/1606.00668 (2016) - [i4]Fanhua Shang, Yuanyuan Liu, James Cheng:
Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization. CoRR abs/1606.01245 (2016) - 2015
- [j9]Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, Hong Cheng:
Robust bilinear factorization with missing and grossly corrupted observations. Inf. Sci. 307: 53-72 (2015) - [j8]Yuanyuan Liu, Fanhua Shang, Licheng Jiao, James Cheng, Hong Cheng:
Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data. IEEE Trans. Cybern. 45(11): 2437-2448 (2015) - [i3]Fanhua Shang, Yuanyuan Liu, James Cheng, Hong Cheng:
Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning. CoRR abs/1512.08120 (2015) - 2014
- [c8]Fanhua Shang, Yuanyuan Liu, James Cheng:
Generalized Higher-Order Tensor Decomposition via Parallel ADMM. AAAI 2014: 1279-1285 - [c7]Fanhua Shang, Yuanyuan Liu, James Cheng, Hong Cheng:
Robust Principal Component Analysis with Missing Data. CIKM 2014: 1149-1158 - [c6]Fanhua Shang, Yuanyuan Liu, James Cheng, Hong Cheng:
Recovering Low-Rank and Sparse Matrices via Robust Bilateral Factorization. ICDM 2014: 965-970 - [c5]Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng:
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion. NIPS 2014: 1763-1771 - [c4]Yuanyuan Liu, Fanhua Shang, Hong Cheng, James Cheng, Hanghang Tong:
Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion. SDM 2014: 866-874 - [c3]Yuanyuan Liu, Fanhua Shang, Hong Cheng, James Cheng:
Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds. UAI 2014: 515-524 - [i2]Fanhua Shang, Yuanyuan Liu, James Cheng:
Generalized Higher-Order Tensor Decomposition via Parallel ADMM. CoRR abs/1407.1399 (2014) - [i1]Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, Hong Cheng:
Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations. CoRR abs/1409.1062 (2014) - 2013
- [j7]Yuanyuan Liu, Licheng Jiao, Fanhua Shang, Fei Yin, Fang Liu:
An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion. Neural Networks 48: 8-18 (2013) - [j6]Yuanyuan Liu, Licheng Jiao, Fanhua Shang:
A fast tri-factorization method for low-rank matrix recovery and completion. Pattern Recognit. 46(1): 163-173 (2013) - [j5]Yuanyuan Liu, Licheng Jiao, Fanhua Shang:
An efficient matrix factorization based low-rank representation for subspace clustering. Pattern Recognit. 46(1): 284-292 (2013) - [j4]Fanhua Shang, Licheng Jiao, Yuanyuan Liu, Hanghang Tong:
Semi-supervised learning with nuclear norm regularization. Pattern Recognit. 46(8): 2323-2336 (2013) - [j3]Yuanyuan Liu, Fanhua Shang:
An Efficient Matrix Factorization Method for Tensor Completion. IEEE Signal Process. Lett. 20(4): 307-310 (2013) - 2012
- [j2]Fanhua Shang, Licheng Jiao, Yuanyuan Liu:
Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification. Neural Process. Lett. 36(2): 101-115 (2012) - [j1]Licheng Jiao, Fanhua Shang, Fei Wang, Yuanyuan Liu:
Fast semi-supervised clustering with enhanced spectral embedding. Pattern Recognit. 45(12): 4358-4369 (2012) - [c2]Fanhua Shang, Licheng Jiao, Yuanyuan Liu, Fei Wang:
Learning spectral embedding via iterative eigenvalue thresholding. CIKM 2012: 1507-1511 - 2011
- [c1]Fanhua Shang, Yuanyuan Liu, Fei Wang:
Learning Spectral Embedding for Semi-supervised Clustering. ICDM 2011: 597-606
Coauthor Index
[j26] [c31] [j25] [c30] [c29] [c28] [c27] [i21] [i20] [j24] [j23] [j22] [j21] [j20] [c26] [c24] [c23] [c22] [c21] [j19] [j18] [j17] [j16] [j15] [j14] [c20] [c19] [c18] [c17] [c16] [i19] [i18] [i17] [j13] [i16] [i15] [i14] [i13] [c15] [c14] [i12] [j12] [j11] [c13] [i11] [i10] [i9] [c12] [c11] [i8] [i7] [i6] [j10] [c10] [c9] [i5] [i4] [j9] [j8] [i3] [c8] [c7] [c6] [c5] [c4] [c3] [i2] [i1] [j7] [j6] [j5] [j4] [j3] [j2] [j1] [c2] [c1]
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