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ORCIDZhouchen Lin
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- affiliation (PhD 2000): Peking University, Department of Machine Intelligence, Beijing, China
- affiliation (former): Microsoft Research Asia, Beijing, China
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2020 – today
- 2023
[j123]Mingqing Xiao
, Shuxin Zheng, Chang Liu, Zhouchen Lin, Tie-Yan Liu:
Invertible Rescaling Network and Its Extensions. Int. J. Comput. Vis. 131(1): 134-159 (2023)- [j122]Huan Li, Zhouchen Lin:
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity. J. Mach. Learn. Res. 24: 157:1-157:37 (2023) - [j121]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin:
SPIDE: A purely spike-based method for training feedback spiking neural networks. Neural Networks 161: 9-24 (2023) - [j120]Xingyu Xie, Qiuhao Wang, Zenan Ling, Xia Li, Guangcan Liu, Zhouchen Lin:
Optimization Induced Equilibrium Networks: An Explicit Optimization Perspective for Understanding Equilibrium Models. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 3604-3616 (2023) - [j119]Xiaoqin Zhang, Jingjing Zheng, Di Wang, Guiying Tang, Zhengyuan Zhou, Zhouchen Lin:
Structured Sparsity Optimization With Non-Convex Surrogates of $\ell _{2,0}$ℓ2,0-Norm: A Unified Algorithmic Framework. IEEE Trans. Pattern Anal. Mach. Intell. 45(5): 6386-6402 (2023) - [j118]Qi Chen, Yifei Wang, Zhengyang Geng, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Equilibrium Image Denoising With Implicit Differentiation. IEEE Trans. Image Process. 32: 1868-1881 (2023) - [c144]Zenan Ling, Xingyu Xie, Qiuhao Wang, Zongpeng Zhang, Zhouchen Lin:
Global Convergence of Over-parameterized Deep Equilibrium Models. AISTATS 2023: 767-787 - [c143]Pengyun Yue, Cong Fang, Zhouchen Lin:
On the Lower Bound of Minimizing Polyak-Łojasiewicz functions. COLT 2023: 2948-2968 - [c142]Pengyun Yue, Long Yang, Cong Fang, Zhouchen Lin:
Zeroth-order Optimization with Weak Dimension Dependency. COLT 2023: 4429-4472 - [c141]Yifei Wang, Qi Zhang, Tianqi Du, Jiansheng Yang, Zhouchen Lin, Yisen Wang:
A Message Passing Perspective on Learning Dynamics of Contrastive Learning. ICLR 2023 - [c140]Lingshen He, Yuxuan Chen, Zhengyang Shen, Yibo Yang, Zhouchen Lin:
Neural ePDOs: Spatially Adaptive Equivariant Partial Differential Operator Based Networks. ICLR 2023 - [c139]Mingjie Li, Yifei Wang, Yisen Wang, Zhouchen Lin:
Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States. ICLR 2023 - [c138]Yibo Yang, Haobo Yuan, Xiangtai Li, Zhouchen Lin, Philip H. S. Torr, Dacheng Tao:
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning. ICLR 2023 - [c137]Yi Wu, Yanyang Xu, Wenhao Zhu, Guojie Song, Zhouchen Lin, Liang Wang, Shaoguo Liu:
KDLGT: A Linear Graph Transformer Framework via Kernel Decomposition Approach. IJCAI 2023: 2370-2378 - [c136]Zongpeng Zhang, Zenan Ling, Tong Lin, Zhouchen Lin:
Gradient Descent Optimizes Normalization-Free ResNets. IJCNN 2023: 1-8 - [i120]Xiangtai Li, Shilin Xu, Yibo Yang, Haobo Yuan, Guangliang Cheng, Yunhai Tong, Zhouchen Lin, Dacheng Tao:
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation. CoRR abs/2301.00954 (2023) - [i119]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin:
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks. CoRR abs/2302.00232 (2023) - [i118]Yibo Yang, Haobo Yuan, Xiangtai Li, Zhouchen Lin, Philip H. S. Torr, Dacheng Tao:
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning. CoRR abs/2302.03004 (2023) - [i117]Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo:
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks. CoRR abs/2302.14311 (2023) - [i116]Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang:
Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium. CoRR abs/2303.00970 (2023) - [i115]Yifei Wang, Qi Zhang, Tianqi Du, Jiansheng Yang, Zhouchen Lin, Yisen Wang:
A Message Passing Perspective on Learning Dynamics of Contrastive Learning. CoRR abs/2303.04435 (2023) - [i114]Haixin Wang, Jianlong Chang, Xiao Luo, Jinan Sun, Zhouchen Lin, Qi Tian:
LION: Implicit Vision Prompt Tuning. CoRR abs/2303.09992 (2023) - [i113]Bruce X. B. Yu, Jianlong Chang, Haixin Wang, Lingbo Liu, Shijie Wang, Zhiyu Wang, Junfan Lin, Lingxi Xie, Haojie Li, Zhouchen Lin, Qi Tian, Chang Wen Chen:
Visual Tuning. CoRR abs/2305.06061 (2023) - [i112]Long Yang, Zhixiong Huang, Fenghao Lei, Yucun Zhong, Yiming Yang, Cong Fang, Shiting Wen, Binbin Zhou, Zhouchen Lin:
Policy Representation via Diffusion Probability Model for Reinforcement Learning. CoRR abs/2305.13122 (2023) - [i111]Yi Hu, Haotong Yang, Zhouchen Lin, Muhan Zhang:
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models. CoRR abs/2305.18507 (2023) - [i110]Jianghui Wang, Cheng Yang, Xingyu Xie, Cong Fang, Zhouchen Lin:
Task-Robust Pre-Training for Worst-Case Downstream Adaptation. CoRR abs/2306.12070 (2023) - [i109]Yibo Yang, Haobo Yuan, Xiangtai Li, Jianlong Wu, Lefei Zhang, Zhouchen Lin, Philip H. S. Torr, Dacheng Tao, Bernard Ghanem:
Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants. CoRR abs/2308.01746 (2023) - [i108]Yuxuan Du, Yibo Yang, Tongliang Liu, Zhouchen Lin, Bernard Ghanem, Dacheng Tao:
ShadowNet for Data-Centric Quantum System Learning. CoRR abs/2308.11290 (2023) - [i107]Pengyun Yue, Hanzhen Zhao, Cong Fang, Di He, Liwei Wang, Zhouchen Lin, Song-chun Zhu:
CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity. CoRR abs/2309.13307 (2023) - 2022
- [b2]Zhouchen Lin, Huan Li, Cong Fang:
Alternating Direction Method of Multipliers for Machine Learning. Springer 2022, ISBN 978-981-16-9839-2, pp. 1-263 - [j117]Hanyuan Hang, Yuchao Cai, Hanfang Yang, Zhouchen Lin:
Under-bagging Nearest Neighbors for Imbalanced Classification. J. Mach. Learn. Res. 23: 118:1-118:63 (2022) - [j116]Huan Li, Zhouchen Lin, Yongchun Fang:
Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization. J. Mach. Learn. Res. 23: 222:1-222:41 (2022) - [j115]Qingyan Meng, Shen Yan, Mingqing Xiao, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo:
Training much deeper spiking neural networks with a small number of time-steps. Neural Networks 153: 254-268 (2022) - [j114]Jia Li, Mingqing Xiao, Cong Fang, Yue Dai, Chao Xu, Zhouchen Lin:
Training Neural Networks by Lifted Proximal Operator Machines. IEEE Trans. Pattern Anal. Mach. Intell. 44(6): 3334-3348 (2022) - [j113]Shiping Wang, Zhaoliang Chen, Shide Du, Zhouchen Lin:
Learning Deep Sparse Regularizers With Applications to Multi-View Clustering and Semi-Supervised Classification. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 5042-5055 (2022) - [j112]Pan Zhou, Xiao-Tong Yuan, Zhouchen Lin, Steven C. H. Hoi:
A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method for Efficient Optimization and Generalization. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 5933-5946 (2022) - [j111]Risheng Liu, Jiaxin Gao, Jin Zhang, Deyu Meng, Zhouchen Lin:
Investigating Bi-Level Optimization for Learning and Vision From a Unified Perspective: A Survey and Beyond. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 10045-10067 (2022) - [c135]Qi Chen, Yifei Wang, Yisen Wang, Jianlong Chang, Qi Tian, Jiansheng Yang, Zhouchen Lin:
Efficient and Scalable Implicit Graph Neural Networks with Virtual Equilibrium. IEEE Big Data 2022: 864-873 - [c134]Nan Ke, Tong Lin, Zhouchen Lin, Xiao-Hua Zhou, Taoyun Ji:
Convolutional Transformer Networks for Epileptic Seizure Detection. CIKM 2022: 4109-4113 - [c133]Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo:
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation. CVPR 2022: 12434-12443 - [c132]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training. ICLR 2022 - [c131]Yifei Wang, Qi Zhang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap. ICLR 2022 - [c130]Mingjie Li, Yisen Wang, Xingyu Xie, Zhouchen Lin:
Optimization inspired Multi-Branch Equilibrium Models. ICLR 2022 - [c129]Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Optimization-Induced Graph Implicit Nonlinear Diffusion. ICML 2022: 3648-3661 - [c128]Mingjie Li, Xiaojun Guo, Yifei Wang, Yisen Wang, Zhouchen Lin:
G2CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters. ICML 2022: 12782-12796 - [c127]Huan Li, Zhouchen Lin:
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the O(ε-7/4) Complexity. ICML 2022: 12901-12916 - [c126]Mingjie Li, Yisen Wang, Zhouchen Lin:
CerDEQ: Certifiable Deep Equilibrium Model. ICML 2022: 12998-13013 - [c125]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 - [c124]Zhengyang Shen, Tao Hong, Qi She, Jinwen Ma, Zhouchen Lin:
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs. ICML 2022: 19827-19846 - [c123]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin:
Online Training Through Time for Spiking Neural Networks. NeurIPS 2022 - [c122]Yibo Yang, Shixiang Chen, Xiangtai Li, Liang Xie, Zhouchen Lin, Dacheng Tao:
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network? NeurIPS 2022 - [c121]Haotong Yang, Zhouchen Lin, Muhan Zhang:
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption. NeurIPS 2022 - [c120]Yibo Yang, Hong Wang, Haobo Yuan, Zhouchen Lin:
Towards Theoretically Inspired Neural Initialization Optimization. NeurIPS 2022 - [i106]Huan Li, Zhouchen Lin:
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the O(ε-7/4) Complexity. CoRR abs/2201.11411 (2022) - [i105]Yibo Yang, Liang Xie, Shixiang Chen, Xiangtai Li, Zhouchen Lin, Dacheng Tao:
Do We Really Need a Learnable Classifier at the End of Deep Neural Network? CoRR abs/2203.09081 (2022) - [i104]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training. CoRR abs/2203.13455 (2022) - [i103]Yifei Wang, Qi Zhang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap. CoRR abs/2203.13457 (2022) - [i102]Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo:
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation. CoRR abs/2205.00459 (2022) - [i101]Mingjie Li, Hao Kong, Zhouchen Lin:
SymNMF-Net for The Symmetric NMF Problem. CoRR abs/2205.13214 (2022) - [i100]Zenan Ling, Xingyu Xie, Qiuhao Wang, Zongpeng Zhang, Zhouchen Lin:
Global Convergence of Over-parameterized Deep Equilibrium Models. CoRR abs/2205.13814 (2022) - [i99]Yang Hu, Xiyuan Wang, Zhouchen Lin, Pan Li, Muhan Zhang:
Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction. CoRR abs/2206.09567 (2022) - [i98]Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Optimization-Induced Graph Implicit Nonlinear Diffusion. CoRR abs/2206.14418 (2022) - [i97]Zhengyang Shen, Tao Hong, Qi She, Jinwen Ma, Zhouchen Lin:
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs. CoRR abs/2208.03720 (2022) - [i96]Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan:
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models. CoRR abs/2208.06677 (2022) - [i95]Haotong Yang, Zhouchen Lin, Muhan Zhang:
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption. CoRR abs/2209.08858 (2022) - [i94]Mingqing Xiao, Shuxin Zheng, Chang Liu, Zhouchen Lin, Tie-Yan Liu:
Invertible Rescaling Network and Its Extensions. CoRR abs/2210.04188 (2022) - [i93]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin:
Online Training Through Time for Spiking Neural Networks. CoRR abs/2210.04195 (2022) - [i92]Yibo Yang, Hong Wang, Haobo Yuan, Zhouchen Lin:
Towards Theoretically Inspired Neural Initialization Optimization. CoRR abs/2210.05956 (2022) - 2021
- [j110]Tiancheng Shen, Yibo Yang, Zhouchen Lin, Mingbin Zhang:
Recurrent learning with clique structures for prostate sparse-view CT artifacts reduction. IET Image Process. 15(3): 648-655 (2021) - [j109]Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu, Hongwei Wen:
Histogram Transform Ensembles for Large-scale Regression. J. Mach. Learn. Res. 22: 95:1-95:87 (2021) - [j108]Hao Kong, Canyi Lu, Zhouchen Lin:
Tensor Q-rank: new data dependent definition of tensor rank. Mach. Learn. 110(7): 1867-1900 (2021) - [j107]Pan Zhou, Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan:
Tensor Low-Rank Representation for Data Recovery and Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 43(5): 1718-1732 (2021) - [j106]Xinbang Zhang, Jianlong Chang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Zhouchen Lin, Chunhong Pan:
DATA: Differentiable ArchiTecture Approximation With Distribution Guided Sampling. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 2905-2920 (2021) - [j105]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) - [j104]Jianlong Wu, Xingxu Xie, Liqiang Nie, Zhouchen Lin, Hongbin Zha:
Reconstruction regularized low-rank subspace learning for cross-modal retrieval. Pattern Recognit. 113: 107813 (2021) - [j103]Xiangtai Li, Xia Li, Ansheng You, Li Zhang, Guangliang Cheng, Kuiyuan Yang, Yunhai Tong, Zhouchen Lin:
Towards Efficient Scene Understanding via Squeeze Reasoning. IEEE Trans. Image Process. 30: 7050-7063 (2021) - [c119]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 - [c118]Zhengyang Shen, Tiancheng Shen, Zhouchen Lin, Jinwen Ma:
PDO-eS2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs. AAAI 2021: 9585-9593 - [c117]Xiangtai Li, Hao He, Xia Li, Duo Li, Guangliang Cheng, Jianping Shi, Lubin Weng, Yunhai Tong, Zhouchen Lin:
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation. CVPR 2021: 4217-4226 - [c116]Yibo Yang, Shan You, Hongyang Li, Fei Wang, Chen Qian, Zhouchen Lin:
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search. CVPR 2021: 6667-6676 - [c115]Huasong Zhong, Jianlong Wu, Chong Chen, Jianqiang Huang, Minghua Deng, Liqiang Nie, Zhouchen Lin, Xian-Sheng Hua:
Graph Contrastive Clustering. ICCV 2021: 9204-9213 - [c114]Zhengyang Geng, Meng-Hao Guo, Hongxu Chen, Xia Li, Ke Wei, Zhouchen Lin:
Is Attention Better Than Matrix Decomposition? ICLR 2021 - [c113]Ke Sun, Zhanxing Zhu, Zhouchen Lin:
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models. ICLR 2021 - [c112]Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin:
GBHT: Gradient Boosting Histogram Transform for Density Estimation. ICML 2021: 2233-2243 - [c111]Ruili Feng, Zhouchen Lin, Jiapeng Zhu, Deli Zhao, Jingren Zhou, Zheng-Jun Zha:
Uncertainty Principles of Encoding GANs. ICML 2021: 3240-3251 - [c110]Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin:
Leveraged Weighted Loss for Partial Label Learning. ICML 2021: 11091-11100 - [c109]Nan Ke, Tong Lin, Zhouchen Lin:
Channel-Weighted Squeeze-and-Excitation Networks For Epileptic Seizure Detection. ICTAI 2021: 666-673 - [c108]Lingshen He, Yuxuan Chen, Zhengyang Shen, Yiming Dong, Yisen Wang, Zhouchen Lin:
Efficient Equivariant Network. NeurIPS 2021: 5290-5302 - [c107]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Dissecting the Diffusion Process in Linear Graph Convolutional Networks. NeurIPS 2021: 5758-5769 - [c106]Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Residual Relaxation for Multi-view Representation Learning. NeurIPS 2021: 12104-12115 - [c105]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin:
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State. NeurIPS 2021: 14516-14528 - [c104]Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, Zhouchen Lin:
On Training Implicit Models. NeurIPS 2021: 24247-24260 - [c103]Lingshen He, Yiming Dong, Yisen Wang, Dacheng Tao, Zhouchen Lin:
Gauge Equivariant Transformer. NeurIPS 2021: 27331-27343 - [c102]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Reparameterized Sampling for Generative Adversarial Networks. ECML/PKDD (3) 2021: 494-509 - [c101]Yong Chen, Yuqing Hou, Shu Leng, Qing Zhang, Zhouchen Lin, Dell Zhang:
Long-Tail Hashing. SIGIR 2021: 1328-1338 - [i91]Yibo Yang, Shan You, Hongyang Li, Fei Wang, Chen Qian, Zhouchen Lin:
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search. CoRR abs/2101.11342 (2021) - [i90]Risheng Liu, Jiaxin Gao, Jin Zhang, Deyu Meng, Zhouchen Lin:
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond. CoRR abs/2101.11517 (2021) - [i89]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Dissecting the Diffusion Process in Linear Graph Convolutional Networks. CoRR abs/2102.10739 (2021) - [i88]Xiangtai Li, Hao He, Xia Li, Duo Li, Guangliang Cheng, Jianping Shi, Lubin Weng, Yunhai Tong, Zhouchen Lin:
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation. CoRR abs/2103.06564 (2021) - [i87]Huasong Zhong, Jianlong Wu, Chong Chen, Jianqiang Huang, Minghua Deng, Liqiang Nie, Zhouchen Lin, Xian-Sheng Hua:
Graph Contrastive Clustering. CoRR abs/2104.01429 (2021) - [i86]Huan Li, Zhouchen Lin:
Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization. CoRR abs/2104.02596 (2021) - [i85]Zhengyang Shen, Tiancheng Shen, Zhouchen Lin, Jinwen Ma:
PDO-eS2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs. CoRR abs/2104.03584 (2021) - [i84]Xingyu Xie, Qiuhao Wang, Zenan Ling, Xia Li, Yisen Wang, Guangcan Liu, Zhouchen Lin:
Optimization Induced Equilibrium Networks. CoRR abs/2105.13228 (2021) - [i83]Hanyuan Hang, Tao Huang, Yuchao Cai, Hanfang Yang, Zhouchen Lin:
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression. CoRR abs/2106.01986 (2021) - [i82]Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin:
Leveraged Weighted Loss for Partial Label Learning. CoRR abs/2106.05731 (2021) - [i81]Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin:
GBHT: Gradient Boosting Histogram Transform for Density Estimation. CoRR abs/2106.05738 (2021) - [i80]Qigong Sun, Xiufang Li, Fanhua Shang, Hongying Liu, Kang Yang, Licheng Jiao, Zhouchen Lin:
Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration. CoRR abs/2106.09886 (2021) - [i79]Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Reparameterized Sampling for Generative Adversarial Networks. CoRR abs/2107.00352 (2021) - [i78]Hanyuan Hang, Yuchao Cai, Hanfang Yang, Zhouchen Lin:
Under-bagging Nearest Neighbors for Imbalanced Classification. CoRR abs/2109.00531 (2021) - [i77]Zhengyang Geng, Meng-Hao Guo, Hongxu Chen, Xia Li, Ke Wei, Zhouchen Lin:
Is Attention Better Than Matrix Decomposition? CoRR abs/2109.04553 (2021) - [i76]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin:
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State. CoRR abs/2109.14247 (2021) - [i75]Yifei Wang, Zhengyang Geng, Feng Jiang, Chuming Li, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Residual Relaxation for Multi-view Representation Learning. CoRR abs/2110.15348 (2021) - [i74]Ke Sun, Mingjie Li, Zhouchen Lin:
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness. CoRR abs/2111.01996 (2021) - [i73]Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, Zhouchen Lin:
On Training Implicit Models. CoRR abs/2111.05177 (2021) - 2020
- [b1]Zhouchen Lin, Huan Li, Cong Fang:
Accelerated Optimization for Machine Learning - First-Order Algorithms. Springer 2020, ISBN 978-981-15-2909-2, pp. 1-275 - [j102]Huan Li, Zhouchen Lin:
On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent. J. Mach. Learn. Res. 21: 33:1-33:45 (2020) - [j101]Huan Li, Zhouchen Lin:
Provable accelerated gradient method for nonconvex low rank optimization. Mach. Learn. 109(1): 103-134 (2020) - [j100]Canyi Lu, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, Shuicheng Yan:
Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm. IEEE Trans. Pattern Anal. Mach. Intell. 42(4): 925-938 (2020) - [j99]Risheng Liu, Shichao Cheng, Yi He, Xin Fan, Zhouchen Lin, Zhongxuan Luo:
On the Convergence of Learning-Based Iterative Methods for Nonconvex Inverse Problems. IEEE Trans. Pattern Anal. Mach. Intell. 42(12): 3027-3039 (2020) - [j98]Huan Li, Cong Fang, Zhouchen Lin:
Accelerated First-Order Optimization Algorithms for Machine Learning. Proc. IEEE 108(11): 2067-2082 (2020) - [j97]