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2020 – today
- 2024
[j16]Li Liu
, Timothy M. Hospedales, Yann LeCun, Mingsheng Long, Jiebo Luo, Wanli Ouyang, Matti Pietikäinen, Tinne Tuytelaars:
Editorial: Learning With Fewer Labels in Computer Vision. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1319-1326 (2024)- [j15]Ying Jin, Zhangjie Cao, Ximei Wang, Jianmin Wang, Mingsheng Long:
One Fits Many: Class Confusion Loss for Versatile Domain Adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 46(11): 7251-7266 (2024) - [c112]Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long:
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting. ICLR 2024 - [c111]Kaichao You, Guo Qin, Anchang Bao, Meng Cao, Ping Huang, Jiulong Shan, Mingsheng Long:
Efficient ConvBN Blocks for Transfer Learning and Beyond. ICLR 2024 - [c110]Kaiyuan Chen, Xingzhuo Guo, Yu Zhang, Jianmin Wang, Mingsheng Long:
CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding. ICML 2024 - [c109]Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Yunzhong Qiu, Li Zhang, Jianmin Wang, Mingsheng Long:
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling. ICML 2024 - [c108]Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, Mingsheng Long:
On the Embedding Collapse when Scaling up Recommendation Models. ICML 2024 - [c107]Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long:
Timer: Generative Pre-trained Transformers Are Large Time Series Models. ICML 2024 - [c106]Haoyu Ma, Jialong Wu, Ningya Feng, Chenjun Xiao, Dong Li, Jianye Hao, Jianmin Wang, Mingsheng Long:
HarmonyDream: Task Harmonization Inside World Models. ICML 2024 - [c105]Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, Mingsheng Long:
Transolver: A Fast Transformer Solver for PDEs on General Geometries. ICML 2024 - [c104]Lanxiang Xing, Haixu Wu, Yuezhou Ma, Jianmin Wang, Mingsheng Long:
HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction. ICML 2024 - [c103]Zhiyu Yao, Jian Wang, Haixu Wu, Jingdong Wang, Mingsheng Long:
Mobile Attention: Mobile-Friendly Linear-Attention for Vision Transformers. ICML 2024 - [c102]Zhongyi Pei, Zhiyao Cen, Yipeng Huang, Chen Wang, Lin Liu, Philip S. Yu, Mingsheng Long, Jianmin Wang:
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization. KDD 2024: 2340-2351 - [c101]Zhiyu Yao, Xinyang Chen, Sinan Wang, Qinyan Dai, Yumeng Li, Tanchao Zhu, Mingsheng Long:
Recommender Transformers with Behavior Pathways. WWW 2024: 3643-3654 - [i87]Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, Mingsheng Long:
Transolver: A Fast Transformer Solver for PDEs on General Geometries. CoRR abs/2402.02366 (2024) - [i86]Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long:
Timer: Transformers for Time Series Analysis at Scale. CoRR abs/2402.02368 (2024) - [i85]Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long:
AutoTimes: Autoregressive Time Series Forecasters via Large Language Models. CoRR abs/2402.02370 (2024) - [i84]Qilong Ma, Haixu Wu, Lanxiang Xing, Jianmin Wang, Mingsheng Long:
EuLagNet: Eulerian Fluid Prediction with Lagrangian Dynamics. CoRR abs/2402.02425 (2024) - [i83]Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Yunzhong Qiu, Li Zhang, Jianmin Wang, Mingsheng Long:
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling. CoRR abs/2402.02475 (2024) - [i82]Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Yong Liu, Yunzhong Qiu, Haoran Zhang, Jianmin Wang, Mingsheng Long:
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables. CoRR abs/2402.19072 (2024) - [i81]Kaichao You, Runsheng Bai, Meng Cao, Jianmin Wang, Ion Stoica, Mingsheng Long:
depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers. CoRR abs/2403.13839 (2024) - [i80]Jialong Wu, Chaoyi Deng, Jianmin Wang, Mingsheng Long:
Supercompiler Code Optimization with Zero-Shot Reinforcement Learning. CoRR abs/2404.16077 (2024) - [i79]Kaiyuan Chen, Xingzhuo Guo, Yu Zhang, Jianmin Wang, Mingsheng Long:
CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding. CoRR abs/2405.02384 (2024) - [i78]Haixu Wu, Huakun Luo, Yuezhou Ma, Jianmin Wang, Mingsheng Long:
RoPINN: Region Optimized Physics-Informed Neural Networks. CoRR abs/2405.14369 (2024) - [i77]Jialong Wu, Shaofeng Yin, Ningya Feng, Xu He, Dong Li, Jianye Hao, Mingsheng Long:
iVideoGPT: Interactive VideoGPTs are Scalable World Models. CoRR abs/2405.15223 (2024) - [i76]Hang Zhou, Yuezhou Ma, Haixu Wu, Haowen Wang, Mingsheng Long:
Unisolver: PDE-Conditional Transformers Are Universal PDE Solvers. CoRR abs/2405.17527 (2024) - [i75]Jincheng Zhong, Xingzhuo Guo, Jiaxiang Dong, Mingsheng Long:
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting. CoRR abs/2406.00773 (2024) - [i74]Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Yong Liu, Mingsheng Long, Jianmin Wang:
Deep Time Series Models: A Comprehensive Survey and Benchmark. CoRR abs/2407.13278 (2024) - [i73]Ningya Feng, Junwei Pan, Jialong Wu, Baixu Chen, Ximei Wang, Qian Li, Xian Hu, Jie Jiang, Mingsheng Long:
Long-Sequence Recommendation Models Need Decoupled Embeddings. CoRR abs/2410.02604 (2024) - [i72]Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Li Zhang, Jianmin Wang, Mingsheng Long:
Metadata Matters for Time Series: Informative Forecasting with Transformers. CoRR abs/2410.03806 (2024) - [i71]Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long:
Timer-XL: Long-Context Transformers for Unified Time Series Forecasting. CoRR abs/2410.04803 (2024) - 2023
- [j14]Haixu Wu, Hang Zhou, Mingsheng Long, Jianmin Wang:
Interpretable weather forecasting for worldwide stations with a unified deep model. Nat. Mac. Intell. 5(6): 602-611 (2023) - [j13]Yuchen Zhang, Mingsheng Long, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I. Jordan, Jianmin Wang:
Skilful nowcasting of extreme precipitation with NowcastNet. Nat. 619(7970): 526-532 (2023) - [j12]Zhangjie Cao, Kaichao You, Ziyang Zhang, Jianmin Wang, Mingsheng Long:
From Big to Small: Adaptive Learning to Partial-Set Domains. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1766-1780 (2023) - [j11]Yunbo Wang, Haixu Wu, Jianjin Zhang, Zhifeng Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long:
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 2208-2225 (2023) - [j10]Zhiyu Yao, Yunbo Wang, Haixu Wu, Jianmin Wang, Mingsheng Long:
ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(11): 13281-13296 (2023) - [j9]Yang Shu, Zhangjie Cao, Jinghan Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long:
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 15275-15291 (2023) - [c100]Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, Mingsheng Long:
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. ICLR 2023 - [c99]Xingzhuo Guo, Yuchen Zhang, Jianmin Wang, Mingsheng Long:
Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms. ICML 2023: 12108-12121 - [c98]Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long:
CLIPood: Generalizing CLIP to Out-of-Distributions. ICML 2023: 31716-31731 - [c97]Haixu Wu, Tengge Hu, Huakun Luo, Jianmin Wang, Mingsheng Long:
Solving High-Dimensional PDEs with Latent Spectral Models. ICML 2023: 37417-37438 - [c96]Jialong Wu, Haoyu Ma, Chaoyi Deng, Mingsheng Long:
Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning. NeurIPS 2023 - [c95]Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long:
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling. NeurIPS 2023 - [c94]Junguang Jiang, Baixu Chen, Junwei Pan, Ximei Wang, Dapeng Liu, Jie Jiang, Mingsheng Long:
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning. NeurIPS 2023 - [c93]Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long:
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors. NeurIPS 2023 - [c92]Jincheng Zhong, Haoyu Ma, Ximei Wang, Zhi Kou, Mingsheng Long:
Bi-tuning: Efficient Transfer from Pre-trained Models. ECML/PKDD (5) 2023: 357-373 - [i70]Junguang Jiang, Baixu Chen, Junwei Pan, Ximei Wang, Dapeng Liu, Jie Jiang, Mingsheng Long:
ForkMerge: Overcoming Negative Transfer in Multi-Task Learning. CoRR abs/2301.12618 (2023) - [i69]Haixu Wu, Tengge Hu, Huakun Luo, Jianmin Wang, Mingsheng Long:
Solving High-Dimensional PDEs with Latent Spectral Models. CoRR abs/2301.12664 (2023) - [i68]Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long:
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling. CoRR abs/2302.00861 (2023) - [i67]Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long:
CLIPood: Generalizing CLIP to Out-of-Distributions. CoRR abs/2302.00864 (2023) - [i66]Kaichao You, Anchang Bao, Guo Qin, Meng Cao, Ping Huang, Jiulong Shan, Mingsheng Long:
Tune-Mode ConvBN Blocks For Efficient Transfer Learning. CoRR abs/2305.11624 (2023) - [i65]Jialong Wu, Haoyu Ma, Chaoyi Deng, Mingsheng Long:
Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning. CoRR abs/2305.18499 (2023) - [i64]Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long:
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors. CoRR abs/2305.18803 (2023) - [i63]Haoyu Ma, Jialong Wu, Ningya Feng, Jianmin Wang, Mingsheng Long:
Harmony World Models: Boosting Sample Efficiency for Model-based Reinforcement Learning. CoRR abs/2310.00344 (2023) - [i62]Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, Mingsheng Long:
On the Embedding Collapse when Scaling up Recommendation Models. CoRR abs/2310.04400 (2023) - [i61]Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long:
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting. CoRR abs/2310.06625 (2023) - [i60]Lanxiang Xing, Haixu Wu, Yuezhou Ma, Jianmin Wang, Mingsheng Long:
HelmSim: Learning Helmholtz Dynamics for Interpretable Fluid Simulation. CoRR abs/2310.10565 (2023) - 2022
- [j8]Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long:
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs. J. Mach. Learn. Res. 23: 209:1-209:47 (2022) - [j7]Zhiyu Yao, Yunbo Wang, Jianmin Wang, Philip S. Yu, Mingsheng Long:
VideoDG: Generalizing Temporal Relations in Videos to Novel Domains. IEEE Trans. Pattern Anal. Mach. Intell. 44(11): 7989-8004 (2022) - [c91]Yiwen Qiu, Jialong Wu, Zhangjie Cao, Mingsheng Long:
Out-of-Dynamics Imitation Learning from Multimodal Demonstrations. CoRL 2022: 1071-1080 - [c90]Geng Chen, Wendong Zhang, Han Lu, Siyu Gao, Yunbo Wang, Mingsheng Long, Xiaokang Yang:
Continual Predictive Learning from Videos. CVPR 2022: 10718-10727 - [c89]Junguang Jiang, Baixu Chen, Jianmin Wang, Mingsheng Long:
Decoupled Adaptation for Cross-Domain Object Detection. ICLR 2022 - [c88]Ximei Wang, Xinyang Chen, Jianmin Wang, Mingsheng Long:
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model. ICLR 2022 - [c87]Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long:
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. ICLR 2022 - [c86]Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long:
Flowformer: Linearizing Transformers with Conservation Flows. ICML 2022: 24226-24242 - [c85]Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, Mingsheng Long:
Supported Policy Optimization for Offline Reinforcement Learning. NeurIPS 2022 - [c84]Baixu Chen, Junguang Jiang, Ximei Wang, Pengfei Wan, Jianmin Wang, Mingsheng Long:
Debiased Self-Training for Semi-Supervised Learning. NeurIPS 2022 - [c83]Yong Liu, Haixu Wu, Jianmin Wang, Mingsheng Long:
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting. NeurIPS 2022 - [c82]Yang Shu, Zhangjie Cao, Ziyang Zhang, Jianmin Wang, Mingsheng Long:
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models. NeurIPS 2022 - [i59]Junguang Jiang, Yang Shu, Jianmin Wang, Mingsheng Long:
Transferability in Deep Learning: A Survey. CoRR abs/2201.05867 (2022) - [i58]Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, Mingsheng Long:
Supported Policy Optimization for Offline Reinforcement Learning. CoRR abs/2202.06239 (2022) - [i57]Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long:
Flowformer: Linearizing Transformers with Conservation Flows. CoRR abs/2202.06258 (2022) - [i56]Baixu Chen, Junguang Jiang, Ximei Wang, Jianmin Wang, Mingsheng Long:
Debiased Pseudo Labeling in Self-Training. CoRR abs/2202.07136 (2022) - [i55]Zhangjie Cao, Kaichao You, Ziyang Zhang, Jianmin Wang, Mingsheng Long:
From Big to Small: Adaptive Learning to Partial-Set Domains. CoRR abs/2203.07375 (2022) - [i54]Geng Chen, Wendong Zhang, Han Lu, Siyu Gao, Yunbo Wang, Mingsheng Long, Xiaokang Yang:
Continual Predictive Learning from Videos. CoRR abs/2204.05624 (2022) - [i53]Chao Huang, Zhangjie Cao, Yunbo Wang, Jianmin Wang, Mingsheng Long:
MetaSets: Meta-Learning on Point Sets for Generalizable Representations. CoRR abs/2204.07311 (2022) - [i52]Yong Liu, Haixu Wu, Jianmin Wang, Mingsheng Long:
Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting. CoRR abs/2205.14415 (2022) - [i51]Yang Shu, Zhangjie Cao, Ziyang Zhang, Jianmin Wang, Mingsheng Long:
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models. CoRR abs/2206.03726 (2022) - [i50]Zhiyu Yao, Xinyang Chen, Sinan Wang, Qinyan Dai, Yumeng Li, Tanchao Zhu, Mingsheng Long:
Recommender Transformers with Behavior Pathways. CoRR abs/2206.06804 (2022) - [i49]Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, Mingsheng Long:
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. CoRR abs/2210.02186 (2022) - [i48]Yiwen Qiu, Jialong Wu, Zhangjie Cao, Mingsheng Long:
Out-of-Dynamics Imitation Learning from Multimodal Demonstrations. CoRR abs/2211.06839 (2022) - 2021
- [j6]Min-Ling Zhang, Sheng-Jun Huang, Mingsheng Long:
Preface. J. Comput. Sci. Technol. 36(3): 588-589 (2021) - [c81]Junguang Jiang, Yifei Ji, Ximei Wang, Yufeng Liu, Jianmin Wang, Mingsheng Long:
Regressive Domain Adaptation for Unsupervised Keypoint Detection. CVPR 2021: 6780-6789 - [c80]Bo Fu, Zhangjie Cao, Jianmin Wang, Mingsheng Long:
Transferable Query Selection for Active Domain Adaptation. CVPR 2021: 7272-7281 - [c79]Chao Huang, Zhangjie Cao, Yunbo Wang, Jianmin Wang, Mingsheng Long:
MetaSets: Meta-Learning on Point Sets for Generalizable Representations. CVPR 2021: 8863-8872 - [c78]Yang Shu, Zhangjie Cao, Chenyu Wang, Jianmin Wang, Mingsheng Long:
Open Domain Generalization with Domain-Augmented Meta-Learning. CVPR 2021: 9624-9633 - [c77]Haixu Wu, Zhiyu Yao, Jianmin Wang, Mingsheng Long:
MotionRNN: A Flexible Model for Video Prediction With Spacetime-Varying Motions. CVPR 2021: 15435-15444 - [c76]Xinyang Chen, Sinan Wang, Jianmin Wang, Mingsheng Long:
Representation Subspace Distance for Domain Adaptation Regression. ICML 2021: 1749-1759 - [c75]Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, Mingsheng Long:
Zoo-Tuning: Adaptive Transfer from A Zoo of Models. ICML 2021: 9626-9637 - [c74]Ximei Wang, Jinghan Gao, Mingsheng Long, Jianmin Wang:
Self-Tuning for Data-Efficient Deep Learning. ICML 2021: 10738-10748 - [c73]Kaichao You, Yong Liu, Jianmin Wang, Mingsheng Long:
LogME: Practical Assessment of Pre-trained Models for Transfer Learning. ICML 2021: 12133-12143 - [c72]Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long:
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. NeurIPS 2021: 22419-22430 - [c71]Hong Liu, Jianmin Wang, Mingsheng Long:
Cycle Self-Training for Domain Adaptation. NeurIPS 2021: 22968-22981 - [i47]Kaichao You, Yong Liu, Mingsheng Long, Jianmin Wang:
LogME: Practical Assessment of Pre-trained Models for Transfer Learning. CoRR abs/2102.11005 (2021) - [i46]Ximei Wang, Jinghan Gao, Jianmin Wang, Mingsheng Long:
Self-Tuning for Data-Efficient Deep Learning. CoRR abs/2102.12903 (2021) - [i45]Haixu Wu, Zhiyu Yao, Mingsheng Long, Jianmin Wang:
MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions. CoRR abs/2103.02243 (2021) - [i44]Hong Liu, Jianmin Wang, Mingsheng Long:
Cycle Self-Training for Domain Adaptation. CoRR abs/2103.03571 (2021) - [i43]Junguang Jiang, Yifei Ji, Ximei Wang, Yufeng Liu, Jianmin Wang, Mingsheng Long:
Regressive Domain Adaptation for Unsupervised Keypoint Detection. CoRR abs/2103.06175 (2021) - [i42]Yunbo Wang, Haixu Wu, Jianjin Zhang, Zhifeng Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long:
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning. CoRR abs/2103.09504 (2021) - [i41]Yang Shu, Zhangjie Cao, Chenyu Wang, Jianmin Wang, Mingsheng Long:
Open Domain Generalization with Domain-Augmented Meta-Learning. CoRR abs/2104.03620 (2021) - [i40]Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long:
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. CoRR abs/2106.13008 (2021) - [i39]Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, Mingsheng Long:
Zoo-Tuning: Adaptive Transfer from a Zoo of Models. CoRR abs/2106.15434 (2021) - [i38]Junguang Jiang, Baixu Chen, Jianmin Wang, Mingsheng Long:
Decoupled Adaptation for Cross-Domain Object Detection. CoRR abs/2110.02578 (2021) - [i37]Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long:
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. CoRR abs/2110.02642 (2021) - [i36]Zhiyu Yao, Yunbo Wang, Haixu Wu, Jianmin Wang, Mingsheng Long:
ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning. CoRR abs/2110.03882 (2021) - [i35]Ximei Wang, Xinyang Chen, Jianmin Wang, Mingsheng Long:
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model. CoRR abs/2110.04572 (2021) - [i34]Yang Shu, Zhangjie Cao, Jinghan Gao, Jianmin Wang, Mingsheng Long:
Omni-Training for Data-Efficient Deep Learning. CoRR abs/2110.07510 (2021) - [i33]Kaichao You, Yong Liu, Jianmin Wang, Michael I. Jordan, Mingsheng Long:
Ranking and Tuning Pre-trained Models: A New Paradigm of Exploiting Model Hubs. CoRR abs/2110.10545 (2021) - 2020
- [c70]Liang Li, Weirui Ye, Mingsheng Long, Yateng Tang, Jin Xu, Jianmin Wang:
Simultaneous Learning of Pivots and Representations for Cross-Domain Sentiment Classification. AAAI 2020: 8220-8227 - [c69]Ying Jin, Zhangjie Cao, Mingsheng Long, Jianmin Wang:
Transferring Pretrained Networks to Small Data via Category Decorrelation. BMVC 2020 - [c68]Sinan Wang, Xinyang Chen, Yunbo Wang, Mingsheng Long, Jianmin Wang:
Progressive Adversarial Networks for Fine-Grained Domain Adaptation. CVPR 2020: 9210-9219 - [c67]Yunbo Wang, Jiajun Wu, Mingsheng Long, Joshua B. Tenenbaum:
Probabilistic Video Prediction From Noisy Data With a Posterior Confidence. CVPR 2020: 10827-10836 - [c66]Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, Han Hu:
Negative Margin Matters: Understanding Margin in Few-Shot Classification. ECCV (4) 2020: 438-455 - [c65]Ying Jin, Ximei Wang, Mingsheng Long, Jianmin Wang:
Minimum Class Confusion for Versatile Domain Adaptation. ECCV (21) 2020: 464-480 - [c64]Bo Fu, Zhangjie Cao, Mingsheng Long, Jianmin Wang:
Learning to Detect Open Classes for Universal Domain Adaptation. ECCV (15) 2020: 567-583 - [c63]Ying Jin, Yunbo Wang, Mingsheng Long, Jianmin Wang, Philip S. Yu, Jiaguang Sun:
A Multi-Player Minimax Game for Generative Adversarial Networks. ICME 2020: 1-6 - [c62]Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jianmin Wang, Philip S. Yu, Jiaguang Sun:
Multi-Task Learning of Generalizable Representations for Video Action Recognition. ICME 2020: 1-6 - [c61]Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jianmin Wang:
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks. ICML 2020: 10778-10788 - [c60]Junguang Jiang, Ximei Wang, Mingsheng Long, Jianmin Wang:
Resource Efficient Domain Adaptation. ACM Multimedia 2020: 2220-2228 - [c59]Zhi Kou, Kaichao You, Mingsheng Long, Jianmin Wang:
Stochastic Normalization. NeurIPS 2020 - [c58]Hong Liu, Mingsheng Long, Jianmin Wang, Yu Wang:
Learning to Adapt to Evolving Domains. NeurIPS 2020 - [c57]Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Calibration with Lower Bias and Variance in Domain Adaptation. NeurIPS 2020 - [c56]Kaichao You, Zhi Kou, Mingsheng Long, Jianmin Wang:
Co-Tuning for Transfer Learning. NeurIPS 2020 - [i32]Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, Han Hu:
Negative Margin Matters: Understanding Margin in Few-shot Classification. CoRR abs/2003.12060 (2020) - [i31]Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Calibration with Lower Bias and Variance in Domain Adaptation. CoRR abs/2007.08259 (2020) - [i30]Yuchen Zhang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
On Localized Discrepancy for Domain Adaptation. CoRR abs/2008.06242 (2020) - [i29]Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jianmin Wang:
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks. CoRR abs/2009.11763 (2020) - [i28]Jincheng Zhong, Ximei Wang, Zhi Kou, Jianmin Wang, Mingsheng Long:
Bi-tuning of Pre-trained Representations. CoRR abs/2011.06182 (2020)
2010 – 2019
- 2019
- [j5]Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Transferable Representation Learning with Deep Adaptation Networks. IEEE Trans. Pattern Anal. Mach. Intell. 41(12): 3071-3085 (2019) - [c55]Yang Shu, Zhangjie Cao, Mingsheng Long, Jianmin Wang:
Transferable Curriculum for Weakly-Supervised Domain Adaptation. AAAI 2019: 4951-4958 - [c54]Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long, Jianmin Wang:
Transferable Attention for Domain Adaptation. AAAI 2019: 5345-5352 - [c53]Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Universal Domain Adaptation. CVPR 2019: 2720-2729 - [c52]Hong Liu, Zhangjie Cao, Mingsheng Long, Jianmin Wang, Qiang Yang:
Separate to Adapt: Open Set Domain Adaptation via Progressive Separation. CVPR 2019: 2927-2936 - [c51]Zhangjie Cao, Kaichao You, Mingsheng Long, Jianmin Wang, Qiang Yang:
Learning to Transfer Examples for Partial Domain Adaptation. CVPR 2019: 2985-2994 - [c50]Yunbo Wang, Jianjin Zhang, Hongyu Zhu, Mingsheng Long, Jianmin Wang, Philip S. Yu:
Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics. CVPR 2019: 9154-9162 - [c49]Rong Kang, Yue Cao, Mingsheng Long, Jianmin Wang, Philip S. Yu:
Maximum-Margin Hamming Hashing. ICCV 2019: 8251-8260 - [c48]Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei:
Eidetic 3D LSTM: A Model for Video Prediction and Beyond. ICLR (Poster) 2019 - [c47]Jianjin Zhang, Yunbo Wang, Mingsheng Long, Jianmin Wang, Philip S. Yu:
Z-Order Recurrent Neural Networks for Video Prediction. ICME 2019: 230-235 - [c46]Xinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wang:
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation. ICML 2019: 1081-1090 - [c45]Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers. ICML 2019: 4013-4022 - [c44]Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan:
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. ICML 2019: 7124-7133 - [c43]Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan:
Bridging Theory and Algorithm for Domain Adaptation. ICML 2019: 7404-7413 - [c42]Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jianmin Wang:
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning. NeurIPS 2019: 1906-1916 - [c41]Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks. NeurIPS 2019: 1951-1961 - [i27]Bin Liu, Yue Cao, Mingsheng Long, Jianmin Wang, Jingdong Wang:
Deep Triplet Quantization. CoRR abs/1902.00153 (2019) - [i26]Binhang Yuan, Chen Wang, Fei Jiang, Mingsheng Long, Philip S. Yu, Yuan Liu:
WaveletFCNN: A Deep Time Series Classification Model for Wind Turbine Blade Icing Detection. CoRR abs/1902.05625 (2019) - [i25]Yunbo Wang, Mingsheng Long, Jianmin Wang, Philip S. Yu:
Spatiotemporal Pyramid Network for Video Action Recognition. CoRR abs/1903.01038 (2019) - [i24]Zhangjie Cao, Kaichao You, Mingsheng Long, Jianmin Wang, Qiang Yang:
Learning to Transfer Examples for Partial Domain Adaptation. CoRR abs/1903.12230 (2019) - [i23]Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan:
Bridging Theory and Algorithm for Domain Adaptation. CoRR abs/1904.05801 (2019) - [i22]Chen Qian, Lijie Wen, Mingsheng Long, Yanwei Li, Akhil Kumar, Jianmin Wang:
Process Extraction from Texts via Multi-Task Architecture. CoRR abs/1906.02127 (2019) - [i21]Kaichao You, Mingsheng Long, Michael I. Jordan, Jianmin Wang:
Learning Stages: Phenomenon, Root Cause, Mechanism Hypothesis, and Implications. CoRR abs/1908.01878 (2019) - [i20]Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Towards Understanding the Transferability of Deep Representations. CoRR abs/1909.12031 (2019) - [i19]Ying Jin, Ximei Wang, Mingsheng Long, Jianmin Wang:
Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation. CoRR abs/1912.03699 (2019) - [i18]Zhiyu Yao, Yunbo Wang, Xingqiang Du, Mingsheng Long, Jianmin Wang:
Adversarial Pyramid Network for Video Domain Generalization. CoRR abs/1912.03716 (2019) - 2018
- [c40]Yue Cao, Mingsheng Long, Jianmin Wang:
Unsupervised Domain Adaptation With Distribution Matching Machines. AAAI 2018: 2795-2802 - [c39]Zhongyi Pei, Zhangjie Cao, Mingsheng Long, Jianmin Wang:
Multi-Adversarial Domain Adaptation. AAAI 2018: 3934-3941 - [c38]Zhangjie Cao, Mingsheng Long, Chao Huang, Jianmin Wang:
Transfer Adversarial Hashing for Hamming Space Retrieval. AAAI 2018: 6698-6705 - [c37]Yue Cao, Mingsheng Long, Bin Liu, Jianmin Wang:
Deep Cauchy Hashing for Hamming Space Retrieval. CVPR 2018: 1229-1237 - [c36]Yue Cao, Bin Liu, Mingsheng Long, Jianmin Wang:
HashGAN: Deep Learning to Hash With Pair Conditional Wasserstein GAN. CVPR 2018: 1287-1296 - [c35]Zhangjie Cao, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Partial Transfer Learning With Selective Adversarial Networks. CVPR 2018: 2724-2732 - [c34]Zhangjie Cao, Lijia Ma, Mingsheng Long, Jianmin Wang:
Partial Adversarial Domain Adaptation. ECCV (8) 2018: 139-155 - [c33]Yue Cao, Bin Liu, Mingsheng Long, Jianmin Wang:
Cross-Modal Hamming Hashing. ECCV (1) 2018: 207-223 - [c32]Yunbo Wang, Zhifeng Gao, Mingsheng Long, Jianmin Wang, Philip S. Yu:
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning. ICML 2018: 5110-5119 - [c31]Ziru Xu, Yunbo Wang, Mingsheng Long, Jianmin Wang:
PredCNN: Predictive Learning with Cascade Convolutions. IJCAI 2018: 2940-2947 - [c30]Bin Liu, Yue Cao, Mingsheng Long, Jianmin Wang, Jingdong Wang:
Deep Triplet Quantization. ACM Multimedia 2018: 755-763 - [c29]Zhangjie Cao, Ziping Sun, Mingsheng Long, Jianmin Wang, Philip S. Yu:
Deep Priority Hashing. ACM Multimedia 2018: 1653-1661 - [c28]Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Conditional Adversarial Domain Adaptation. NeurIPS 2018: 1647-1657 - [c27]Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Generalized Zero-Shot Learning with Deep Calibration Network. NeurIPS 2018: 2009-2019 - [i17]Yunbo Wang, Zhifeng Gao, Mingsheng Long, Jianmin Wang, Philip S. Yu:
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning. CoRR abs/1804.06300 (2018) - [i16]Zhangjie Cao, Lijia Ma, Mingsheng Long, Jianmin Wang:
Partial Adversarial Domain Adaptation. CoRR abs/1808.04205 (2018) - [i15]Zhangjie Cao, Ziping Sun, Mingsheng Long, Jianmin Wang, Philip S. Yu:
Deep Priority Hashing. CoRR abs/1809.01238 (2018) - [i14]Zhongyi Pei, Zhangjie Cao, Mingsheng Long, Jianmin Wang:
Multi-Adversarial Domain Adaptation. CoRR abs/1809.02176 (2018) - [i13]Yunbo Wang, Jianjin Zhang, Hongyu Zhu, Mingsheng Long, Jianmin Wang, Philip S. Yu:
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics. CoRR abs/1811.07490 (2018) - [i12]Yunbo Wang, Zhiyu Yao, Hongyu Zhu, Mingsheng Long, Jianmin Wang, Philip S. Yu:
Reversing Two-Stream Networks with Decoding Discrepancy Penalty for Robust Action Recognition. CoRR abs/1811.08362 (2018) - 2017
- [c26]Zhangjie Cao, Mingsheng Long, Jianmin Wang, Qiang Yang:
Transitive Hashing Network for Heterogeneous Multimedia Retrieval. AAAI 2017: 81-87 - [c25]Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu:
Collective Deep Quantization for Efficient Cross-Modal Retrieval. AAAI 2017: 3974-3980 - [c24]Yue Cao, Mingsheng Long, Jianmin Wang:
Correlation Hashing Network for Efficient Cross-Modal Retrieval. BMVC 2017 - [c23]Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu:
Deep Visual-Semantic Quantization for Efficient Image Retrieval. CVPR 2017: 916-925 - [c22]Yunbo Wang, Mingsheng Long, Jianmin Wang, Philip S. Yu:
Spatiotemporal Pyramid Network for Video Action Recognition. CVPR 2017: 2097-2106 - [c21]Zhangjie Cao, Mingsheng Long, Jianmin Wang, Philip S. Yu:
HashNet: Deep Learning to Hash by Continuation. ICCV 2017: 5609-5618 - [c20]Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan:
Deep Transfer Learning with Joint Adaptation Networks. ICML 2017: 2208-2217 - [c19]Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, Philip S. Yu:
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs. NIPS 2017: 879-888 - [c18]Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu:
Learning Multiple Tasks with Multilinear Relationship Networks. NIPS 2017: 1594-1603 - [i11]Zhangjie Cao, Mingsheng Long, Jianmin Wang, Philip S. Yu:
HashNet: Deep Learning to Hash by Continuation. CoRR abs/1702.00758 (2017) - [i10]Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Domain Adaptation with Randomized Multilinear Adversarial Networks. CoRR abs/1705.10667 (2017) - [i9]Zhangjie Cao, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Partial Transfer Learning with Selective Adversarial Networks. CoRR abs/1707.07901 (2017) - [i8]Zhangjie Cao, Mingsheng Long, Chao Huang, Jianmin Wang:
Transfer Adversarial Hashing for Hamming Space Retrieval. CoRR abs/1712.04616 (2017) - 2016
- [j4]Mingsheng Long, Jianmin Wang, Yue Cao, Jia-Guang Sun, Philip S. Yu:
Deep Learning of Transferable Representation for Scalable Domain Adaptation. IEEE Trans. Knowl. Data Eng. 28(8): 2027-2040 (2016) - [c17]Han Zhu, Mingsheng Long, Jianmin Wang, Yue Cao:
Deep Hashing Network for Efficient Similarity Retrieval. AAAI 2016: 2415-2421 - [c16]Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu, Qingfu Wen:
Deep Quantization Network for Efficient Image Retrieval. AAAI 2016: 3457-3463 - [c15]Yue Cao, Mingsheng Long, Jianmin Wang, Qiang Yang, Philip S. Yu:
Deep Visual-Semantic Hashing for Cross-Modal Retrieval. KDD 2016: 1445-1454 - [c14]Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu:
Correlation Autoencoder Hashing for Supervised Cross-Modal Search. ICMR 2016: 197-204 - [c13]Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan:
Unsupervised Domain Adaptation with Residual Transfer Networks. NIPS 2016: 136-144 - [c12]Mingsheng Long, Yue Cao, Jianmin Wang, Philip S. Yu:
Composite Correlation Quantization for Efficient Multimodal Retrieval. SIGIR 2016: 579-588 - [i7]Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Unsupervised Domain Adaptation with Residual Transfer Networks. CoRR abs/1602.04433 (2016) - [i6]Yue Cao, Mingsheng Long, Jianmin Wang:
Correlation Hashing Network for Efficient Cross-Modal Retrieval. CoRR abs/1602.06697 (2016) - [i5]Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Deep Transfer Learning with Joint Adaptation Networks. CoRR abs/1605.06636 (2016) - [i4]Zhangjie Cao, Mingsheng Long, Qiang Yang:
Transitive Hashing Network for Heterogeneous Multimedia Retrieval. CoRR abs/1608.04307 (2016) - 2015
- [j3]Mingsheng Long, Jianmin Wang, Jia-Guang Sun, Philip S. Yu:
Domain Invariant Transfer Kernel Learning. IEEE Trans. Knowl. Data Eng. 27(6): 1519-1532 (2015) - [c11]Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan:
Learning Transferable Features with Deep Adaptation Networks. ICML 2015: 97-105 - [i3]Mingsheng Long, Jianmin Wang:
Learning Transferable Features with Deep Adaptation Networks. CoRR abs/1502.02791 (2015) - [i2]Mingsheng Long, Jianmin Wang, Philip S. Yu:
Compositional Correlation Quantization for Large-Scale Multimodal Search. CoRR abs/1504.04818 (2015) - [i1]Mingsheng Long, Jianmin Wang:
Learning Multiple Tasks with Deep Relationship Networks. CoRR abs/1506.02117 (2015) - 2014
- [j2]Mingsheng Long, Jianmin Wang, Guiguang Ding, Sinno Jialin Pan, Philip S. Yu:
Adaptation Regularization: A General Framework for Transfer Learning. IEEE Trans. Knowl. Data Eng. 26(5): 1076-1089 (2014) - [j1]Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, Qiang Yang:
Transfer Learning with Graph Co-Regularization. IEEE Trans. Knowl. Data Eng. 26(7): 1805-1818 (2014) - [c10]Xiangdong Huang, Jianmin Wang, Jian Bai, Guiguang Ding, Mingsheng Long:
Inherent Replica Inconsistency in Cassandra. BigData Congress 2014: 740-747 - [c9]Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu:
Transfer Joint Matching for Unsupervised Domain Adaptation. CVPR 2014: 1410-1417 - [c8]Wu Xiang, Jianmin Wang, Mingsheng Long:
Local Hybrid Coding for Image Classification. ICPR 2014: 3744-3749 - 2013
- [c7]Mingsheng Long, Guiguang Ding, Jianmin Wang, Jiaguang Sun, Yuchen Guo, Philip S. Yu:
Transfer Sparse Coding for Robust Image Representation. CVPR 2013: 407-414 - [c6]Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu:
Transfer Feature Learning with Joint Distribution Adaptation. ICCV 2013: 2200-2207 - [c5]Jiangfeng Shi, Mingsheng Long, Qiang Liu, Guiguang Ding, Jianmin Wang:
Twin Bridge Transfer Learning for Sparse Collaborative Filtering. PAKDD (1) 2013: 496-507 - 2012
- [c4]Lianghao Li, Xiaoming Jin, Mingsheng Long:
Topic Correlation Analysis for Cross-Domain Text Classification. AAAI 2012: 998-1004 - [c3]Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, Qiang Yang:
Transfer Learning with Graph Co-Regularization. AAAI 2012: 1033-1039 - [c2]Mingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, Wei Wang:
Dual Transfer Learning. SDM 2012: 540-551 - 2010
- [c1]Mingsheng Long, Wei Cheng, Xiaoming Jin, Jianmin Wang, Dou Shen:
Transfer Learning via Cluster Correspondence Inference. ICDM 2010: 917-922
Coauthor Index
[j15] [c110] [c109] [c107] [c106] [c105] [c104] [c102] [i87] [i86] [i85] [i84] [i83] [i82] [i81] [i80] [i79] [i78] [i74] [i72] [i71] [j14] [j13] [j12] [j11] [j10] [j9] [c100] [c99] [c98] [c97] [c95] [c93] [i69] [i68] [i67] [i64] [i63] [i60] [j8] [j7] [c89] [c88] [c87] [c86] [c85] [c84] [c83] [c82] [i59] [i58] [i57] [i56] [i55] [i53] [i52] [i51] [i49] [c81] [c80] [c79] [c78] [c77] [c76] [c75] [c74] [c73] [c72] [c71] [i47] [i46] [i45] [i44] [i43] [i42] [i41] [i40] [i39] [i38] [i37] [i36] [i35] [i34] [i33] [c70] [c69] [c68] [c65] [c64] [c63] [c62] [c61] [c60] [c59] [c58] [c57] [c56] [i31] [i30] [i29] [i28] [j5] [c55] [c54] [c53] [c52] [c51] [c50] [c49] [c47] [c46] [c45] [c42] [c41] [i27] [i25] [i24] [i22] [i21] [i20] [i19] [i18] [c40] [c39] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [c28] [c27] [i17] [i16] [i15] [i14] [i13] [i12] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [i11] [i10] [i9] [i8] [j4] [c17] [c16] [c15] [c14] [c13] [c12] [i7] [i6] [i5] [j3] [c11] [i3] [i2] [i1] [j2] [j1] [c10] [c9] [c8] [c7] [c6] [c5] [c3] [c2] [c1]
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