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2020 – today
- 2023
[j22]Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, De-Chuan Zhan:
PyCIL: a Python toolbox for class-incremental learning. Sci. China Inf. Sci. 66(9) (2023)- [j21]Xin-Chun Li
, Yang Yang, De-Chuan Zhan:
MrTF: model refinery for transductive federated learning. Data Min. Knowl. Discov. 37(5): 2046-2069 (2023) - [j20]Han-Jia Ye, Su Lu, De-Chuan Zhan:
Generalized Knowledge Distillation via Relationship Matching. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1817-1834 (2023) - [j19]Han-Jia Ye, Lu Han, De-Chuan Zhan:
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 3721-3737 (2023) - [j18]Yang Yang, Jia-Qi Yang, Ran Bao, De-Chuan Zhan, Hengshu Zhu, Xiaoru Gao, Hui Xiong, Jian Yang:
Corporate Relative Valuation Using Heterogeneous Multi-Modal Graph Neural Network. IEEE Trans. Knowl. Data Eng. 35(1): 211-224 (2023) - [j17]Yang Yang, Da-Wei Zhou, De-Chuan Zhan, Hui Xiong, Yuan Jiang, Jian Yang:
Cost-Effective Incremental Deep Model: Matching Model Capacity With the Least Sampling. IEEE Trans. Knowl. Data Eng. 35(4): 3575-3588 (2023) - [c67]Shaowei Zhang, Jiahan Cao, Lei Yuan, Yang Yu, De-Chuan Zhan:
Self-Motivated Multi-Agent Exploration. AAMAS 2023: 476-484 - [c66]Yi-Kai Zhang, Qi-Wei Wang, De-Chuan Zhan, Han-Jia Ye:
Learning Debiased Representations via Conditional Attribute Interpolation. CVPR 2023: 7599-7608 - [c65]Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, De-Chuan Zhan:
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning. ICLR 2023 - [c64]Lu Han, Han-Jia Ye, De-Chuan Zhan:
Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps. ICLR 2023 - [c63]Fu-Yun Wang, Da-Wei Zhou, Liu Liu, Han-Jia Ye, Yatao Bian, De-Chuan Zhan, Peilin Zhao:
BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion. ICLR 2023 - [c62]Yichu Xu, Wenqian Li, Yinchuan Li, Yunfeng Shao, Yan Pang, De-Chuan Zhan:
One Important Thing To Do Before Federated Training. Tiny Papers @ ICLR 2023 - [c61]Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Preserving Locality in Vision Transformers for Class Incremental Learning. ICME 2023: 1157-1162 - [c60]Shenghua Wan, Yucen Wang, Minghao Shao, Ruying Chen, De-Chuan Zhan:
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models. ICML 2023: 35426-35443 - [c59]Jia-Qi Yang, Yucheng Xu, Jia-Lei Shen, Ke-Bin Fan, De-Chuan Zhan, Yang Yang:
IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics. KDD 2023: 2930-2940 - [i47]Shaowei Zhang, Jiahan Cao, Lei Yuan, Yang Yu, De-Chuan Zhan:
Self-Motivated Multi-Agent Exploration. CoRR abs/2301.02083 (2023) - [i46]Lu Han, Han-Jia Ye, De-Chuan Zhan:
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. CoRR abs/2301.06010 (2023) - [i45]Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu:
Deep Class-Incremental Learning: A Survey. CoRR abs/2302.03648 (2023) - [i44]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu:
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need. CoRR abs/2303.07338 (2023) - [i43]Lu Han, Han-Jia Ye, De-Chuan Zhan:
The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting. CoRR abs/2304.05206 (2023) - [i42]Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Preserving Locality in Vision Transformers for Class Incremental Learning. CoRR abs/2304.06971 (2023) - [i41]Xin-Chun Li, Yang Yang, De-Chuan Zhan:
MrTF: Model Refinery for Transductive Federated Learning. CoRR abs/2305.04201 (2023) - [i40]Jia-Qi Yang, Yucheng Xu, Jia-Lei Shen, Ke-Bin Fan, De-Chuan Zhan, Yang Yang:
IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics. CoRR abs/2305.18978 (2023) - [i39]Da-Wei Zhou, Yuanhan Zhang, Jingyi Ning, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu:
Learning without Forgetting for Vision-Language Models. CoRR abs/2305.19270 (2023) - [i38]Yi-Kai Zhang, Ting-Ji Huang, Yao-Xiang Ding, De-Chuan Zhan, Han-Jia Ye:
Model Spider: Learning to Rank Pre-Trained Models Efficiently. CoRR abs/2306.03900 (2023) - [i37]Jia-Qi Yang, De-Chuan Zhan, Le Gan:
Beyond Probability Partitions: Calibrating Neural Networks with Semantic Aware Grouping. CoRR abs/2306.04985 (2023) - [i36]Jia-Qi Yang, Chenglei Dai, Dan Ou, Ju Huang, De-Chuan Zhan, Qingwen Liu, Xiaoyi Zeng, Yang Yang:
COURIER: Contrastive User Intention Reconstruction for Large-Scale Pre-Train of Image Features. CoRR abs/2306.05001 (2023) - [i35]Shenghua Wan, Yucen Wang, Minghao Shao, Ruying Chen, De-Chuan Zhan:
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models. CoRR abs/2306.10695 (2023) - [i34]Qi-Wei Wang, Hongyu Lu, Yu Chen, Da-Wei Zhou, De-Chuan Zhan, Ming Chen, Han-Jia Ye:
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data. CoRR abs/2307.07509 (2023) - [i33]Yi-Kai Zhang, Lu Ren, Chao Yi, Qi-Wei Wang, De-Chuan Zhan, Han-Jia Ye:
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse. CoRR abs/2308.09158 (2023) - [i32]Hai-Long Sun, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
PILOT: A Pre-Trained Model-Based Continual Learning Toolbox. CoRR abs/2309.07117 (2023) - 2022
- [j16]Lu Han, Han-Jia Ye, De-Chuan Zhan:
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. Trans. Mach. Learn. Res. 2022 (2022) - [j15]Da-Wei Zhou, Yang Yang, De-Chuan Zhan:
Learning to Classify With Incremental New Class. IEEE Trans. Neural Networks Learn. Syst. 33(6): 2429-2443 (2022) - [c58]Jia-Qi Yang, Ke-Bin Fan, Hao Ma, De-Chuan Zhan:
RID-Noise: Towards Robust Inverse Design under Noisy Environments. AAAI 2022: 4654-4661 - [c57]Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, De-Chuan Zhan:
Forward Compatible Few-Shot Class-Incremental Learning. CVPR 2022: 9036-9046 - [c56]Xin-Chun Li, Yichu Xu, Shaoming Song, Bingshuai Li, Yinchuan Li, Yunfeng Shao, De-Chuan Zhan:
Federated Learning with Position-Aware Neurons. CVPR 2022: 10072-10081 - [c55]Han-Jia Ye, Yi Shi, De-Chuan Zhan:
Identifying Ambiguous Similarity Conditions via Semantic Matching. CVPR 2022: 16589-16598 - [c54]Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
FOSTER: Feature Boosting and Compression for Class-Incremental Learning. ECCV (25) 2022: 398-414 - [c53]Xin-Chun Li, Yan-Jia Wang, Le Gan, De-Chuan Zhan:
Exploring Transferability Measures and Domain Selection in Cross-Domain Slot Filling. ICASSP 2022: 3758-3762 - [c52]Yi-Kai Zhang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation. INTERSPEECH 2022: 531-535 - [c51]Xin-Chun Li, Jin-Lin Tang, Shaoming Song, Bingshuai Li, Yinchuan Li, Yunfeng Shao, Le Gan, De-Chuan Zhan:
Avoid Overfitting User Specific Information in Federated Keyword Spotting. INTERSPEECH 2022: 3869-3873 - [c50]Xin Han, Ye Zhu, Kai Ming Ting, De-Chuan Zhan, Gang Li:
Streaming Hierarchical Clustering Based on Point-Set Kernel. KDD 2022: 525-533 - [c49]Jia-Qi Yang, De-Chuan Zhan:
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems. NeurIPS 2022 - [c48]Xin-Chun Li, Wen-Shu Fan, Shaoming Song, Yinchuan Li, Bingshuai Li, Yunfeng Shao, De-Chuan Zhan:
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again. NeurIPS 2022 - [i31]Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, De-Chuan Zhan:
Forward Compatible Few-Shot Class-Incremental Learning. CoRR abs/2203.06953 (2022) - [i30]Xin-Chun Li, Yi-Chu Xu, Shaoming Song, Bingshuai Li, Yinchuan Li, Yunfeng Shao, De-Chuan Zhan:
Federated Learning with Position-Aware Neurons. CoRR abs/2203.14666 (2022) - [i29]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks. CoRR abs/2203.17030 (2022) - [i28]Han-Jia Ye, Yi Shi, De-Chuan Zhan:
Identifying Ambiguous Similarity Conditions via Semantic Matching. CoRR abs/2204.04053 (2022) - [i27]Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
FOSTER: Feature Boosting and Compression for Class-Incremental Learning. CoRR abs/2204.04662 (2022) - [i26]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Faculty Distillation with Optimal Transport. CoRR abs/2204.11526 (2022) - [i25]Han-Jia Ye, Su Lu, De-Chuan Zhan:
Generalized Knowledge Distillation via Relationship Matching. CoRR abs/2205.01915 (2022) - [i24]Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, De-Chuan Zhan:
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning. CoRR abs/2205.13218 (2022) - [i23]Jia-Qi Yang, De-Chuan Zhan:
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems. CoRR abs/2206.00407 (2022) - [i22]Lu Han, Han-Jia Ye, De-Chuan Zhan:
Contrastive Principal Component Learning: Modeling Similarity by Augmentation Overlap. CoRR abs/2206.00471 (2022) - [i21]Xin-Chun Li, Jin-Lin Tang, Shaoming Song, Bingshuai Li, Yinchuan Li, Yunfeng Shao, Le Gan, De-Chuan Zhan:
Avoid Overfitting User Specific Information in Federated Keyword Spotting. CoRR abs/2206.08864 (2022) - [i20]Xin-Chun Li, Wen-Shu Fan, Shaoming Song, Yinchuan Li, Bingshuai Li, Yunfeng Shao, De-Chuan Zhan:
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again. CoRR abs/2210.04427 (2022) - 2021
- [j14]Xin-Chun Li, De-Chuan Zhan, Jia-Qi Yang, Yi Shi:
Deep multiple instance selection. Sci. China Inf. Sci. 64(3) (2021) - [j13]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan:
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning. Int. J. Comput. Vis. 129(6): 1930-1953 (2021) - [j12]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou:
Heterogeneous Few-Shot Model Rectification With Semantic Mapping. IEEE Trans. Pattern Anal. Mach. Intell. 43(11): 3878-3891 (2021) - [j11]Yang Yang, De-Chuan Zhan, Yi-Feng Wu, Zhi-Bin Liu, Hui Xiong, Yuan Jiang:
Semi-Supervised Multi-Modal Clustering and Classification with Incomplete Modalities. IEEE Trans. Knowl. Data Eng. 33(2): 682-695 (2021) - [j10]Yang Yang, Zhao-Yang Fu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang:
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. IEEE Trans. Knowl. Data Eng. 33(2): 696-709 (2021) - [c47]Jia-Qi Yang, Xiang Li, Shuguang Han, Tao Zhuang, De-Chuan Zhan, Xiaoyi Zeng, Bin Tong:
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling. AAAI 2021: 4582-4589 - [c46]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Tailoring Embedding Function to Heterogeneous Few-Shot Tasks by Global and Local Feature Adaptors. AAAI 2021: 8776-8783 - [c45]Han-Jia Ye, Xin-Chun Li, De-Chuan Zhan:
Task Cooperation for Semi-Supervised Few-Shot Learning. AAAI 2021: 10682-10690 - [c44]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Learning Placeholders for Open-Set Recognition. CVPR 2021: 4401-4410 - [c43]Han-Jia Ye, De-Chuan Zhan, Wei-Lun Chao:
Procrustean Training for Imbalanced Deep Learning. ICCV 2021: 92-102 - [c42]Yang Yang, Chubing Zhang, Yi-Chu Xu, Dianhai Yu, De-Chuan Zhan, Jian Yang:
Rethinking Label-Wise Cross-Modal Retrieval from A Semantic Sharing Perspective. IJCAI 2021: 3300-3306 - [c41]Cheng Hang, Wei Wang, De-Chuan Zhan:
Multi-Modal Multi-Instance Multi-Label Learning with Graph Convolutional Network. IJCNN 2021: 1-8 - [c40]Xin-Chun Li, De-Chuan Zhan:
FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data. KDD 2021: 995-1005 - [c39]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Co-Transport for Class-Incremental Learning. ACM Multimedia 2021: 1645-1654 - [c38]Su Lu, Han-Jia Ye, Le Gan, De-Chuan Zhan:
Towards Enabling Meta-Learning from Target Models. NeurIPS 2021: 8060-8071 - [c37]Da-Wei Zhou, Yang Yang, De-Chuan Zhan:
Detecting Sequentially Novel Classes with Stable Generalization Ability. PAKDD (1) 2021: 371-382 - [c36]Xin-Chun Li, De-Chuan Zhan, Yunfeng Shao, Bingshuai Li, Shaoming Song:
FedPHP: Federated Personalization with Inherited Private Models. ECML/PKDD (1) 2021: 587-602 - [i19]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Learning Placeholders for Open-Set Recognition. CoRR abs/2103.15086 (2021) - [i18]Han-Jia Ye, De-Chuan Zhan, Wei-Lun Chao:
Procrustean Training for Imbalanced Deep Learning. CoRR abs/2104.01769 (2021) - [i17]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Support-Target Protocol for Meta-Learning. CoRR abs/2104.03736 (2021) - [i16]Su Lu, Han-Jia Ye, De-Chuan Zhan:
Few-Shot Action Recognition with Compromised Metric via Optimal Transport. CoRR abs/2104.03737 (2021) - [i15]Yang Yang, Zhao-Yang Fu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang:
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. CoRR abs/2104.08489 (2021) - [i14]Han-Jia Ye, Da-Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, De-Chuan Zhan:
Contextualizing Multiple Tasks via Learning to Decompose. CoRR abs/2106.08112 (2021) - [i13]Han-Jia Ye, Lu Ming, De-Chuan Zhan, Wei-Lun Chao:
Few-Shot Learning with a Strong Teacher. CoRR abs/2107.00197 (2021) - [i12]Xin-Chun Li, Le Gan, De-Chuan Zhan, Yunfeng Shao, Bingshuai Li, Shaoming Song:
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes. CoRR abs/2107.11954 (2021) - [i11]Xin-Chun Li, De-Chuan Zhan, Yunfeng Shao, Bingshuai Li, Shaoming Song:
Preliminary Steps Towards Federated Sentiment Classification. CoRR abs/2107.11956 (2021) - [i10]Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan:
Co-Transport for Class-Incremental Learning. CoRR abs/2107.12654 (2021) - [i9]Jiahan Cao, Lei Yuan, Jianhao Wang, Shaowei Zhang, Chongjie Zhang, Yang Yu, De-Chuan Zhan:
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates. CoRR abs/2109.12508 (2021) - [i8]Jia-Qi Yang, Ke-Bin Fan, Hao Ma, De-Chuan Zhan:
RID-Noise: Towards Robust Inverse Design under Noisy Environments. CoRR abs/2112.03912 (2021) - [i7]Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, De-Chuan Zhan:
PyCIL: A Python Toolbox for Class-Incremental Learning. CoRR abs/2112.12533 (2021) - 2020
- [j9]Yang Yang, Nengjun Zhu, Yi-Feng Wu, Jian Cao, De-Chuan Zhan, Hui Xiong:
A semi-supervised attention model for identifying authentic sneakers. Big Data Min. Anal. 3(1): 29-40 (2020) - [j8]Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan:
Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach. Mach. Learn. 109(3): 643-664 (2020) - [j7]Han-Jia Ye, De-Chuan Zhan, Nan Li, Yuan Jiang:
Learning Multiple Local Metrics: Global Consideration Helps. IEEE Trans. Pattern Anal. Mach. Intell. 42(7): 1698-1712 (2020) - [c35]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions. CVPR 2020: 8805-8814 - [c34]Han-Jia Ye, Su Lu, De-Chuan Zhan:
Distilling Cross-Task Knowledge via Relationship Matching. CVPR 2020: 12393-12402 - [c33]Jia-Qi Yang, De-Chuan Zhan, Xin-Chun Li:
Bottom-Up and Top-Down Graph Pooling. PAKDD (2) 2020: 568-579 - [c32]Xin-Chun Li, De-Chuan Zhan, Jia-Qi Yang, Yi Shi, Cheng Hang, Yi Lu:
Towards Understanding Transfer Learning Algorithms Using Meta Transfer Features. PAKDD (2) 2020: 855-866 - [i6]Han-Jia Ye, Hong-You Chen, De-Chuan Zhan, Wei-Lun Chao:
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning. CoRR abs/2001.01385 (2020) - [i5]Wei-Lun Chao, Han-Jia Ye, De-Chuan Zhan, Mark E. Campbell, Kilian Q. Weinberger:
Revisiting Meta-Learning as Supervised Learning. CoRR abs/2002.00573 (2020) - [i4]Han-Jia Ye, Lu Han, De-Chuan Zhan:
Revisiting Unsupervised Meta-Learning: Amplifying or Compensating for the Characteristics of Few-Shot Tasks. CoRR abs/2011.14663 (2020) - [i3]Jia-Qi Yang, Xiang Li, Shuguang Han, Tao Zhuang, De-Chuan Zhan, Xiaoyi Zeng, Bin Tong:
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling. CoRR abs/2012.03245 (2020)
2010 – 2019
- 2019
- [j6]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang:
Fast generalization rates for distance metric learning. Mach. Learn. 108(2): 267-295 (2019) - [j5]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou:
What Makes Objects Similar: A Unified Multi-Metric Learning Approach. IEEE Trans. Pattern Anal. Mach. Intell. 41(5): 1257-1270 (2019) - [c31]Xiang-Rong Sheng, De-Chuan Zhan, Su Lu, Yuan Jiang:
Multi-View Anomaly Detection: Neighborhood in Locality Matters. AAAI 2019: 4894-4901 - [c30]Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang:
Deep Robust Unsupervised Multi-Modal Network. AAAI 2019: 5652-5659 - [c29]Zhao-Yang Fu, De-Chuan Zhan, Xin-Chun Li, Yi-Xing Lu:
Automatic Successive Reinforcement Learning with Multiple Auxiliary Rewards. IJCAI 2019: 2336-2342 - [c28]Yang Yang, Ke-Tao Wang, De-Chuan Zhan, Hui Xiong, Yuan Jiang:
Comprehensive Semi-Supervised Multi-Modal Learning. IJCAI 2019: 4092-4098 - [c27]Yang Yang, Da-Wei Zhou, De-Chuan Zhan, Hui Xiong, Yuan Jiang:
Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability. KDD 2019: 74-82 - [i2]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Learning Classifier Synthesis for Generalized Few-Shot Learning. CoRR abs/1906.02944 (2019) - 2018
- [c26]Yi-Feng Wu, De-Chuan Zhan, Yuan Jiang:
DMTMV: A Unified Learning Framework for Deep Multi-task Multi-view Learning. ICBK 2018: 49-56 - [c25]Xuan Huo, Yang Yang, Ming Li, De-Chuan Zhan:
Learning Semantic Features for Software Defect Prediction by Code Comments Embedding. ICDM 2018: 1049-1054 - [c24]Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou:
Rectify Heterogeneous Models with Semantic Mapping. ICML 2018: 1904-1913 - [c23]Yang Yang, De-Chuan Zhan, Xiang-Rong Sheng, Yuan Jiang:
Semi-Supervised Multi-Modal Learning with Incomplete Modalities. IJCAI 2018: 2998-3004 - [c22]Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan, Peng He:
Distance Metric Facilitated Transportation between Heterogeneous Domains. IJCAI 2018: 3012-3018 - [c21]Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang:
Complex Object Classification: A Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. KDD 2018: 2594-2603 - [c20]Yang Yang, De-Chuan Zhan, Yi-Feng Wu, Yuan Jiang:
Multi-network User Identification via Graph-Aware Embedding. PAKDD (2) 2018: 209-221 - [c19]Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Yuan Jiang:
Deep Multi-modal Learning with Cascade Consensus. PRICAI 2018: 64-72 - [i1]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Learning Embedding Adaptation for Few-Shot Learning. CoRR abs/1812.03664 (2018) - 2017
- [c18]Yang Yang, De-Chuan Zhan, Ying Fan, Yuan Jiang, Zhi-Hua Zhou:
Deep Learning for Fixed Model Reuse. AAAI 2017: 2831-2837 - [c17]Yang Yang, De-Chuan Zhan, Ying Fan, Yuan Jiang:
Instance Specific Discriminative Modal Pursuit: A Serialized Approach. ACML 2017: 65-80 - [c16]Yang Yang, De-Chuan Zhan, Xiang-Yu Guo, Yuan Jiang:
Modal Consistency based Pre-Trained Multi-Model Reuse. IJCAI 2017: 3287-3293 - [c15]Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang:
Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps. IJCAI 2017: 3315-3321 - 2016
- [c14]