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James T. Kwok
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- affiliation: Hong Kong University of Science and Technology
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Journal Articles
- 2024
- [j81]Yifan Wang, Jie Gui, Yuan Yan Tang, James Tin-Yau Kwok:
CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition. IEEE Trans. Inf. Forensics Secur. 19: 7810-7823 (2024) - [j80]Jie Gui
, Xiaofeng Cong
, Lei He
, Yuan Yan Tang, James Tin-Yau Kwok
:
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding. IEEE Trans. Multim. 26: 4819-4830 (2024) - [j79]Fan Feng
, Lu Hou
, Qi She
, Rosa H. M. Chan
, James T. Kwok
:
Power Law in Deep Neural Networks: Sparse Network Generation and Continual Learning With Preferential Attachment. IEEE Trans. Neural Networks Learn. Syst. 35(7): 8999-9013 (2024) - 2023
- [j78]Yongqi Zhang
, Quanming Yao
, James T. Kwok
:
Bilinear Scoring Function Search for Knowledge Graph Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1458-1473 (2023) - [j77]Hui Zhang, Quanming Yao
, James T. Kwok
, Xiang Bai
:
Searching a High Performance Feature Extractor for Text Recognition Network. IEEE Trans. Pattern Anal. Mach. Intell. 45(5): 6231-6246 (2023) - [j76]Huapeng Wu
, Jie Gui
, Jun Zhang
, James T. Kwok
, Zhihui Wei
:
Feedback Pyramid Attention Networks for Single Image Super-Resolution. IEEE Trans. Circuits Syst. Video Technol. 33(9): 4881-4892 (2023) - [j75]Jidong Ge
, Yuxiang Liu, Jie Gui
, Lanting Fang, Ming Lin, James Tin-Yau Kwok
, Liguo Huang
, Bin Luo:
Learning the Relation Between Similarity Loss and Clustering Loss in Self-Supervised Learning. IEEE Trans. Image Process. 32: 3442-3454 (2023) - [j74]Biwei Cao
, Jiuxin Cao
, Jie Gui
, Jiayun Shen, Bo Liu
, Lei He, Yuan Yan Tang, James Tin-Yau Kwok
:
AlignVE: Visual Entailment Recognition Based on Alignment Relations. IEEE Trans. Multim. 25: 7378-7387 (2023) - 2022
- [j73]Huapeng Wu
, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei:
Pyramidal dense attention networks for single image super-resolution. IET Image Process. 16(12): 3247-3257 (2022) - [j72]Kangshun Li, Dunmin Chen, Zhaolian Zeng, Guang Chen, James Tin-Yau Kwok:
New transformation method in continuous particle swarm optimisation for feature selection. Int. J. Wirel. Mob. Comput. 22(2): 114-124 (2022) - [j71]Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok:
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. J. Mach. Learn. Res. 23: 136:1-136:60 (2022) - [j70]Quanming Yao
, Hansi Yang, En-Liang Hu
, James T. Kwok
:
Efficient Low-Rank Semidefinite Programming With Robust Loss Functions. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 6153-6168 (2022) - 2021
- [j69]Yaqing Wang
, Quanming Yao
, James T. Kwok, Lionel M. Ni:
Generalizing from a Few Examples: A Survey on Few-shot Learning. ACM Comput. Surv. 53(3): 63:1-63:34 (2021) - [j68]Huan Zhao
, Quanming Yao
, Yangqiu Song, James T. Kwok, Dik Lun Lee:
Side Information Fusion for Recommender Systems over Heterogeneous Information Network. ACM Trans. Knowl. Discov. Data 15(4): 60:1-60:32 (2021) - [j67]Yuan Cao
, Heng Qi
, Jie Gui
, Keqiu Li, Yuan Yan Tang, James Tin-Yau Kwok
:
Learning to Hash With Dimension Analysis Based Quantizer for Image Retrieval. IEEE Trans. Multim. 23: 3907-3918 (2021) - [j66]Yuefeng Ma
, Xun Liang
, Gang Sheng, James T. Kwok
, Maoli Wang
, Guangshun Li
:
Noniterative Sparse LS-SVM Based on Globally Representative Point Selection. IEEE Trans. Neural Networks Learn. Syst. 32(2): 788-798 (2021) - 2020
- [j65]Yaqing Wang
, James T. Kwok
, Lionel M. Ni
:
Generalized Convolutional Sparse Coding With Unknown Noise. IEEE Trans. Image Process. 29: 5386-5395 (2020) - 2019
- [j64]Quanming Yao
, James T. Kwok, Taifeng Wang, Tie-Yan Liu:
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers. IEEE Trans. Pattern Anal. Mach. Intell. 41(11): 2628-2643 (2019) - [j63]Quanming Yao
, James T. Kwok:
Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion. IEEE Trans. Knowl. Data Eng. 31(9): 1665-1679 (2019) - [j62]En-Liang Hu
, James T. Kwok:
Low-Rank Matrix Learning Using Biconvex Surrogate Minimization. IEEE Trans. Neural Networks Learn. Syst. 30(11): 3517-3527 (2019) - 2018
- [j61]Elham J. Barezi, James T. Kwok, Hamid R. Rabiee:
Corrigendum to "Multi-label learning in the independent label sub-spaces" [Pattern Recognition Letters 97(2017) 8-12]. Pattern Recognit. Lett. 112: 152 (2018) - [j60]Yaqing Wang
, Quanming Yao
, James T. Kwok
, Lionel M. Ni
:
Scalable Online Convolutional Sparse Coding. IEEE Trans. Image Process. 27(10): 4850-4859 (2018) - [j59]Yue Zhu
, James T. Kwok, Zhi-Hua Zhou:
Multi-Label Learning with Global and Local Label Correlation. IEEE Trans. Knowl. Data Eng. 30(6): 1081-1094 (2018) - [j58]Yuefeng Ma
, Xun Liang
, James T. Kwok, Jianping Li, Xiaoping Zhou, Haiyan Zhang:
Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set. IEEE Trans. Neural Networks Learn. Syst. 29(4): 1120-1131 (2018) - 2017
- [j57]Quanming Yao, James T. Kwok:
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. J. Mach. Learn. Res. 18: 179:1-179:52 (2017) - [j56]Elham J. Barezi, James T. Kwok, Hamid R. Rabiee:
Multi-Label learning in the independent label sub-spaces. Pattern Recognit. Lett. 97: 8-12 (2017) - [j55]Wenwu He, James Tin-Yau Kwok, Ji Zhu, Yang Liu:
A Note on the Unification of Adaptive Online Learning. IEEE Trans. Neural Networks Learn. Syst. 28(5): 1178-1191 (2017) - 2016
- [j54]Jinho Kim, James T. Kwok, Kazutoshi Sumiya, Byoung-Tak Zhang:
Special issue: First International Conference on Big Data and Smart Computing (BigComp2014). Data Knowl. Eng. 104: 15-16 (2016) - 2015
- [j53]Wei Bi, James T. Kwok:
Bayes-Optimal Hierarchical Multilabel Classification. IEEE Trans. Knowl. Data Eng. 27(11): 2907-2918 (2015) - [j52]Mu Li, Wei Bi, James T. Kwok, Bao-Liang Lu
:
Large-Scale Nyström Kernel Matrix Approximation Using Randomized SVD. IEEE Trans. Neural Networks Learn. Syst. 26(1): 152-164 (2015) - [j51]Kai Zhang, Liang Lan
, James T. Kwok, Slobodan Vucetic, Bahram Parvin:
Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines. IEEE Trans. Neural Networks Learn. Syst. 26(3): 444-457 (2015) - [j50]En-Liang Hu, James T. Kwok:
Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent. IEEE Trans. Neural Networks Learn. Syst. 26(9): 1927-1938 (2015) - 2014
- [j49]James T. Kwok, Liqing Zhang, Hongtao Lu:
Selected papers from the 2011 International Conference on Neural Information Processing (ICONIP 2011). Neurocomputing 129: 1-2 (2014) - [j48]Wenwu He, James T. Kwok:
Simple randomized algorithms for online learning with kernels. Neural Networks 60: 17-24 (2014) - [j47]Wei Bi, James T. Kwok:
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification. IEEE Trans. Neural Networks Learn. Syst. 25(12): 2275-2287 (2014) - 2013
- [j46]Yufeng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou:
Convex and scalable weakly labeled SVMs. J. Mach. Learn. Res. 14(1): 2151-2188 (2013) - 2012
- [j45]Liqing Zhang, James Tin-Yau Kwok, Changshui Zhang:
A brief introduction to the special issue for ISNN2010. Neurocomputing 76(1): 1 (2012) - [j44]Jianhua Zhao
, Philip L. H. Yu, James T. Kwok:
Bilinear Probabilistic Principal Component Analysis. IEEE Trans. Neural Networks Learn. Syst. 23(3): 492-503 (2012) - [j43]Leon Wenliang Zhong, James T. Kwok:
Efficient Sparse Modeling With Automatic Feature Grouping. IEEE Trans. Neural Networks Learn. Syst. 23(9): 1436-1447 (2012) - 2011
- [j42]Wen-Yun Yang, Bao-Liang Lu
, James T. Kwok:
Incorporating cellular sorting structure for better prediction of protein subcellular locations. J. Exp. Theor. Artif. Intell. 23(1): 79-95 (2011) - [j41]Sinno Jialin Pan
, Ivor W. Tsang
, James T. Kwok, Qiang Yang:
Domain Adaptation via Transfer Component Analysis. IEEE Trans. Neural Networks 22(2): 199-210 (2011) - [j40]Shutao Li, Mingkui Tan, Ivor W. Tsang
, James Tin-Yau Kwok:
A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions. IEEE Trans. Syst. Man Cybern. Part B 41(4): 1003-1014 (2011) - 2010
- [j39]Ming Zhao, Shutao Li, James Tin-Yau Kwok:
Text detection in images using sparse representation with discriminative dictionaries. Image Vis. Comput. 28(12): 1590-1599 (2010) - [j38]Yan-xia Jin, Kai Zhang, James T. Kwok, Han-chang Zhou:
Fast and accurate kernel density approximation using a divide-and-conquer approach. J. Zhejiang Univ. Sci. C 11(9): 677-689 (2010) - [j37]Kai Zhang, James T. Kwok:
Simplifying mixture models through function approximation. IEEE Trans. Neural Networks 21(4): 644-658 (2010) - [j36]Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. Tsang
:
Incorporating the loss function into discriminative clustering of structured outputs. IEEE Trans. Neural Networks 21(10): 1564-1575 (2010) - [j35]Kai Zhang, James T. Kwok:
Clustered Nyström method for large scale manifold learning and dimension reduction. IEEE Trans. Neural Networks 21(10): 1576-1587 (2010) - 2009
- [j34]Kai Zhang, James T. Kwok:
Density-Weighted Nyström Method for Computing Large Kernel Eigensystems. Neural Comput. 21(1): 121-146 (2009) - [j33]Brian Kan-Wing Mak
, Tsz-Chung Lai, Ivor W. Tsang
, James Tin-Yau Kwok:
Maximum Penalized Likelihood Kernel Regression for Fast Adaptation. IEEE Trans. Speech Audio Process. 17(7): 1372-1381 (2009) - [j32]Kai Zhang, Ivor W. Tsang
, James T. Kwok:
Maximum Margin Clustering Made Practical. IEEE Trans. Neural Networks 20(4): 583-596 (2009) - [j31]Mingqing Hu, Yiqiang Chen, James Tin-Yau Kwok:
Building Sparse Multiple-Kernel SVM Classifiers. IEEE Trans. Neural Networks 20(5): 827-839 (2009) - 2008
- [j30]Ivor Wai-Hung Tsang
, András Kocsor, James Tin-Yau Kwok:
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines. IEEE Trans. Neural Networks 19(4): 610-624 (2008) - [j29]Xianchao Xie, Shuicheng Yan
, James T. Kwok, Thomas S. Huang:
Matrix-Variate Factor Analysis and Its Applications. IEEE Trans. Neural Networks 19(10): 1821-1826 (2008) - 2007
- [j28]Patrick C. K. Hung, Dickson K. W. Chiu
, W. W. Fung, William K. Cheung, Raymond K. Wong, Samuel P. M. Choi, Eleanna Kafeza
, James T. Kwok, Joshua C. C. Pun, Vivying S. Y. Cheng:
End-to-end privacy control in service outsourcing of human intensive processes: A multi-layered Web service integration approach. Inf. Syst. Frontiers 9(1): 85-101 (2007) - [j27]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Surrogate maximization/minimization algorithms and extensions. Mach. Learn. 69(1): 1-33 (2007) - [j26]Jooyoung Park, Daesung Kang, Jongho Kim, James T. Kwok, Ivor W. Tsang
:
SVDD-Based Pattern Denoising. Neural Comput. 19(7): 1919-1938 (2007) - [j25]Fei Wang, Jingdong Wang
, Changshui Zhang, James T. Kwok:
Face recognition using spectral features. Pattern Recognit. 40(10): 2786-2797 (2007) - [j24]James T. Kwok, Ivor Wai-Hung Tsang
, Jacek M. Zurada:
A Class of Single-Class Minimax Probability Machines for Novelty Detection. IEEE Trans. Neural Networks 18(3): 778-785 (2007) - 2006
- [j23]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Model-based transductive learning of the kernel matrix. Mach. Learn. 63(1): 69-101 (2006) - [j22]Brian Kan-Wing Mak
, Roger Wend-Huu Hsiao, Simon Ka-Lung Ho, James T. Kwok:
Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting. IEEE Trans. Speech Audio Process. 14(4): 1267-1280 (2006) - [j21]Jeffrey Junfeng Pan, James T. Kwok, Qiang Yang, Yiqiang Chen:
Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing. IEEE Trans. Knowl. Data Eng. 18(9): 1181-1193 (2006) - [j20]Ivor Wai-Hung Tsang
, James Tin-Yau Kwok:
Efficient hyperkernel learning using second-order cone programming. IEEE Trans. Neural Networks 17(1): 48-58 (2006) - [j19]Ivor Wai-Hung Tsang
, James Tin-Yau Kwok, Jacek M. Zurada:
Generalized Core Vector Machines. IEEE Trans. Neural Networks 17(5): 1126-1140 (2006) - [j18]Haitao Zhao, Pong Chi Yuen, James T. Kwok:
A novel incremental principal component analysis and its application for face recognition. IEEE Trans. Syst. Man Cybern. Part B 36(4): 873-886 (2006) - 2005
- [j17]Ivor W. Tsang
, James T. Kwok, Pak-Ming Cheung:
Core Vector Machines: Fast SVM Training on Very Large Data Sets. J. Mach. Learn. Res. 6: 363-392 (2005) - [j16]Brian Mak
, James Tin-Yau Kwok, Simon Ka-Lung Ho:
Kernel Eigenvoice Speaker Adaptation. IEEE Trans. Speech Audio Process. 13(5-2): 984-992 (2005) - 2004
- [j15]Victor Cheng, Chun Hung Li, James T. Kwok, Chi-Kwong Li:
Dissimilarity learning for nominal data. Pattern Recognit. 37(7): 1471-1477 (2004) - [j14]James Tin-Yau Kwok, Ivor Wai-Hung Tsang
:
The pre-image problem in kernel methods. IEEE Trans. Neural Networks 15(6): 1517-1525 (2004) - [j13]Shutao Li, James Tin-Yau Kwok, Ivor Wai-Hung Tsang
, Yaonan Wang:
Fusing images with different focuses using support vector machines. IEEE Trans. Neural Networks 15(6): 1555-1561 (2004) - 2003
- [j12]Kwok-Wai Cheung
, James T. Kwok, Martin H. C. Law, Kwok Ching Tsui:
Mining customer product ratings for personalized marketing. Decis. Support Syst. 35(2): 231-243 (2003) - [j11]Shutao Li, James T. Kwok, Hailong Zhu, Yaonan Wang:
Texture classification using the support vector machines. Pattern Recognit. 36(12): 2883-2893 (2003) - [j10]James T. Kwok, Ivor W. Tsang
:
Linear dependency between ε and the input noise in ε-support vector regression. IEEE Trans. Neural Networks 14(3): 544-553 (2003) - 2002
- [j9]Shutao Li, James T. Kwok, Yaonan Wang:
Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Inf. Fusion 3(1): 17-23 (2002) - [j8]Shutao Li, James T. Kwok, Yaonan Wang:
Multifocus image fusion using artificial neural networks. Pattern Recognit. Lett. 23(8): 985-997 (2002) - 2001
- [j7]Shutao Li, James T. Kwok, Yaonan Wang:
Combination of images with diverse focuses using the spatial frequency. Inf. Fusion 2(3): 169-176 (2001) - 2000
- [j6]James Tin-Yau Kwok:
The evidence framework applied to support vector machines. IEEE Trans. Neural Networks Learn. Syst. 11(5): 1162-1173 (2000) - 1999
- [j5]James Tin-Yau Kwok:
Moderating the outputs of support vector machine classifiers. IEEE Trans. Neural Networks 10(5): 1018-1031 (1999) - 1997
- [j4]James Tin-Yau Kwok, Dit-Yan Yeung:
Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans. Neural Networks 8(3): 630-645 (1997) - [j3]James Tin-Yau Kwok, Dit-Yan Yeung:
Objective functions for training new hidden units in constructive neural networks. IEEE Trans. Neural Networks 8(5): 1131-1148 (1997) - 1996
- [j2]James Tin-Yau Kwok, Dit-Yan Yeung:
Use of bias term in projection pursuit learning improves approximation and convergence properties. IEEE Trans. Neural Networks 7(5): 1168-1183 (1996) - 1995
- [j1]James Tin-Yau Kwok, Dit-Yan Yeung:
Improving the approximation and convergence capabilities of projection pursuit learning. Neural Process. Lett. 2(3): 20-25 (1995)
Conference and Workshop Papers
- 2024
- [c155]Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, James T. Kwok:
Forward-Backward Reasoning in Large Language Models for Mathematical Verification. ACL (Findings) 2024: 6647-6661 - [c154]Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Zhongdao Wang, James T. Kwok, Ping Luo, Huchuan Lu, Zhenguo Li:
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis. ICLR 2024 - [c153]Lifeng Shen, Weiyu Chen, James T. Kwok:
Multi-Resolution Diffusion Models for Time Series Forecasting. ICLR 2024 - [c152]Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu:
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models. ICLR 2024 - [c151]Weiyu Chen, James T. Kwok:
Efficient Pareto Manifold Learning with Low-Rank Structure. ICML 2024 - [c150]Runsheng Yu, Youzhi Zhang, James T. Kwok:
Improving Sharpness-Aware Minimization by Lookahead. ICML 2024 - 2023
- [c149]Hansi Yang
, Yongqi Zhang
, Quanming Yao
, James T. Kwok
:
Positive-Unlabeled Node Classification with Structure-aware Graph Learning. CIKM 2023: 4390-4394 - [c148]Yunhao Gou, Tom Ko, Hansi Yang, James T. Kwok, Yu Zhang, Mingxuan Wang:
Leveraging per Image-Token Consistency for Vision-Language Pre-training. CVPR 2023: 19155-19164 - [c147]Yanbin Wei, Qiushi Huang, Yu Zhang, James T. Kwok:
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. EMNLP (Findings) 2023: 8667-8683 - [c146]Yuhui Guo, Xun Liang, James T. Kwok, Xiangping Zheng, Bo Wu, Yuefeng Ma:
Cross-Modal Matching and Adaptive Graph Attention Network for RGB-D Scene Recognition. ICASSP 2023: 1-5 - [c145]Xinchi Deng, Han Shi, Runhui Huang, Changlin Li, Hang Xu, Jianhua Han, James T. Kwok, Shen Zhao, Wei Zhang, Xiaodan Liang:
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training. ICCV 2023: 22121-22132 - [c144]Weisen Jiang, Hansi Yang, Yu Zhang, James T. Kwok:
An Adaptive Policy to Employ Sharpness-Aware Minimization. ICLR 2023 - [c143]Zhili Liu, Kai Chen, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, James T. Kwok:
Task-customized Masked Autoencoder via Mixture of Cluster-conditional Experts. ICLR 2023 - [c142]Runsheng Yu, Weiyu Chen, Xinrun Wang, James T. Kwok:
Enhancing Meta Learning via Multi-Objective Soft Improvement Functions. ICLR 2023 - [c141]Weisen Jiang, Yu Zhang, James T. Kwok:
Effective Structured Prompting by Meta-Learning and Representative Verbalizer. ICML 2023: 15186-15199 - [c140]Lifeng Shen, James T. Kwok:
Non-autoregressive Conditional Diffusion Models for Time Series Prediction. ICML 2023: 31016-31029 - [c139]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang
, James T. Kwok:
Nonparametric Iterative Machine Teaching. ICML 2023: 40851-40870 - [c138]Zhenqian Shen, Hansi Yang, Yong Li, James T. Kwok, Quanming Yao:
Efficient Hyper-parameter Optimization with Cubic Regularization. NeurIPS 2023 - [c137]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok:
Nonparametric Teaching for Multiple Learners. NeurIPS 2023 - 2022
- [c136]Sen Li, Fuyu Lv, Taiwei Jin, Guiyang Li, Yukun Zheng, Tao Zhuang, Qingwen Liu, Xiaoyi Zeng, James T. Kwok, Qianli Ma:
Query Rewriting in TaoBao Search. CIKM 2022: 3262-3271 - [c135]Han Shi, Jiahui Gao, Hang Xu, Xiaodan Liang, Zhenguo Li, Lingpeng Kong, Stephen M. S. Lee, James T. Kwok:
Revisiting Over-smoothing in BERT from the Perspective of Graph. ICLR 2022 - [c134]Weisen Jiang, James T. Kwok, Yu Zhang:
Subspace Learning for Effective Meta-Learning. ICML 2022: 10177-10194 - [c133]Hansi Yang, James T. Kwok:
Efficient Variance Reduction for Meta-learning. ICML 2022: 25070-25095 - 2021
- [c132]Lifeng Shen, Zhongzhong Yu, Qianli Ma, James T. Kwok:
Time Series Anomaly Detection with Multiresolution Ensemble Decoding. AAAI 2021: 9567-9575 - [c131]Han Shi, Jiahui Gao, Xiaozhe Ren, Hang Xu, Xiaodan Liang, Zhenguo Li, James Tin-Yau Kwok:
SparseBERT: Rethinking the Importance Analysis in Self-attention. ICML 2021: 9547-9557 - [c130]Weisen Jiang, Yu Zhang, James T. Kwok:
SEEN: Few-Shot Classification with SElf-ENsemble. IJCNN 2021: 1-8 - [c129]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. NeurIPS 2021: 20970-20982 - [c128]Weisen Jiang, James T. Kwok, Yu Zhang:
Effective Meta-Regularization by Kernelized Proximal Regularization. NeurIPS 2021: 26212-26222 - [c127]Zac Wellmer, James T. Kwok:
Dropout's Dream Land: Generalization from Learned Simulators to Reality. ECML/PKDD (1) 2021: 255-270 - [c126]Yaqing Wang
, Quanming Yao, James T. Kwok:
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning. WWW 2021: 1798-1808 - 2020
- [c125]Han Shi, Haozheng Fan, James T. Kwok:
Effective Decoding in Graph Auto-Encoder Using Triadic Closure. AAAI 2020: 906-913 - [c124]Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok:
Searching to Exploit Memorization Effect in Learning with Noisy Labels. ICML 2020: 10789-10798 - [c123]Rishabh Mehrotra, Ben Carterette, Yong Li, Quanming Yao, Chen Gao, James T. Kwok, Qiang Yang, Isabelle Guyon:
Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys. KDD 2020: 3533-3534 - [c122]Lifeng Shen, Zhuocong Li, James T. Kwok:
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network. NeurIPS 2020 - [c121]Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang:
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS. NeurIPS 2020 - [c120]Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li, Cho-Jui Hsieh:
Efficient Neural Interaction Function Search for Collaborative Filtering. WWW 2020: 1660-1670 - 2019
- [c119]Lu Hou, Ruiliang Zhang, James T. Kwok:
Analysis of Quantized Models. ICLR (Poster) 2019 - [c118]Quanming Yao, James Tin-Yau Kwok, Bo Han:
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. ICML 2019: 7035-7044 - [c117]Quanming Yao
, Xiawei Guo, James T. Kwok, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang:
Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. IJCAI 2019: 4114-4120 - [c116]Minsam Kim, James T. Kwok:
Dynamic Unit Surgery for Deep Neural Network Compression and Acceleration. IJCNN 2019: 1-8 - [c115]Lu Hou, Jinhua Zhu, James T. Kwok, Fei Gao, Tao Qin, Tie-Yan Liu:
Normalization Helps Training of Quantized LSTM. NeurIPS 2019: 7344-7354 - [c114]Shuai Zheng, Ziyue Huang, James T. Kwok:
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. NeurIPS 2019: 11446-11456 - [c113]Zac Wellmer, James T. Kwok:
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space. ECML/PKDD (3) 2019: 118-133 - 2018
- [c112]Lu Hou, James T. Kwok:
Loss-aware Weight Quantization of Deep Networks. ICLR (Poster) 2018 - [c111]Yaqing Wang, Quanming Yao, James Tin-Yau Kwok, Lionel M. Ni:
Online Convolutional Sparse Coding with Sample-Dependent Dictionary. ICML 2018: 5196-5205 - [c110]Shuai Zheng, James Tin-Yau Kwok:
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data. ICML 2018: 5927-5935 - [c109]Quanming Yao, James T. Kwok:
Scalable Robust Matrix Factorization with Nonconvex Loss. NeurIPS 2018: 5066-5075 - 2017
- [c108]Xiawei Guo, Quanming Yao, James Tin-Yau Kwok:
Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm. AAAI 2017: 1948-1954 - [c107]Huan Zhao, Quanming Yao
, James T. Kwok, Dik Lun Lee:
Collaborative Filtering with Social Local Models. ICDM 2017: 645-654 - [c106]Lu Hou, Quanming Yao, James T. Kwok:
Loss-aware Binarization of Deep Networks. ICLR (Poster) 2017 - [c105]Shuai Zheng, James T. Kwok:
Follow the Moving Leader in Deep Learning. ICML 2017: 4110-4119 - [c104]Quanming Yao
, James T. Kwok, Fei Gao, Wei Chen, Tie-Yan Liu:
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems. IJCAI 2017: 3308-3314 - [c103]Yaqing Wang
, James T. Kwok, Quanming Yao, Lionel M. Ni:
Zero-shot learning with a partial set of observed attributes. IJCNN 2017: 3777-3784 - 2016
- [c102]Lu Hou, James T. Kwok, Jacek M. Zurada:
Efficient Learning of Timeseries Shapelets. AAAI 2016: 1209-1215 - [c101]Yufeng Li, James T. Kwok, Zhi-Hua Zhou:
Towards Safe Semi-Supervised Learning for Multivariate Performance Measures. AAAI 2016: 1816-1822 - [c100]Ruiliang Zhang, Shuai Zheng, James T. Kwok:
Asynchronous Distributed Semi-Stochastic Gradient Optimization. AAAI 2016: 2323-2329 - [c99]Shuai Zheng, Ruiliang Zhang, James T. Kwok:
Fast Nonsmooth Regularized Risk Minimization with Continuation. AAAI 2016: 2393-2399 - [c98]Quanming Yao, James T. Kwok:
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. ICML 2016: 2645-2654 - [c97]Quanming Yao, James T. Kwok:
Greedy Learning of Generalized Low-Rank Models. IJCAI 2016: 2294-2300 - [c96]Shuai Zheng, James T. Kwok:
Fast-and-Light Stochastic ADMM. IJCAI 2016: 2407-2613 - [c95]Xiawei Guo, James T. Kwok:
Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering. ECML/PKDD (1) 2016: 426-442 - 2015
- [c94]Quanming Yao, James T. Kwok:
Colorization by Patch-Based Local Low-Rank Matrix Completion. AAAI 2015: 1959-1965 - [c93]Quanming Yao
, James T. Kwok, Wenliang Zhong:
Fast Low-Rank Matrix Learning with Nonconvex Regularization. ICDM 2015: 539-548 - [c92]Quanming Yao, James T. Kwok:
Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion. IJCAI 2015: 4002-4008 - [c91]Yihai Huang, James T. Kwok:
Collaborative filtering via co-factorization of individuals and groups. IJCNN 2015: 1-8 - [c90]Kai Fan, Ziteng Wang, Jeffrey M. Beck, James T. Kwok, Katherine A. Heller:
Fast Second Order Stochastic Backpropagation for Variational Inference. NIPS 2015: 1387-1395 - 2014
- [c89]Shuai Zheng, James T. Kwok:
Accurate Integration of Aerosol Predictions by Smoothing on a Manifold. AAAI 2014: 1376-1383 - [c88]Wei Bi, James T. Kwok:
Multilabel Classification with Label Correlations and Missing Labels. AAAI 2014: 1680-1686 - [c87]Wenliang Zhong, James T. Kwok:
Gradient Descent with Proximal Average for Nonconvex and Composite Regularization. AAAI 2014: 2206-2212 - [c86]Wenliang Zhong, James Tin-Yau Kwok:
Accelerated Stochastic Gradient Method for Composite Regularization. AISTATS 2014: 1086-1094 - [c85]Wenliang Zhong, James Tin-Yau Kwok:
Fast Stochastic Alternating Direction Method of Multipliers. ICML 2014: 46-54 - [c84]Ruiliang Zhang, James T. Kwok:
Asynchronous Distributed ADMM for Consensus Optimization. ICML 2014: 1701-1709 - [c83]Wei Bi, Liwei Wang, James T. Kwok, Zhuowen Tu:
Learning to Predict from Crowdsourced Data. UAI 2014: 82-91 - 2013
- [c82]James T. Kwok:
Learning from High-Dimensional Data in Multitask/Multilabel Classification. ACPR 2013: 16-17 - [c81]Leon Wenliang Zhong, James T. Kwok:
Efficient Learning for Models with DAG-Structured Parameter Constraints. ICDM 2013: 897-906 - [c80]Kai Zhang, Vincent Wenchen Zheng, Qiaojun Wang, James Tin-Yau Kwok, Qiang Yang, Ivan Marsic:
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels. ICML (3) 2013: 388-395 - [c79]Wei Bi, James Tin-Yau Kwok:
Efficient Multi-label Classification with Many Labels. ICML (3) 2013: 405-413 - [c78]En-Liang Hu, James T. Kwok:
Flexible Nonparametric Kernel Learning with Different Loss Functions. ICONIP (2) 2013: 116-123 - [c77]En-Liang Hu, James T. Kwok:
Efficient Kernel Learning from Side Information Using ADMM. IJCAI 2013: 1408-1414 - [c76]Wenliang Zhong, James T. Kwok:
Accurate Probability Calibration for Multiple Classifiers. IJCAI 2013: 1939-1945 - 2012
- [c75]Wei Bi, James T. Kwok:
Hierarchical Multilabel Classification with Minimum Bayes Risk. ICDM 2012: 101-110 - [c74]Wenliang Zhong, James Tin-Yau Kwok:
Convex Multitask Learning with Flexible Task Clusters. ICML 2012 - [c73]Wei Bi, James T. Kwok:
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification. NIPS 2012: 153-161 - 2011
- [c72]Mu Li, Xiao-Chen Lian, James T. Kwok, Bao-Liang Lu
:
Time and space efficient spectral clustering via column sampling. CVPR 2011: 2297-2304 - [c71]Wenliang Zhong, James T. Kwok:
Efficient Sparse Modeling with Automatic Feature Grouping. ICML 2011: 9-16 - [c70]Wei Bi, James T. Kwok:
MultiLabel Classification on Tree- and DAG-Structured Hierarchies. ICML 2011: 17-24 - [c69]Weike Pan, James T. Kwok:
Structured clustering with automatic kernel adaptation. IJCNN 2011: 1322-1327 - 2010
- [c68]Yufeng Li, James T. Kwok, Zhi-Hua Zhou:
Cost-Sensitive Semi-Supervised Support Vector Machine. AAAI 2010: 500-505 - [c67]Mu Li, James T. Kwok, Bao-Liang Lu
:
Online multiple instance learning with no regret. CVPR 2010: 1395-1401 - [c66]Mu Li, James T. Kwok, Bao-Liang Lu:
Making Large-Scale Nyström Approximation Possible. ICML 2010: 631-638 - [c65]Chonghai Hu, James T. Kwok:
Manifold regularization for structured outputs via the joint kernel. IJCNN 2010: 1-8 - [c64]Wen-Yun Yang, James T. Kwok, Bao-Liang Lu:
Spectral and Semidefinite Relaxation of the CLUHSIC Algorithm. SDM 2010: 106-117 - 2009
- [c63]Bin Zhao, James Tin-Yau Kwok, Fei Wang, Changshui Zhang:
Unsupervised Maximum Margin Feature Selection with manifold regularization. CVPR 2009: 888-895 - [c62]Bin Zhao, James Tin-Yau Kwok, Changshui Zhang:
Maximum Margin Clustering with Multivariate Loss Function. ICDM 2009: 637-646 - [c61]Xi Chen, Weike Pan, James T. Kwok, Jaime G. Carbonell:
Accelerated Gradient Method for Multi-task Sparse Learning Problem. ICDM 2009: 746-751 - [c60]Yufeng Li, James T. Kwok, Zhi-Hua Zhou:
Semi-supervised learning using label mean. ICML 2009: 633-640 - [c59]Kai Zhang, James T. Kwok, Bahram Parvin:
Prototype vector machine for large scale semi-supervised learning. ICML 2009: 1233-1240 - [c58]Sinno Jialin Pan, Ivor W. Tsang
, James T. Kwok, Qiang Yang:
Domain Adaptation via Transfer Component Analysis. IJCAI 2009: 1187-1192 - [c57]Chonghai Hu, James T. Kwok, Weike Pan:
Accelerated Gradient Methods for Stochastic Optimization and Online Learning. NIPS 2009: 781-789 - [c56]Yufeng Li, James T. Kwok, Ivor W. Tsang
, Zhi-Hua Zhou:
A Convex Method for Locating Regions of Interest with Multi-instance Learning. ECML/PKDD (2) 2009: 15-30 - [c55]Bin Zhao, James T. Kwok, Changshui Zhang:
Multiple Kernel Clustering. SDM 2009: 638-649 - [c54]Yufeng Li, Ivor W. Tsang
, James Tin-Yau Kwok, Zhi-Hua Zhou:
Tighter and Convex Maximum Margin Clustering. AISTATS 2009: 344-351 - 2008
- [c53]Sinno Jialin Pan, James T. Kwok, Qiang Yang:
Transfer Learning via Dimensionality Reduction. AAAI 2008: 677-682 - [c52]Sinno Jialin Pan, Dou Shen, Qiang Yang, James T. Kwok:
Transferring Localization Models across Space. AAAI 2008: 1383-1388 - [c51]Kai Zhang, Ivor W. Tsang
, James T. Kwok:
Improved Nyström low-rank approximation and error analysis. ICML 2008: 1232-1239 - 2007
- [c50]Sinno Jialin Pan, James T. Kwok, Qiang Yang, Jeffrey Junfeng Pan:
Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning. AAAI 2007: 1108-1113 - [c49]Ivor W. Tsang
, András Kocsor, James T. Kwok:
Simpler core vector machines with enclosing balls. ICML 2007: 911-918 - [c48]Kai Zhang, Ivor W. Tsang
, James T. Kwok:
Maximum margin clustering made practical. ICML 2007: 1119-1126 - [c47]James T. Kwok, Pak-Ming Cheung:
Marginalized Multi-Instance Kernels. IJCAI 2007: 901-906 - [c46]Ivor W. Tsang
, James T. Kwok:
Ensembles of Partially Trained SVMs with Multiplicative Updates. IJCAI 2007: 1089-1094 - 2006
- [c45]Kai Zhang, James T. Kwok, Ming Tang:
Accelerated Convergence Using Dynamic Mean Shift. ECCV (2) 2006: 257-268 - [c44]Ivor W. Tsang, András Kocsor, James T. Kwok:
Diversified SVM Ensembles for Large Data Sets. ECML 2006: 792-800 - [c43]Ivor W. Tsang
, James T. Kwok, Brian Mak, Kai Zhang, Jeffrey Junfeng Pan:
Fast Speaker Adaption Via Maximum Penalized Likelihood Kernel Regression. ICASSP (1) 2006: 997-1000 - [c42]Jooyoung Park, Daesung Kang, James T. Kwok, Sang-Woong Lee, Bon-Woo Hwang, Seong-Whan Lee:
Facial Image Reconstruction by SVDD-Based Pattern De-noising. ICB 2006: 129-135 - [c41]Pak-Ming Cheung, James T. Kwok:
A regularization framework for multiple-instance learning. ICML 2006: 193-200 - [c40]Juan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwok:
Locally adaptive classification piloted by uncertainty. ICML 2006: 225-232 - [c39]Kai Zhang, James T. Kwok:
Block-quantized kernel matrix for fast spectral embedding. ICML 2006: 1097-1104 - [c38]Shutao Li, Chen Liao, James T. Kwok:
Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares. ICONIP (3) 2006: 11-20 - [c37]Shutao Li, Jinglin Peng, James T. Kwok, Jing Zhang:
Multimodal Registration using the Discrete Wavelet Frame Transform. ICPR (3) 2006: 877-880 - [c36]Ivor W. Tsang
, James T. Kwok, Shutao Li:
Learning the Kernel in Mahalanobis One-Class Support Vector Machines. IJCNN 2006: 1169-1175 - [c35]Ken Chen, Bao-Liang Lu
, James T. Kwok:
Efficient Classification of Multi-label and Imbalanced Data using Min-Max Modular Classifiers. IJCNN 2006: 1770-1775 - [c34]Shutao Li, Chen Liao, James T. Kwok:
Wavelet-Based Feature Extraction for Microarray Data Classification. IJCNN 2006: 5028-5033 - [c33]Ivor W. Tsang
, András Kocsor, James T. Kwok:
Efficient kernel feature extraction for massive data sets. KDD 2006: 724-729 - [c32]Ivor W. Tsang
, James T. Kwok:
Large-Scale Sparsified Manifold Regularization. NIPS 2006: 1401-1408 - [c31]Kai Zhang, James T. Kwok:
Simplifying Mixture Models through Function Approximation. NIPS 2006: 1577-1584 - 2005
- [c30]Patrick C. K. Hung, Dickson K. W. Chiu, W. W. Fung, William K. Cheung, Raymond K. Wong, Samuel P. M. Choi, Eleanna Kafeza
, James T. Kwok, Joshua C. C. Pun, Vivying S. Y. Cheng:
Towards end-to-end privacy control in the outsourcing of marketing activities: a web service integration solution. ICEC 2005: 454-461 - [c29]Ivor W. Tsang
, James Tin-Yau Kwok, Pak-Ming Cheung:
Very Large SVM Training using Core Vector Machines. AISTATS 2005: 349-356 - [c28]Kai Zhang, Ming Tang, James T. Kwok:
Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation. CVPR (2) 2005: 1001-1007 - [c27]Kin Fung Simon Wong, Ivor W. Tsang
, Victor Cheung, S.-H. Gary Chan
, James T. Kwok:
Position estimation for wireless sensor networks. GLOBECOM 2005: 5 - [c26]Ivor W. Tsang
, James T. Kwok, Kimo T. Lai:
Core Vector Regression for very large regression problems. ICML 2005: 912-919 - [c25]Jeffrey Junfeng Pan, James T. Kwok, Qiang Yang, Yiqiang Chen:
Accurate and Low-cost Location Estimation Using Kernels. IJCAI 2005: 1366-1371 - 2004
- [c24]Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan Yeung:
Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo. AAAI 2004: 372-377 - [c23]Ivor W. Tsang, James T. Kwok:
Efficient Hyperkernel Learning Using Second-Order Cone Programming. ECML 2004: 453-464 - [c22]Haitao Zhao, Pong Chi Yuen, James T. Kwok, Jingyu Yang:
Incremental PCA based face recognition. ICARCV 2004: 687-691 - [c21]Brian Mak, James T. Kwok, Simon Ka-Lung Ho:
A study of various composite kernels for kernel eigenvoice speaker adaptation. ICASSP (1) 2004: 325-328 - [c20]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model. ICML 2004 - [c19]Zhihua Zhang, Dit-Yan Yeung, James T. Kwok:
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm. ICML 2004 - [c18]Brian Mak, Simon Ka-Lung Ho, James T. Kwok:
Speedup of kernel eigenvoice speaker adaptation by embedded kernel PCA. INTERSPEECH 2004: 2913-2916 - 2003
- [c17]James T. Kwok, Ivor W. Tsang
:
Learning with Idealized Kernels. ICML 2003: 400-407 - [c16]James T. Kwok, Ivor W. Tsang
:
The Pre-Image Problem in Kernel Methods. ICML 2003: 408-415 - [c15]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Parametric Distance Metric Learning with Label Information. IJCAI 2003: 1450- - [c14]James T. Kwok, Brian Mak, Simon Ka-Lung Ho:
Eigenvoice Speaker Adaptation via Composite Kernel PCA. NIPS 2003: 1401-1408 - 2002
- [c13]Shutao Li, James T. Kwok, Yaonan Wang:
Fusing Images with Multiple Focuses Using Support Vector Machines. ICANN 2002: 1287-1292 - [c12]Hailong Zhu, James T. Kwok, Liangsheng Qu:
Improving De-Noising by Coefficient De-Noising and Dyadic Wavelet Transform. ICPR (2) 2002: 273- - 2001
- [c11]Martin H. C. Law, James Tin-Yau Kwok:
Bayesian Support Vector Regression. AISTATS 2001: 162-167 - [c10]Martin H. C. Law, James T. Kwok:
Applying the Bayesian Evidence Framework to \nu -Support Vector Regression. ECML 2001: 312-323 - [c9]James T. Kwok:
Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression. ICANN 2001: 405-410 - 2000
- [c8]Martin H. C. Law, James T. Kwok:
Rival Penalized Competitive Learning for Model-Based Sequence Clustering. ICPR 2000: 2195-2198 - 1999
- [c7]James Tin-Yau Kwok:
Integrating the evidence framework and the support vector machine. ESANN 1999: 177-182 - [c6]James Tin-Yau Kwok:
Moderating the outputs of support vector machine classifiers. IJCNN 1999: 943-948 - 1998
- [c5]James Tin-Yau Kwok:
Automated Text Categorization Using Support Vector Machine. ICONIP 1998: 347-351 - [c4]James Tin-Yau Kwok:
Support vector mixture for classification and regression problems. ICPR 1998: 255-258 - 1996
- [c3]James Tin-Yau Kwok, Dit-Yan Yeung:
Bayesian Regularization in Constructive Neural Networks. ICANN 1996: 557-562 - 1995
- [c2]James Tin-Yau Kwok, Dit-Yan Yeung:
Efficient cross-validation for feedforward neural networks. ICNN 1995: 2789-2794 - 1993
- [c1]James Tin-Yau Kwok, Dit-Yan Yeung:
Experimental analysis of input weight freezing in constructive neural networks. ICNN 1993: 511-516
Parts in Books or Collections
- 2020
- [p2]Xiawei Guo, Quanming Yao
, James T. Kwok, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang:
Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. Federated Learning 2020: 269-283 - 2015
- [p1]James Tin-Yau Kwok, Zhi-Hua Zhou, Lei Xu:
Machine Learning. Handbook of Computational Intelligence 2015: 495-522
Editorship
- 2020
- [e12]Haiqin Yang
, Kitsuchart Pasupa
, Andrew Chi-Sing Leung
, James T. Kwok
, Jonathan H. Chan
, Irwin King
:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18-22, 2020, Proceedings, Part IV. Communications in Computer and Information Science 1332, Springer 2020, ISBN 978-3-030-63819-1 [contents] - [e11]Haiqin Yang
, Kitsuchart Pasupa
, Andrew Chi-Sing Leung
, James T. Kwok
, Jonathan H. Chan
, Irwin King
:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18-22, 2020, Proceedings, Part V. Communications in Computer and Information Science 1333, Springer 2020, ISBN 978-3-030-63822-1 [contents] - [e10]Haiqin Yang
, Kitsuchart Pasupa
, Andrew Chi-Sing Leung
, James T. Kwok
, Jonathan H. Chan
, Irwin King
:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part I. Lecture Notes in Computer Science 12532, Springer 2020, ISBN 978-3-030-63829-0 [contents] - [e9]Haiqin Yang
, Kitsuchart Pasupa
, Andrew Chi-Sing Leung
, James T. Kwok
, Jonathan H. Chan
, Irwin King
:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part II. Lecture Notes in Computer Science 12533, Springer 2020, ISBN 978-3-030-63832-0 [contents] - [e8]Haiqin Yang
, Kitsuchart Pasupa
, Andrew Chi-Sing Leung
, James T. Kwok
, Jonathan H. Chan
, Irwin King
:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part III. Lecture Notes in Computer Science 12534, Springer 2020, ISBN 978-3-030-63835-1 [contents] - 2011
- [e7]Bao-Liang Lu, Liqing Zhang, James T. Kwok:
Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part I. Lecture Notes in Computer Science 7062, Springer 2011, ISBN 978-3-642-24954-9 [contents] - [e6]Bao-Liang Lu, Liqing Zhang, James T. Kwok:
Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II. Lecture Notes in Computer Science 7063, Springer 2011, ISBN 978-3-642-24957-0 [contents] - [e5]Bao-Liang Lu, Liqing Zhang, James T. Kwok:
Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part III. Lecture Notes in Computer Science 7064, Springer 2011, ISBN 978-3-642-24964-8 [contents] - 2010
- [e4]Liqing Zhang, Bao-Liang Lu, James Tin-Yau Kwok:
Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I. Lecture Notes in Computer Science 6063, Springer 2010, ISBN 978-3-642-13277-3 [contents] - [e3]Liqing Zhang, Bao-Liang Lu, James Tin-Yau Kwok:
Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II. Lecture Notes in Computer Science 6064, Springer 2010, ISBN 978-3-642-13317-6 [contents] - 2008
- [e2]Niels da Vitoria Lobo, Takis Kasparis, Fabio Roli, James Tin-Yau Kwok, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Marco Loog:
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. Lecture Notes in Computer Science 5342, Springer 2008, ISBN 978-3-540-89688-3 [contents] - 2006
- [e1]Dit-Yan Yeung, James T. Kwok, Ana L. N. Fred, Fabio Roli, Dick de Ridder:
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006, Proceedings. Lecture Notes in Computer Science 4109, Springer 2006, ISBN 3-540-37236-9 [contents]
Informal and Other Publications
- 2024
- [i70]Yanbin Wei, Shuai Fu, Weisen Jiang, James T. Kwok, Yu Zhang:
Rendering Graphs for Graph Reasoning in Multimodal Large Language Models. CoRR abs/2402.02130 (2024) - [i69]Yanbin Wei, Qiushi Huang, James T. Kwok, Yu Zhang:
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. CoRR abs/2402.02389 (2024) - [i68]Zhili Liu, Kai Chen, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, James T. Kwok:
Task-customized Masked AutoEncoder via Mixture of Cluster-conditional Experts. CoRR abs/2402.05382 (2024) - [i67]Yunhao Gou, Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T. Kwok, Yu Zhang:
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation. CoRR abs/2403.09572 (2024) - [i66]Zhili Liu, Yunhao Gou, Kai Chen, Lanqing Hong, Jiahui Gao, Fei Mi, Yu Zhang, Zhenguo Li, Xin Jiang, Qun Liu, James T. Kwok:
Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment. CoRR abs/2405.00557 (2024) - [i65]Runsheng Yu, Yong Wang, Xiaoqi Jiao, Youzhi Zhang, James T. Kwok:
Direct Alignment of Language Models via Quality-Aware Self-Refinement. CoRR abs/2405.21040 (2024) - [i64]Lifeng Shen, Jincheng Yu, Hansi Yang, James T. Kwok:
Mixup Augmentation with Multiple Interpolations. CoRR abs/2406.01417 (2024) - [i63]Hansi Yang, James T. Kwok:
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning. CoRR abs/2406.13183 (2024) - [i62]Tao Li, Weisen Jiang, Fanghui Liu, Xiaolin Huang, James T. Kwok:
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy. CoRR abs/2407.03641 (2024) - [i61]Weiyu Chen, James T. Kwok:
Efficient Pareto Manifold Learning with Low-Rank Structure. CoRR abs/2407.20734 (2024) - 2023
- [i60]Jidong Ge, Yuxiang Liu, Jie Gui, Lanting Fang, Ming Lin, James Tin-Yau Kwok, LiGuo Huang
, Bin Luo:
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning. CoRR abs/2301.03041 (2023) - [i59]Jie Gui, Xiaofeng Cong, Chengwei Peng, Yuan Yan Tang, James Tin-Yau Kwok:
Adversarial Attack and Defense for Dehazing Networks. CoRR abs/2303.17255 (2023) - [i58]Biwei Cao, Lulu Hua, Jiuxin Cao, Jie Gui, Bo Liu
, James Tin-Yau Kwok:
No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation. CoRR abs/2303.18049 (2023) - [i57]Weisen Jiang, Hansi Yang, Yu Zhang, James T. Kwok:
An Adaptive Policy to Employ Sharpness-Aware Minimization. CoRR abs/2304.14647 (2023) - [i56]Qianli Ma, Zhen Liu
, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, James T. Kwok:
A Survey on Time-Series Pre-Trained Models. CoRR abs/2305.10716 (2023) - [i55]Weisen Jiang, Yu Zhang, James T. Kwok:
Effective Structured Prompting by Meta-Learning and Representative Verbalizer. CoRR abs/2306.00618 (2023) - [i54]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang
, James T. Kwok:
Nonparametric Iterative Machine Teaching. CoRR abs/2306.03007 (2023) - [i53]Lifeng Shen, James T. Kwok:
Non-autoregressive Conditional Diffusion Models for Time Series Prediction. CoRR abs/2306.05043 (2023) - [i52]Jie Gui, Xiaofeng Cong, Lei He, Yuan Yan Tang, James Tin-Yau Kwok:
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding. CoRR abs/2306.05675 (2023) - [i51]Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, James T. Kwok:
Forward-Backward Reasoning in Large Language Models for Verification. CoRR abs/2308.07758 (2023) - [i50]Xinchi Deng, Han Shi, Runhui Huang, Changlin Li, Hang Xu, Jianhua Han, James T. Kwok, Shen Zhao, Wei Zhang, Xiaodan Liang:
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training. CoRR abs/2308.11331 (2023) - [i49]Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu:
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models. CoRR abs/2309.12284 (2023) - [i48]Yulong Zhang, Shuhao Chen, Weisen Jiang, Yu Zhang, Jiangang Lu, James T. Kwok:
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation. CoRR abs/2309.14360 (2023) - [i47]Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James T. Kwok, Ping Luo, Huchuan Lu, Zhenguo Li:
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis. CoRR abs/2310.00426 (2023) - [i46]Weisen Jiang, Baijiong Lin, Han Shi, Yu Zhang, Zhenguo Li, James T. Kwok:
Effective and Parameter-Efficient Reusing Fine-Tuned Models. CoRR abs/2310.01886 (2023) - [i45]Zhili Liu, Kai Chen, Yifan Zhang, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T. Kwok:
Geom-Erasing: Geometry-Driven Removal of Implicit Concept in Diffusion Models. CoRR abs/2310.05873 (2023) - [i44]Hansi Yang, Yongqi Zhang, Quanming Yao, James T. Kwok:
Positive-Unlabeled Node Classification with Structure-aware Graph Learning. CoRR abs/2310.13538 (2023) - [i43]Mingwei Xu, Xiaofeng Cao, Ivor W. Tsang
, James T. Kwok:
Aggregation Weighting of Federated Learning via Generalization Bound Estimation. CoRR abs/2311.05936 (2023) - [i42]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang
, James T. Kwok:
Nonparametric Teaching for Multiple Learners. CoRR abs/2311.10318 (2023) - [i41]Yunhao Gou, Zhili Liu, Kai Chen, Lanqing Hong, Hang Xu, Aoxue Li, Dit-Yan Yeung, James T. Kwok, Yu Zhang:
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning. CoRR abs/2312.12379 (2023) - 2022
- [i40]Han Shi, Jiahui Gao, Hang Xu, Xiaodan Liang, Zhenguo Li, Lingpeng Kong, Stephen M. S. Lee, James T. Kwok:
Revisiting Over-smoothing in BERT from the Perspective of Graph. CoRR abs/2202.08625 (2022) - [i39]Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok:
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. CoRR abs/2205.03059 (2022) - [i38]Hui Zhang, Quanming Yao, James T. Kwok, Xiang Bai:
Searching a High-Performance Feature Extractor for Text Recognition Network. CoRR abs/2209.13139 (2022) - [i37]Biwei Cao, Jiuxin Cao, Jie Gui, Jiayun Shen, Bo Liu
, Lei He, Yuan Yan Tang, James Tin-Yau Kwok:
AlignVE: Visual Entailment Recognition Based on Alignment Relations. CoRR abs/2211.08736 (2022) - [i36]Yunhao Gou, Tom Ko, Hansi Yang, James T. Kwok, Yu Zhang, Mingxuan Wang:
Leveraging per Image-Token Consistency for Vision-Language Pre-training. CoRR abs/2211.15398 (2022) - 2021
- [i35]Hansi Yang, Quanming Yao, James T. Kwok:
Tensorizing Subgraph Search in the Supernet. CoRR abs/2101.01078 (2021) - [i34]Han Shi, Jiahui Gao, Xiaozhe Ren, Hang Xu, Xiaodan Liang, Zhenguo Li, James T. Kwok:
SparseBERT: Rethinking the Importance Analysis in Self-attention. CoRR abs/2102.12871 (2021) - [i33]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. CoRR abs/2106.06326 (2021) - [i32]Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei:
Feedback Pyramid Attention Networks for Single Image Super-Resolution. CoRR abs/2106.06966 (2021) - [i31]Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei:
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution. CoRR abs/2106.06996 (2021) - [i30]Zac Wellmer, James T. Kwok:
Dropout's Dream Land: Generalization from Learned Simulators to Reality. CoRR abs/2109.08342 (2021) - 2020
- [i29]Yaqing Wang, Quanming Yao, James T. Kwok:
Efficient Low-Rank Matrix Learning by Factorizable Nonconvex Regularization. CoRR abs/2008.06542 (2020) - [i28]Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama:
A Survey of Label-noise Representation Learning: Past, Present and Future. CoRR abs/2011.04406 (2020) - 2019
- [i27]Yaqing Wang
, James T. Kwok, Lionel M. Ni:
General Convolutional Sparse Coding with Unknown Noise. CoRR abs/1903.03253 (2019) - [i26]Shuai Zheng
, James T. Kwok:
Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning. CoRR abs/1905.09899 (2019) - [i25]Shuai Zheng
, Ziyue Huang, James T. Kwok:
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. CoRR abs/1905.10936 (2019) - [i24]Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li:
Searching for Interaction Functions in Collaborative Filtering. CoRR abs/1906.12091 (2019) - [i23]Zac Wellmer, James T. Kwok:
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space. CoRR abs/1909.07373 (2019) - [i22]Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang:
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization. CoRR abs/1911.09336 (2019) - [i21]Han Shi, Haozheng Fan, James T. Kwok:
Effective Decoding in Graph Auto-Encoder using Triadic Closure. CoRR abs/1911.11322 (2019) - 2018
- [i20]Huan Zhao, Quanming Yao, Yangqiu Song, James T. Kwok, Dik Lun Lee:
Learning with Heterogeneous Side Information Fusion for Recommender Systems. CoRR abs/1801.02411 (2018) - [i19]Lu Hou, James T. Kwok:
Loss-aware Weight Quantization of Deep Networks. CoRR abs/1802.08635 (2018) - [i18]Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni:
Online Convolutional Sparse Coding with Sample-Dependent Dictionary. CoRR abs/1804.10366 (2018) - [i17]Lu Hou, James T. Kwok:
Power Law in Sparsified Deep Neural Networks. CoRR abs/1805.01891 (2018) - [i16]Shuai Zheng, James T. Kwok:
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data. CoRR abs/1806.02927 (2018) - 2017
- [i15]Quanming Yao, James T. Kwok:
Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion. CoRR abs/1703.05487 (2017) - [i14]Yue Zhu, James T. Kwok, Zhi-Hua Zhou:
Multi-Label Learning with Global and Local Label Correlation. CoRR abs/1704.01415 (2017) - [i13]Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni:
Online Convolutional Sparse Coding. CoRR abs/1706.06972 (2017) - [i12]Quanming Yao, James T. Kwok, Taifeng Wang, Tie-Yan Liu:
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers. CoRR abs/1708.00146 (2017) - 2016
- [i11]Shuai Zheng, Ruiliang Zhang, James Tin-Yau Kwok:
Fast Nonsmooth Regularized Risk Minimization with Continuation. CoRR abs/1602.07844 (2016) - [i10]Shuai Zheng, James T. Kwok:
Fast-and-Light Stochastic ADMM. CoRR abs/1604.07070 (2016) - [i9]Quanming Yao, James T. Kwok:
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. CoRR abs/1606.03841 (2016) - [i8]Quanming Yao, James T. Kwok:
Learning of Generalized Low-Rank Models: A Greedy Approach. CoRR abs/1607.08012 (2016) - [i7]Quanming Yao, James T. Kwok:
Fast Learning with Nonconvex L1-2 Regularization. CoRR abs/1610.09461 (2016) - [i6]Lu Hou, Quanming Yao, James T. Kwok:
Loss-aware Binarization of Deep Networks. CoRR abs/1611.01600 (2016) - 2015
- [i5]Ruiliang Zhang, Shuai Zheng, James T. Kwok:
Fast Distributed Asynchronous SGD with Variance Reduction. CoRR abs/1508.01633 (2015) - [i4]Quanming Yao, James Tin-Yau Kwok, Wenliang Zhong:
Fast Low-Rank Matrix Learning with Nonconvex Regularization. CoRR abs/1512.00984 (2015) - 2013
- [i3]Yufeng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou:
Convex and Scalable Weakly Labeled SVMs. CoRR abs/1303.1271 (2013) - [i2]Leon Wenliang Zhong, James T. Kwok:
Fast Stochastic Alternating Direction Method of Multipliers. CoRR abs/1308.3558 (2013) - 2012
- [i1]Wenliang Zhong, James Tin-Yau Kwok:
Convex Multitask Learning with Flexible Task Clusters. CoRR abs/1206.4601 (2012)
Coauthor Index
![](https://dblp.uni-trier.de/img/cog.dark.24x24.png)
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