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Shujian Yu
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2020 – today
- 2024
- [j33]Qi Zhang, Mingfei Lu, Shujian Yu, Jingmin Xin, Badong Chen:
Discovering common information in multi-view data. Inf. Fusion 108: 102400 (2024) - [j32]Shuo Ye, Shujian Yu, Yu Wang, Xinge You:
R2-trans: Fine-grained visual categorization with redundancy reduction. Image Vis. Comput. 143: 104923 (2024) - [j31]Feiya Lv, Shujian Yu, Huawei Ye, Jinsong Zhao, Chenglin Wen:
Incipient fault detection and isolation with Cauchy-Schwarz divergence: A probabilistic approach. J. Frankl. Inst. 361(16): 107114 (2024) - [j30]Kaizhong Zheng, Shujian Yu, Liangjun Chen, Lujuan Dang, Badong Chen:
BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping. NeuroImage 292: 120594 (2024) - [j29]Kaizhong Zheng, Shujian Yu, Badong Chen:
CI-GNN: A Granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis. Neural Networks 172: 106147 (2024) - [c48]Changkyu Choi, Shujian Yu, Michael Kampffmeyer, Arnt-Børre Salberg, Nils Olav Handegard, Robert Jenssen:
DIB-X: Formulating Explainability Principles for a Self-Explainable Model Through Information Theoretic Learning. ICASSP 2024: 7170-7174 - [c47]Yuxin Dong, Tieliang Gong, Hong Chen, Shujian Yu, Chen Li:
Rethinking Information-theoretic Generalization: Loss Entropy Induced PAC Bounds. ICLR 2024 - [c46]Shujian Yu, Xi Yu, Sigurd Løkse, Robert Jenssen, José C. Príncipe:
Cauchy-Schwarz Divergence Information Bottleneck for Regression. ICLR 2024 - [c45]Wanqi Zhou, Shuanghao Bai, Shujian Yu, Qibin Zhao, Badong Chen:
Jacobian Regularizer-based Neural Granger Causality. ICML 2024 - [i48]Shujian Yu, Xi Yu, Sigurd Løkse, Robert Jenssen, José C. Príncipe:
Cauchy-Schwarz Divergence Information Bottleneck for Regression. CoRR abs/2404.17951 (2024) - [i47]Mingfei Lu, Shujian Yu, Robert Jenssen, Badong Chen:
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications. CoRR abs/2405.04061 (2024) - [i46]Wanqi Zhou, Shuanghao Bai, Shujian Yu, Qibin Zhao, Badong Chen:
Jacobian Regularizer-based Neural Granger Causality. CoRR abs/2405.08779 (2024) - [i45]Xiaoyun Xu, Zhuoran Liu, Stefanos Koffas, Shujian Yu, Stjepan Picek:
BAN: Detecting Backdoors Activated by Adversarial Neuron Noise. CoRR abs/2405.19928 (2024) - [i44]Wenzhe Yin, Shujian Yu, Yicong Lin, Jie Li, Jan-Jakob Sonke, Efstratios Gavves:
Domain Adaptation with Cauchy-Schwarz Divergence. CoRR abs/2405.19978 (2024) - [i43]Qi Zhang, Mingfei Lu, Shujian Yu, Jingmin Xin, Badong Chen:
Discovering Common Information in Multi-view Data. CoRR abs/2406.15043 (2024) - 2023
- [j28]Shuo Ye, Shujian Yu, Wenjin Hou, Yu Wang, Xinge You:
Coping with change: Learning invariant and minimum sufficient representations for fine-grained visual categorization. Comput. Vis. Image Underst. 237: 103837 (2023) - [j27]Kristoffer K. Wickstrøm, Sigurd Løkse, Michael C. Kampffmeyer, Shujian Yu, José C. Príncipe, Robert Jenssen:
Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy. Entropy 25(6): 899 (2023) - [j26]Francesco Alesiani, Shujian Yu, Xi Yu:
Gated information bottleneck for generalization in sequential environments. Knowl. Inf. Syst. 65(2): 683-705 (2023) - [j25]Yantao Wei, Shujian Yu, Luis G. Sánchez Giraldo, José C. Príncipe:
Multiscale principle of relevant information for hyperspectral image classification. Mach. Learn. 112(4): 1227-1252 (2023) - [j24]Yuxin Dong, Tieliang Gong, Shujian Yu, Chen Li:
Optimal Randomized Approximations for Matrix-Based Rényi's Entropy. IEEE Trans. Inf. Theory 69(7): 4218-4234 (2023) - [j23]Ane Blázquez-García, Kristoffer Wickstrøm, Shujian Yu, Karl Øyvind Mikalsen, Ahcène Boubekki, Angel Conde, Usue Mori, Robert Jenssen, José Antonio Lozano:
Selective Imputation for Multivariate Time Series Datasets With Missing Values. IEEE Trans. Knowl. Data Eng. 35(9): 9490-9501 (2023) - [j22]Zhengqiang Zhang, Qinmu Peng, Sichao Fu, Wenjie Wang, Yiu-Ming Cheung, Yue Zhao, Shujian Yu, Xinge You:
A Componentwise Approach to Weakly Supervised Semantic Segmentation Using Dual-Feedback Network. IEEE Trans. Neural Networks Learn. Syst. 34(10): 7541-7554 (2023) - [c44]Yuxin Dong, Tieliang Gong, Shujian Yu, Hong Chen, Chen Li:
Robust and Fast Measure of Information via Low-Rank Representation. AAAI 2023: 7450-7458 - [c43]Hongming Li, Shujian Yu, José C. Príncipe:
Causal Recurrent Variational Autoencoder for Medical Time Series Generation. AAAI 2023: 8562-8570 - [c42]Shujian Yu:
The Analysis of Deep Neural Networks by Information Theory: From Explainability to Generalization. AAAI 2023: 15462 - [c41]Luis Pedro Silvestrin, Shujian Yu, Mark Hoogendoorn:
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study. ECAI 2023: 2146-2153 - [c40]Qiang Li, Shujian Yu, Kristoffer H. Madsen, Vince D. Calhoun, Armin Iraji:
Higher-Order Organization in the Human Brain From Matrix-Based Rényi's Entropy. ICASSP Workshops 2023: 1-5 - [c39]Hongzhi Liu, Kaizhong Zheng, Shujian Yu, Badong Chen:
Towards a More Stable and General Subgraph Information Bottleneck. ICASSP 2023: 1-5 - [c38]Yichen Zhang, Shujian Yu, Badong Chen:
Sequential Invariant Information Bottleneck. ICASSP 2023: 1-5 - [c37]Kaizhong Zheng, Shujian Yu, Badong Chen:
Identification of Predictive Subnetwork for Brain Network-Based Psychiatric Diagnosis: An Information-Theoretic Perspective. ICASSP Workshops 2023: 1-5 - [i42]Kaizhong Zheng, Shujian Yu, Badong Chen:
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis. CoRR abs/2301.01642 (2023) - [i41]Hongming Li, Shujian Yu, José C. Príncipe:
Causal Recurrent Variational Autoencoder for Medical Time Series Generation. CoRR abs/2301.06574 (2023) - [i40]Shujian Yu, Hongming Li, Sigurd Løkse, Robert Jenssen, José C. Príncipe:
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making. CoRR abs/2301.08970 (2023) - [i39]Qiang Li, Shujian Yu, Kristoffer H. Madsen, Vince D. Calhoun, Armin Iraji:
Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy. CoRR abs/2303.11994 (2023) - [i38]Shuo Ye, Shujian Yu, Wenjin Hou, Yu Wang, Xinge You:
Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization. CoRR abs/2306.04893 (2023) - [i37]Luis Pedro Silvestrin, Shujian Yu, Mark Hoogendoorn:
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study. CoRR abs/2307.08572 (2023) - [i36]Francesco Alesiani, Shujian Yu, Mathias Niepert:
Continual Invariant Risk Minimization. CoRR abs/2310.13977 (2023) - [i35]Xiaoyun Xu, Shujian Yu, Jingzheng Wu, Stjepan Picek:
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness. CoRR abs/2312.04960 (2023) - 2022
- [j21]Qiang Li, Greg Ver Steeg, Shujian Yu, Jesus Malo:
Functional Connectome of the Human Brain with Total Correlation. Entropy 24(12): 1725 (2022) - [j20]Wanqi Zhou, Shujian Yu, Badong Chen:
Causality detection with matrix-based transfer entropy. Inf. Sci. 613: 357-375 (2022) - [j19]Shiyu Duan, Shujian Yu, José C. Príncipe:
Modularizing Deep Learning via Pairwise Learning With Kernels. IEEE Trans. Neural Networks Learn. Syst. 33(4): 1441-1451 (2022) - [j18]Tieliang Gong, Yuxin Dong, Shujian Yu, Bo Dong:
Computationally Efficient Approximations for Matrix-Based Rényi's Entropy. IEEE Trans. Signal Process. 70: 6170-6184 (2022) - [c36]Ammar Shaker, Shujian Yu, Daniel Oñoro-Rubio:
Learning to Transfer with von Neumann Conditional Divergence. AAAI 2022: 8231-8239 - [c35]Hongming Li, Shujian Yu, José C. Príncipe:
Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing. ICASSP 2022: 3878-3882 - [c34]Qi Zhang, Shujian Yu, Jingmin Xin, Badong Chen:
Multi-View Information Bottleneck Without Variational Approximation. ICASSP 2022: 4318-4322 - [c33]Ammar Shaker, Francesco Alesiani, Shujian Yu:
Modular-Relatedness for Continual Learning. IDA 2022: 290-301 - [c32]Yunhuan Li, Xi Yu, Shujian Yu, Badong Chen:
Adversarial Training for the Adversarial Robustness of EEG-Based Brain-Computer Interfaces. MLSP 2022: 1-6 - [c31]Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, José C. Príncipe:
Principle of relevant information for graph sparsification. UAI 2022: 2331-2341 - [i34]Hongming Li, Shujian Yu, José C. Príncipe:
Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing. CoRR abs/2202.02951 (2022) - [i33]Yu Wang, Shuo Ye, Shujian Yu, Xinge You:
R2-Trans: Fine-Grained Visual Categorization with Redundancy Reduction. CoRR abs/2204.10095 (2022) - [i32]Qi Zhang, Shujian Yu, Jingmin Xin, Badong Chen:
Multi-view Information Bottleneck Without Variational Approximation. CoRR abs/2204.10530 (2022) - [i31]Kaizhong Zheng, Shujian Yu, Baojuan Li, Robert Jenssen, Badong Chen:
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck. CoRR abs/2205.03612 (2022) - [i30]Yuxin Dong, Tieliang Gong, Shujian Yu, Chen Li:
Optimal Randomized Approximations for Matrix based Renyi's Entropy. CoRR abs/2205.07426 (2022) - [i29]Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, José C. Príncipe:
Principle of Relevant Information for Graph Sparsification. CoRR abs/2206.00118 (2022) - [i28]Yufeng Shi, Shujian Yu, Duanquan Xu, Xinge You:
Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval. CoRR abs/2209.12491 (2022) - [i27]Yuxin Dong, Tieliang Gong, Shujian Yu, Hong Chen, Chen Li:
Robust and Fast Measure of Information via Low-rank Representation. CoRR abs/2211.16784 (2022) - 2021
- [j17]Feiya Lv, Shujian Yu, Chenglin Wen, José C. Príncipe:
Interpretable fault detection using projections of mutual information matrix. J. Frankl. Inst. 358(7): 4028-4057 (2021) - [j16]Shujian Yu, Kristoffer Wickstrøm, Robert Jenssen, José C. Príncipe:
Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration. IEEE Trans. Neural Networks Learn. Syst. 32(1): 435-442 (2021) - [c30]Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, José C. Príncipe:
Measuring Dependence with Matrix-based Entropy Functional. AAAI 2021: 10781-10789 - [c29]Xi Yu, Shujian Yu, José C. Príncipe:
Deep Deterministic Information Bottleneck with Matrix-Based Entropy Functional. ICASSP 2021: 3160-3164 - [c28]Francesco Alesiani, Shujian Yu, Xi Yu:
Gated Information Bottleneck for Generalization in Sequential Environments. ICDM 2021: 1-10 - [c27]Shujian Yu, Luis G. Sánchez Giraldo, José C. Príncipe:
Information-Theoretic Methods in Deep Neural Networks: Recent Advances and Emerging Opportunities. IJCAI 2021: 4669-4678 - [c26]Ammar Shaker, Francesco Alesiani, Shujian Yu, Wenzhe Yin:
Bilevel Continual Learning. IJCNN 2021: 1-8 - [i26]Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, José C. Príncipe:
Measuring Dependence with Matrix-based Entropy Functional. CoRR abs/2101.10160 (2021) - [i25]Xi Yu, Shujian Yu, José C. Príncipe:
Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional. CoRR abs/2102.00533 (2021) - [i24]Ammar Shaker, Shujian Yu:
Learning to Transfer with von Neumann Conditional Divergence. CoRR abs/2108.03531 (2021) - [i23]Bo Hu, Shujian Yu, José C. Príncipe:
Information Theoretic Structured Generative Modeling. CoRR abs/2110.05794 (2021) - [i22]Francesco Alesiani, Shujian Yu, Xi Yu:
Gated Information Bottleneck for Generalization in Sequential Environments. CoRR abs/2110.06057 (2021) - [i21]Tieliang Gong, Yuxin Dong, Shujian Yu, Hong Chen, Bo Dong, Chen Li, Qinghua Zheng:
Computationally Efficient Approximations for Matrix-based Renyi's Entropy. CoRR abs/2112.13720 (2021) - 2020
- [j15]Qi Zheng, Shujian Yu, Xinge You:
Coarse-to-fine salient object detection with low-rank matrix recovery. Neurocomputing 376: 232-243 (2020) - [j14]Shiyu Duan, Shujian Yu, Yunmei Chen, José C. Príncipe:
On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation. Neural Comput. 32(1): 97-135 (2020) - [j13]Shujian Yu, Luis Gonzalo Sánchez Giraldo, Robert Jenssen, José C. Príncipe:
Multivariate Extension of Matrix-Based Rényi's $\alpha$α-Order Entropy Functional. IEEE Trans. Pattern Anal. Mach. Intell. 42(11): 2960-2966 (2020) - [j12]Jiamiao Xu, Shujian Yu, Xinge You, Mengjun Leng, Xiao-Yuan Jing, C. L. Philip Chen:
Multiview Hybrid Embedding: A Divide-and-Conquer Approach. IEEE Trans. Cybern. 50(8): 3640-3653 (2020) - [c25]Rishabh Singh, Shujian Yu, José C. Príncipe:
Composite Dynamic Texture Synthesis Using Hierarchical Linear Dynamical System. ICASSP 2020: 2757-2761 - [c24]Shujian Yu, Ammar Shaker, Francesco Alesiani, José C. Príncipe:
Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications. IJCAI 2020: 2777-2784 - [c23]Ammar Shaker, Christoph Gärtner, Xiao He, Shujian Yu:
Online Meta-Forest for Regression Data Streams. IJCNN 2020: 1-8 - [c22]Francesco Alesiani, Shujian Yu, Ammar Shaker, Wenzhe Yin:
Towards Interpretable Multi-task Learning Using Bilevel Programming. ECML/PKDD (2) 2020: 593-608 - [i20]Shujian Yu, Ammar Shaker, Francesco Alesiani, José C. Príncipe:
Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications. CoRR abs/2005.02196 (2020) - [i19]Shiyu Duan, Shujian Yu, José C. Príncipe:
Modularizing Deep Learning via Pairwise Learning With Kernels. CoRR abs/2005.05541 (2020) - [i18]Yanjun Li, Shujian Yu, José C. Príncipe, Xiaolin Li, Dapeng Oliver Wu:
PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders. CoRR abs/2007.06503 (2020) - [i17]Feiya Lv, Shujian Yu, Chenglin Wen, José C. Príncipe:
Mutual Information Matrix for Interpretable Fault Detection. CoRR abs/2007.10692 (2020) - [i16]Francesco Alesiani, Shujian Yu, Ammar Shaker, Wenzhe Yin:
Towards Interpretable Multi-Task Learning Using Bilevel Programming. CoRR abs/2009.05483 (2020) - [i15]Shujian Yu, Francesco Alesiani, Ammar Shaker, Wenzhe Yin:
Learning an Interpretable Graph Structure in Multi-Task Learning. CoRR abs/2009.05618 (2020) - [i14]Ammar Shaker, Francesco Alesiani, Shujian Yu, Wenzhe Yin:
Bilevel Continual Learning. CoRR abs/2011.01168 (2020)
2010 – 2019
- 2019
- [j11]Shujian Yu, José C. Príncipe:
Simple Stopping Criteria for Information Theoretic Feature Selection. Entropy 21(1): 99 (2019) - [j10]Shujian Yu, Zubin Abraham, Heng Wang, Mohak Shah, Yantao Wei, José C. Príncipe:
Concept drift detection and adaptation with hierarchical hypothesis testing. J. Frankl. Inst. 356(5): 3187-3215 (2019) - [j9]Shujian Yu, José C. Príncipe:
Understanding autoencoders with information theoretic concepts. Neural Networks 117: 104-123 (2019) - [j8]Peng Zhang, Shujian Yu, Jiamiao Xu, Xinge You, Xiubao Jiang, Xiao-Yuan Jing, Dacheng Tao:
Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling. IEEE Trans. Circuits Syst. Video Technol. 29(12): 3673-3686 (2019) - [c21]Liugfen Li, Guowei Lin, Zouyu Xie, Shujian Yu, Zhen Guo, Yufang Liang:
Data Encryption Transmission and Authentication Scheme Based on Blockchain Technology. DPTA 2019: 2037-2044 - [i13]Zhengqiang Zhang, Shujian Yu, Shi Yin, Qinmu Peng, Xinge You:
Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation. CoRR abs/1905.12190 (2019) - [i12]Yantao Wei, Shujian Yu, José C. Príncipe:
Multiscale Principle of Relevant Information for Hyperspectral Image Classification. CoRR abs/1907.06022 (2019) - [i11]Kristoffer Wickstrøm, Sigurd Løkse, Michael Kampffmeyer, Shujian Yu, José C. Príncipe, Robert Jenssen:
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels. CoRR abs/1909.11396 (2019) - 2018
- [j7]Weihua Ou, Fei Long, Yi Tan, Shujian Yu, Pengpeng Wang:
Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning. Multim. Tools Appl. 77(10): 12955-12978 (2018) - [j6]Yue Zhao, Xinge You, Shujian Yu, Chang Xu, Wei Yuan, Xiao-Yuan Jing, Taiping Zhang, Dacheng Tao:
Multi-view manifold learning with locality alignment. Pattern Recognit. 78: 154-166 (2018) - [c20]Ying Ma, Bing Ouyang, Stephanie Farrington, Shujian Yu, John Reed, José C. Príncipe:
Joint Image Segmentation and Classification with Application to Cluttered Coral Images. GlobalSIP 2018: 365-369 - [c19]Shujian Yu, Xiaoyang Wang, José C. Príncipe:
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels. IJCAI 2018: 3033-3039 - [i10]Shujian Yu, José C. Príncipe:
Understanding Autoencoders with Information Theoretic Concepts. CoRR abs/1804.00057 (2018) - [i9]Shujian Yu, Robert Jenssen, José C. Príncipe:
Understanding Convolutional Neural Network Training with Information Theory. CoRR abs/1804.06537 (2018) - [i8]Jiamiao Xu, Shujian Yu, Xinge You, Mengjun Leng, Xiao-Yuan Jing, C. L. Philip Chen:
Multi-view Hybrid Embedding: A Divide-and-Conquer Approach. CoRR abs/1804.07237 (2018) - [i7]Qi Zheng, Shujian Yu, Xinge You, Qinmu Peng, Wei Yuan:
Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery. CoRR abs/1805.07936 (2018) - [i6]Shujian Yu, Xiaoyang Wang, José C. Príncipe:
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels. CoRR abs/1806.10131 (2018) - [i5]Shujian Yu, Luis Gonzalo Sánchez Giraldo, Robert Jenssen, José C. Príncipe:
Multivariate Extension of Matrix-based Renyi's α-order Entropy Functional. CoRR abs/1808.07912 (2018) - [i4]Peng Zhang, Shujian Yu, Jiamiao Xu, Xinge You, Xiubao Jiang, Xiao-Yuan Jing, Dacheng Tao:
Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling. CoRR abs/1811.07498 (2018) - [i3]Shujian Yu, José C. Príncipe:
Simple stopping criteria for information theoretic feature selection. CoRR abs/1811.11971 (2018) - 2017
- [c18]Shujian Yu, Zheng Cao, Xiubao Jiang:
Robust linear discriminant analysis with a Laplacian assumption on projection distribution. ICASSP 2017: 2567-2571 - [c17]Shujian Yu, Matthew Emigh, Eder Santana, José C. Príncipe:
Autoencoders trained with relevant information: Blending Shannon and Wiener's perspectives. ICASSP 2017: 6115-6119 - [c16]Shujian Yu, Zubin Abraham:
Concept Drift Detection with Hierarchical Hypothesis Testing. SDM 2017: 768-776 - [i2]Zheng Cao, Shujian Yu, Bing Ouyang, Fraser R. Dalgleish, Anni K. Vuorenkoski, Gabriel Alsenas, José C. Príncipe:
Marine Animal Classification with Correntropy Loss Based Multi-view Learning. CoRR abs/1705.01217 (2017) - [i1]Shujian Yu, Zubin Abraham, Heng Wang, Mohak Shah, José C. Príncipe:
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing. CoRR abs/1707.07821 (2017) - 2016
- [j5]Haiquan Zhao, Xiangping Zeng, Zhengyou He, Shujian Yu, Badong Chen:
Improved functional link artificial neural network via convex combination for nonlinear active noise control. Appl. Soft Comput. 42: 351-359 (2016) - [j4]Weihua Ou, Shujian Yu, Gai Li, Jian Lu, Kesheng Zhang, Gang Xie:
Multi-view non-negative matrix factorization by patch alignment framework with view consistency. Neurocomputing 204: 116-124 (2016) - [j3]Shujian Yu, Xinge You, Weihua Ou, Xiubao Jiang, Kexin Zhao, Ziqi Zhu, Yi Mou, Xinyi Zhao:
STFT-like time frequency representations of nonstationary signal with arbitrary sampling schemes. Neurocomputing 204: 211-221 (2016) - [j2]Ziqi Zhu, Xinge You, Shujian Yu, Jixin Zou, Haiquan Zhao:
Dynamic texture modeling and synthesis using multi-kernel Gaussian process dynamic model. Signal Process. 124: 63-71 (2016) - [j1]Xinge You, Weigang Guo, Shujian Yu, Kan Li, José C. Príncipe, Dacheng Tao:
Kernel Learning for Dynamic Texture Synthesis. IEEE Trans. Image Process. 25(10): 4782-4795 (2016) - [c15]