List of Publications
Tensor Learning Team, RIKEN AIP
https://qibinzhao.github.io

Conference Papers
2024

2023

[1]

Y. Zheng, X. Zhao, J. Zeng, C. Li, Q. Zhao, H. Li, and T. Huang, “SVDinsTN: A tensor
network paradigm for efficient structure search from regularized modeling perspective,” inIEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024.

[2]

Y. Qiu, G. Zhou, A. Wang, Z. Huang, and Q. Zhao, “Towards multi-mode outlier robust tensor ring decomposition,” in Proceedings of the 38th AAAI Conference on Artificial Intelligence(AAAI), 2024.

[3]

Z. Tao, T. Tanaka, and Q. Zhao, “Efficient nonparametric tensor decomposition for binary
and count data,” in Proceedings of the 38th AAAI Conference on Artificial Intelligence(AAAI), 2024.

[4]

S. Bai, M. Zhang, W. Zhou, S. Huang, Z. Luan, D. Wang, and B. Chen, “Prompt-based
distribution alignment for unsupervised domain adaptation,” in Proceedings of the 38th AAAI
Conference on Artificial Intelligence (AAAI), 2024.

[5]

G. Lin, C. Li, J. Zhang, T. Tanaka, and Q. Zhao, “Adversarial training on purification (AToP):
Advancing both robustness and generalization,” in The Twelfth International Conference on
Learning Representations (ICLR), 2024.

[6]

C. Ju, R. J. Kobler, L. Tang, C. Guan, and M. Kawanabe, “Deep geodesic canonical correlation analysis for covariance-based neuroimaging data,” in The Twelfth International Conference on Learning Representations (ICLR), 2024.

[7]

C. Li, J. Zeng, C. Li, C. F. Caiafa, and Q. Zhao, “Alternating local enumeration (tnale):
Solving tensor network structure search with fewer evaluations,” in International Conference
on Machine Learning (ICML), PMLR, 2023, pp. 20 384–20 411.

[8]

Z. Tao, T. Tanaka, and Q. Zhao, “Scalable bayesian tensor ring factorization for multiway data analysis,” in International Conference on Neural Information Processing (ICONIP),
Springer, 2023, pp. 490–503.

[9]

C. Ju, R. J. Kobler, and C. Guan, “Score-based data generation for eeg spatial covariancematrices: Towards boosting bci performance,” in Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023,
pp. 1–7.

[10]

J. Zhang, Y. Hong, and Q. Zhao, “Memorization weights for instance reweighting in adversarial training,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI),
vol. 37, 2023, pp. 11 228–11 236.

[11]

S. Mo, Z. Sun, and C. Li, “Multi-level contrastive learning for self-supervised vision transformers,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer
Vision, 2023, pp. 2778–2787.
1

2022

[12]

S. Mo, Z. Sun, and C. Li, “Representation disentanglement in generative models with contrastive learning,” in Proceedings of the IEEE/CVF Winter Conference on Applications ofComputer Vision, 2023, pp. 1531–1540.

[13]

Z. Lin, H. Huang, Y. Yu, G. Zhou, and Q. Zhao, “Consistent anchor induced multi-view
deep matrix factorization,” in The 42nd Chinese Control Conference (CCC), IEEE, 2023,
pp. 7633–7637.

[14]

Z. Tao, T. Tanaka, and Q. Zhao, “Undirected probabilistic model for tensor decomposition,”in Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023.

[15]

A. Wang, C. Li, M. Bai, Z. Jin, G. Zhou, and Q. Zhao, “Transformed low-rank parameterization can help robust generalization for tensor neural networks,” in Thirty-seventh Conference
on Neural Information Processing Systems (NeurIPS), 2023.

[16]

H. Takayama and T. Yokota, “Fast signal completion algorithm with cyclic convolutionalsmoothing,” in 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, 2022, pp. 364–371.

[17]

R. Yamamoto, H. Hontani, A. Imakura, and T. Yokota, “Consistent mdt-tucker: A hankel
structure constrained tucker decomposition in delay embedded space,” in 2022 Asia-PacificSignal and Information Processing Association Annual Summit and Conference (APSIPA
ASC), IEEE, 2022, pp. 137–142.

[18]

M. Bai, J. Chen, Q. Zhao, C. Li, J. Zhang, and J. Gao, “Tensor neural controlled differential
equations,” in 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, 2022,
pp. 1–9.

[19]

H. Huang, Y. Luo, G. Zhou, and Q. Zhao, “Multi-view data representation via deep autoencoderlike nonnegative matrix factorization,” in IEEE International Conference on Acoustics, Speechand Signal Processing (ICASSP), IEEE, 2022, pp. 3338–3342.

[20]

K. Konstantinidis, Y. L. Xu, Q. Zhao, and D. P. Mandic, “Variational Bayesian tensor networks with structured posteriors,” in IEEE International Conference on Acoustics, Speechand Signal Processing (ICASSP), IEEE, 2022, pp. 3638–3642.

[21]

W. Wang, L. Niu, J. Zhang, X. Yang, and L. Zhang, “Dual-path image inpainting with auxiliary GAN inversion,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition
CVPR 2022, 2022, pp. 11 411–11 420.

[22]

J. Tang, K. Li, M. Hou, X. Jin, W. Kong, Y. Ding, and Q. Zhao, “Mmt: Multi-way multimodal transformer for multimodal learning,” in IJCAI, 2022.

[23]

C. Li, J. Zeng, Z. Tao, and Q. Zhao, “Permutation search of tensor network structures via
local sampling,” in International Conference on Machine Learning (ICML), PMLR, 2022,
pp. 13 106–13 124.

[24]

R. J. Kobler, J.-i. Hirayama, Q. Zhao, and M. Kawanabe, “Spd domain-specific batch normalization to crack interpretable unsupervised domain adaptation in eeg,” in NeurIPS 2022,
2022.

2

2021

[25]

R. Yamamoto, H. Hontani, A. Imakura, and T. Yokota, “Fast algorithm for low-rank tensor completion in delay-embedded space,” in Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR), 2022, pp. 2058–2066.

[26]

Y. Li, Z. Sun, and C. Li, “Are we pruning the correct channels in image-to-image translation
models,” in The 33rd British Machine Vision Conference (BMVC) Proceedings, 2022.

[27]

S. Mo, Z. Sun, and C. Li, “Rethinking prototypical contrastive learning through alignment,
uniformity and correlation,” in The 33rd British Machine Vision Conference (BMVC) Proceedings, 2022.

[28]

Y. Hong, L. Niu, and J. Zhang, “Shadow generation for composite image in real-world
scenes,” in Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, 2022, pp. 914–
922.

[29]

X. Zhao, S. Takata, K. Fukumori, and T. Tanaka, “Infant posture assessment based on
rotational keypoint detection,” in 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, 2021, pp. 1546–1550.

[30]

X. Zhao, J. Solé-Casals, Q. Zhao, J. Cao, and T. Tanaka, “Multi-feature fusion for epileptic
focus localization based on tensor representation,” in 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, 2021,
pp. 1323–1327.

[31]

Z. Huang, Y. Qiu, Q. Zhao, and G. Zhou, “Bayesian robust Tucker decomposition for multiway data analysis,” in 2021 China Automation Congress (CAC), IEEE, 2021, pp. 5559–
5564.

[32]

Q. Jiang, Y. Yu, Q. Zhao, and W. Sun, “Semi-supervised robust dual-graph concept factorization via L2,1 norm,” in 2021 Chinese Automation Congress (CAC), 2021.

[33]

M. Bai, Q. Zhao, and J. Gao, “Tensorial time series prediction via tensor neural ordinary
differential equations,” in 2021 International Joint Conference on Neural Networks (IJCNN),
IEEE, 2021, pp. 1–8.

[34]

Z. Huang, C. Li, F. Duan, and Q. Zhao, “Multi-distorted image restoration with tensor 1 x
1 convolutional layer,” in 2021 International Joint Conference on Neural Networks (IJCNN),
IEEE, 2021, pp. 1–8.

[35]

J. Zhang, Z. Tao, L. Zhang, and Q. Zhao, “Tensor decomposition via core tensor networks,”
in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2021, pp. 2130–2134.

[36]

Z. Sun, C. Li, and Q. Zhao, “Hide chopin in the music: Efficient information steganography
via random shuffling,” in IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), IEEE, 2021, pp. 2370–2374.

[37]

W. Wang, J. Zhang, L. Niu, H. Ling, X. Yang, and L. Zhang, “Parallel multi-resolution fusion
network for image inpainting,” in IEEE/CVF International Conference on Computer Vision,
ICCV 2021, IEEE, 2021, pp. 14 539–14 548.

3

2020

[38]

H. Qiu, C. Li, Y. Weng, Z. Sun, X. He, and Q. Zhao, “On the memory mechanism of tensorpower recurrent models,” in International Conference on Artificial Intelligence and Statistics(AISTATS’21), PMLR, 2021, pp. 3682–3690.

[39]

Z. Tao, X. Zhao, T. Tanaka, and Q. Zhao, “Bayesian latent factor model for higher-orderdata,” in Proceedings of The 13th Asian Conference on Machine Learning (ACML), ser. Proceedings of Machine Learning Research, vol. 157, PMLR, 2021, pp. 1285–1300.

[40]

J. Tang, K. Li, X. Jin, A. Cichocki, Q. Zhao, and W. Kong, “CTFN: Hierarchical learning
for multimodal sentiment analysis using coupled-translation fusion network,” in Proceedingsof the 59th Annual Meeting of the Association for Computational Linguistics and the 11th
International Joint Conference on Natural Language Processing (Volume 1: Long Papers),
2021, pp. 5301–5311.

[41]

Y.-B. Zheng, T.-Z. Huang, X.-L. Zhao, Q. Zhao, and T.-X. Jiang, “Fully-connected tensor
network decomposition and its application to higher-order tensor completion,” in Proceedingsof the AAAI Conference on Artificial Intelligence (AAAI’21), vol. 35, 2021, pp. 11 071–11 078.

[42]

C. F. Caiafa, Z. Wang, J. Sole-Casals, and Q. Zhao, “Learning from incomplete featuresby simultaneous training of neural networks and sparse coding,” in Proceedings of theIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops,
2021, pp. 2621–2630.

[43]

X. Zhao, J. Solé-Casals, B. Li, Z. Huang, A. Wang, J. Cao, T. Tanaka, and Q. Zhao, “Classification of epileptic iEEG signals by CNN and data augmentation,” in IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 926–930.

[44]

C. Li and Z. Sun, “Evolutionary topology search for tensor network decomposition,” in International Conference on Machine Learning (ICML), PMLR, 2020, pp. 5947–5957.

[45]

X. Zhao, L. Sui, T. Tanaka, J. Cao, and Q. Zhao, “Epileptic focus localization based on iEEG
plot images by using convolutional neural network,” in Proceedings of the 12th International
Conference on Bioinformatics and Computational Biology, vol. 70, 2020, pp. 173–181.

[46]

B. Li, C. Li, F. Duan, N. Zheng, and Q. Zhao, “TPFN: Applying outer product along time tomultimodal sentiment analysis fusion on incomplete data,” in The 2020 European Conference
on Computer Vision (ECCV-20), 2020.

[47]

C. Li, M. E. Khan, Z. Sun, G. Niu, B. Han, S. Xie, and Q. Zhao, “Beyond unfolding: Exact
recovery of latent convex tensor decomposition under reshuffling,” in Thirty-Fourth AAAI
Conference on Artificial Intelligence (AAAI-20), 2020, pp. 4602–4609.

[48]

Q. Shi, J. Yin, J. Cai, A. Cichocki, T. Yokota, L. Chen, M. Yuan, and J. Zeng, “Block hankel
tensor arima for multiple short time series forecasting.,” in Thirty-Fourth AAAI Conferenceon Artificial Intelligence (AAAI-20), 2020, pp. 5758–5766.

[49]

A. Wang, C. Li, Z. Jin, and Q. Zhao, “Robust tensor decomposition via orientation invariant
tubal nuclear norms,” in Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20),
2020, pp. 6102–6109.

4

2019

[50]

B. Li, X. Zhao, Q. Zhao, T. Tanaka, and J. Cao, “A one-dimensional convolutional neuralnetwork model for automated localization of epileptic foci,” in 2019 Asia-Pacific Signal andInformation Processing Association Annual Summit and Conference (APSIPA ASC), IEEE,
2019, pp. 741–744.

[51]

F. Aminmansour, A. Patterson, L. Le, Y. Peng, D. Mitchell, F. Pestilli, C. Caiafa, R. Greiner,and M. White, “Learning macroscopic brain connectomes via group-sparse factorization,” inAdvances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019, pp. 8847–
8857.

[52]

M. Hou, J. Tang, J. Zhang, W. Kong, and Q. Zhao, “Deep multimodal multilinear fusion
with high-order polynomial pooling,” in Advances in Neural Information Processing Systems
32 (NeurIPS 2019), 2019, pp. 12 113–12 122.

[53]

L. Sui, X. Zhao, Q. Zhao, T. Tanaka, and J. Cao, “Localization of epileptic foci by using convolutional neural network based on ieeg,” in Artificial Intelligence Applications andInnovations (AIAI), Springer International Publishing, 2019, pp. 331–339.

[54]

J. Yu, C. Li, Q. Zhao, and G. Zhao, “Tensor-ring nuclear norm minimization and applicationfor visual : Data completion,” in IEEE International Conference on Acoustics, Speech andSignal Processing (ICASSP), 2019, pp. 3142–3146. doi: 10.1109/ICASSP.2019.8683115.

[55]

W. He, L. Yuan, and N. Yokoya, “Total-variation-regularized tensor ring completion for remote sensing image reconstruction,” in IEEE International Conference on Acoustics, Speechand Signal Processing (ICASSP), IEEE, 2019, pp. 8603–8607.

[56]

C. Li, Z. Sun, J. Yu, M. Hou, and Q. Zhao, “Low-rank embedding of kernels in convolutional
neural networks under random shuffling,” in IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), IEEE, 2019, pp. 3022–3026.

[57]

A. Wang, X. Song, X. Wu, Z. Lai, and Z. Jin, “Generalized dantzig selector for low-tubalrank tensor recovery,” in IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), IEEE, 2019, pp. 3427–3431.

[58]

A. Wang, X. Song, X. Wu, Z. Lai, and Z. Jin, “Latent schatten TT norm for tensor
completion,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019, pp. 2922–2926.

[59]

A. Wang, X. Song, X. Wu, Z. Lai, and Z. Jin, “Robust low-tubal-rank tensor completion,” inIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE,
2019, pp. 3432–3436.

[60]

L. Yuan, C. Li, J. Cao, and Q. Zhao, “Randomized tensor ring decomposition and its application to large-scale data reconstruction,” in IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), 2019, pp. 2127–2131.

[61]

Q. Zhao, M. Sugiyama, L. Yuan, and A. Cichocki, “Learning efficient tensor representations
with ring-structured networks,” in IEEE International Conference on Acoustics, Speech andSignal Processing (ICASSP), IEEE, 2019, pp. 8608–8612.

5

2018

[62]

Q. Chen, L. Yuan, Y. Miao, Q. Zhao, T. Tanaka, and J. Cao, “Quasi-brain-death eeg diagnosis based on tensor train decomposition,” in International Symposium on Neural Networks(ISNN), Springer, 2019, pp. 501–511.

[63]

L. Yuan, C. Li, D. Mandic, J. Cao, and Q. Zhao, “Tensor ring decomposition with rank
minimization on latent space: An efficient approach for tensor completion,” in Proceedingsof the AAAI Conference on Artificial Intelligence (AAAI), vol. 33, 2019, pp. 9151–9158.

[64]

P. P. Liang, Z. Liu, Y.-H. H. Tsai, Q. Zhao, R. Salakhutdinov, and L.-P. Morency, “Learning
representations from imperfect time series data via tensor rank regularization,” in Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2019,
pp. 1569–1576.

[65]

W. He, Q. Yao, C. Li, N. Yokoya, and Q. Zhao, “Non-local meets global: An integrated
paradigm for hyperspectral denoising,” in Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2019, pp. 6868–6877.

[66]

C. Li, W. He, L. Yuan, Z. Sun, and Q. Zhao, “Guaranteed matrix completion under multiple
linear transformations,” in Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 2019, pp. 11 136–11 145.

[67]

T. Yokota, K. Kawai, M. Sakata, Y. Kimura, and H. Hontani, “Dynamic pet image reconstruction using nonnegative matrix factorization incorporated with deep image prior,” in The
IEEE International Conference on Computer Vision (ICCV), 2019, pp. 3126–3135.

[68]

L. Yuan, Q. Zhao, and J. Cao, “High-order tensor completion for data recovery via sparsetensor-train optimization,” in 2018 IEEE International Conference on Acoustics, Speech andSignal Processing (ICASSP), IEEE, 2018, pp. 1258–1262.

[69]

J. Yu, G. Zhou, Q. Zhao, and K. Xie, “An effective tensor completion method based on
multi-linear tensor ring decomposition,” in APSIPA-ASC 2018, 2018, pp. 1244–1349.

[70]

L. Yuan, J. Cao, X. Zhao, Q. Wu, and Q. Zhao, “Higher-dimension tensor completion via
low-rank tensor ring decomposition,” in APSIPA-ASC 2018, 2018, pp. 1071–1076.

[71]

X. Zhao, T. Tanaka, W. Kong, Q. Zhao, J. Cao, H. Sugano, and N. Yoshida, “Epileptic focus
localization based on iEEG by using positive unlabeled (PU) learning,” in APSIPA-ASC 2018,2018, pp. 493–497.

[72]

T. M. Rutkowski, Q. Zhao, M. S. Abe, and M. Otake, “AI neurotechnology for aging
societies–task-load and dementia EEG digital biomarker development using information geometry machine learning methods,” in NeurIPS Workshop, 2018.

[73]

M. Hou, B. Chaib-draa, C. Li, and Q. Zhao, “Generative adversarial positive-unlabeled learning,” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), 2018, pp. 2255–2261.

[74]

Q. Zhao, M. Sugiyama, L. Yuan, and A. Cichocki, “Learning efficient tensor representations
with ring structure networks,” in Sixth International Conference on Learning Representations(ICLR Workshop), 2018.

6

2017

[75]

X. Zhao, Q. Zhao, T. Tanaka, J. Cao, W. Kong, H. Sugano, and N. Yoshida, “Detection of
epileptic foci based on interictal iEEG by using convolutional neural network,” in The 23rd
International Conference on Digital Signal Processing (DSP), 2018.

[76]

X. Kong, W. Kong, Q. Fan, Q. Zhao, and A. Cichocki, “Task-independent EEG identification
via low-rank matrix decomposition,” in The IEEE International Conference on Bioinformatics
and Biomedicine (BIBM), 2018, pp. 412–419.

[77]

X. Cao, X. Zhao, and Q. Zhao, “Tensorizing generative adversarial nets,” in The Third
International Conference On Consumer Electronics (ICCE) Asia, 2018, pp. 206–212.

[78]

X. Zhao, G. Cui, L. Yuan, T. Tanaka, Q. Zhao, and J. Cao, “A hybrid brain computerinterface based on audiovisual stimuli p300,” in The Third International Conference On
Consumer Electronics (ICCE) Asia, 2018, pp. 206–212.

[79]

Y. Xin, Q. Wu, Q. Zhao, and Q. Wu, “Semi-supervised regularized discriminant analysis for
EEG-based BCI system,” in International Conference on Intelligent Data Engineering andAutomated Learning (IDEAL), Springer, 2017, pp. 516–523.

[80]

G. Cui, L. Zhu, Q. Zhao, J. Cao, and A. Cichocki, “A graph theory analysis on distinguishing EEG-based brain death and coma,” in International Conference on Neural Information
Processing (ICONIP), ser. Lecture Notes in Computer Science, Springer, vol. 10637, 2017,
pp. 589–595.

[81]

L. Yuan, Q. Zhao, and J. Cao, “Completion of high order tensor data with missing entries via
tensor-train decomposition,” in International Conference on Neural Information Processing(ICONIP), ser. Lecture Notes in Computer Science, Springer, vol. 10634, 2017, pp. 222–229.

[82]

Q. Shi, Y.-m. Cheung, and Q. Zhao, “Feature extraction for incomplete data via low-rank
Tucker decomposition,” in Joint European Conference on Machine Learning and Knowledge
Discovery in Databases (ECML PKDD), ser. Lecture Notes in Computer Science, IEEE,
vol. 10534, 2017, pp. 564–581.

[83]

L. Gui, Q. Zhao, and J. Cao, “Brain image completion by Bayesian tensor decomposition,”in Proceedings of 22nd International Conference on Digital Signal Processing (DSP), IEEE,
2017, pp. 1–4.

Journal Papers
2024

[84]

J. Zeng, Y. Qiu, Y. Ma, A. Wang, and Q. Zhao, “A novel tensor ring sparsity measurement
for image completion,” Entropy, vol. 26, no. 2, p. 105, 2024.

[85]

M. Bai, D. Zhou, and Q. Zhao, “Tendiffpure: A convolutional tensor-train denoising diffusion
model for purification,” Frontiers of Information Technology and Electronic Engineering,
vol. 25, no. 1, pp. 160–169, 2024.

[86]

J. Lin, T.-Z. Huang, X.-L. Zhao, T.-Y. Ji, and Q. Zhao, “Tensor robust kernel pca for
multidimensional data,” IEEE Transactions on Neural Networks and Learning Systems, 2024.

7

2023

[87]

Y. Qiu, G. Zhou, A. Wang, Q. Zhao, and S. Xie, “Balanced unfolding induced tensor nuclear
norms for high-order tensor completion,” IEEE Transactions on Neural Networks and Learning
Systems, 2024.

[88]

S. Han, Z. Sun, K. Zhao, F. Duan, C. F. Caiafa, Y. Zhang, and J. Solé-Casals, “Early
prediction of dementia using fmri data with a graph convolutional network approach,” Journalof Neural Engineering, vol. 21, no. 1, p. 016 013, 2024.

[89]

Z. Tao, T. Tanaka, and Q. Zhao, “Nonparametric tensor ring decomposition with scalable
amortized inference,” Neural Networks, vol. 169, pp. 431–441, 2024.

[90]

X. Zhao, Q. Zhao, T. Tanaka, J. Solé-Casals, G. Zhou, T. Mitsuhashi, H. Sugano, N. Yoshida,
and J. Cao, “Classification of the epileptic seizure onset zone based on partial annotation,”
Cognitive Neurodynamics, vol. 17, no. 3, pp. 703–713, 2023.

[91]

J. Wang, A. Qu, Q. Wang, Q. Zhao, J. Liu, and Q. Wu, “Tt-net: Tensorized transformer
network for 3d medical image segmentation,” Computerized Medical Imaging and Graphics,
vol. 107, p. 102 234, 2023.

[92]

S. Shan, Z. Ding, K. Zhang, H. Wei, C. Li, and Q. Zhao, “Acgl-tr: A deep learning model
for spatio-temporal short-term irradiance forecast,” Energy Conversion and Management,
vol. 284, p. 116 970, 2023.

[93]

H. Takayama and T. Yokota, “A new model for tensor completion: Smooth convolutional
tensor factorization,” IEEE Access, vol. 11, pp. 67 526–67 539, 2023.

[94]

J. Zeng, G. Zhou, Y. Qiu, Y. Ma, and Q. Zhao, “Hyperspectral and multispectral image
fusion via bayesian nonlocal cp factorization,” IEEE Geoscience and Remote Sensing Letters,
vol. 21, 2023.

[95]

H. Wang, J. Peng, X. Cao, J. Wang, Q. Zhao, and D. Meng, “Hyperspectral image denoising
via nonlocal spectral sparse subspace representation,” IEEE Journal of Selected Topics inApplied Earth Observations and Remote Sensing, vol. 16, pp. 5189–5203, 2023.

[96]

J. Tang, D. Liu, X. Jin, Y. Peng, Q. Zhao, Y. Ding, and W. Kong, “BAFN: Bi-direction
attention based fusion network for multimodal sentiment analysis,” IEEE Transactions on
Circuits and Systems for Video Technology, vol. 33, no. 4, pp. 1966–1978, Apr. 2023.

[97]

X. Jin, N. Li, W. Kong, Q. Zhao, J. Tang, L. Zhu, and J. Cao, “Disentangled adversarial generalization network for cross-session task-independent brainprint recognition,” IEEE
Transactions on Cognitive and Developmental Systems, 2023.

[98]

Y. Yu, G. Zhou, N. Zheng, Y. Qiu, S. Xie, and Q. Zhao, “Graph-regularized non-negative
tensor-ring decomposition for multiway representation learning,” IEEE Transactions on Cybernetics, vol. 53, no. 5, pp. 3114 –3127, 2023.

[99]

H. Huang, G. Zhou, Q. Zhao, L. He, and S. Xie, “Comprehensive multiview representation
learning via deep autoencoder-like nonnegative matrix factorization,” IEEE Transactions on
Neural Networks and Learning Systems, 2023.

[100]

H. Qiu, C. Li, Y. Weng, Z. Sun, and Q. Zhao, “Fractional tensor recurrent unit (ftru): A
stable forecasting model with long memory,” IEEE Transactions on Neural Networks andLearning Systems, 2023.
8

2022

[101]

Y. Yu, A. Bezerianos, A. Cichocki, and J. Li, “Latent space coding capsule network formental workload classification,” IEEE Transactions on Neural Systems and Rehabilitation
Engineering, vol. 31, pp. 3417–3427, 2023.

[102]

H. Huang, G. Zhou, N. Liang, Q. Zhao, and S. Xie, “Diverse deep matrix factorization
with hypergraph regularization for multiview data representation,” IEEE/CAA Journal ofAutomatica Sinica, vol. 10, no. JAS-2022-0135, p. 2154, 2023.

[103]

H. Huang, G. Zhou, Y. Zheng, Z. Yang, and Q. Zhao, “Exclusivity and consistency induced
nmf for multi-view representation learning,” Knowledge-Based Systems, vol. 281, p. 111 020,
2023.

[104]

A. Wang, G. Zhou, Z. Jin, and Q. Zhao, “Noisy tensor completion via orientation invariant
tubal nuclear norm,” Pacific Journal of Optimization, vol. 19, no. 2, pp. 273–313, 2023.

[105]

L. Shi, B. Cheng, D. Li, S. Xiang, T. Liu, and Q. Zhao, “A cnn-based lamb wave processing
model for field monitoring of fatigue cracks in orthotropic steel bridge decks,” Structures,vol. 57, p. 105 146, 2023.

[106]

J. Tang, M. Hou, X. Jin, J. Zhang, Q. Zhao, and W. Kong, “Tree-based mix-order polynomial
fusion network for multimodal sentiment analysis,” Systems, vol. 11, no. 1, p. 44, 2023.

[107]

X. Zhao, Y. Yu, G. Zhou, Q. Zhao, and W. Sun, “Fast hypergraph regularized nonnegative
tensor ring decomposition based on low-rank approximation,” Applied Intelligence, pp. 1–24,
2022.

[108]

K. Takahashi, Z. Sun, J. Solé-Casals, A. Cichocki, A. H. Phan, Q. Zhao, H.-H. Zhao, S.
Deng, and R. Micheletto, “Data augmentation for convolutional LSTM based brain computerinterface system,” Applied Soft Computing, p. 108 811, 2022.

[109]

Y. Luo, A. Wang, G. Zhou, and Q. Zhao, “A hybrid norm for guaranteed tensor recovery,”
Frontiers in Physics, p. 447, 2022.

[110]

W.-J. Zheng, X.-L. Zhao, Y.-B. Zheng, and Z.-F. Pang, “Nonlocal patch-based fully connected tensor network decomposition for multispectral image inpainting,” IEEE Geoscienceand Remote Sensing Letters, vol. 19, pp. 1–5, 2022.

[111]

Y. Qiu, G. Zhou, Z. Huang, Q. Zhao, and S. Xie, “Efficient tensor robust PCA under hybrid
model of tucker and tensor train,” IEEE Signal Processing Letters, vol. 29, pp. 627–631,
2022.

[112]

X. Chen, G. Zhou, Y. Wang, M. Hou, Q. Zhao, and S. Xie, “Accommodating multiple tasks’disparities with distributed knowledge-sharing mechanism,” IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 2440–2452, 2022.

[113]

Y.-C. Miao, X.-L. Zhao, X. Fu, J.-L. Wang, and Y.-B. Zheng, “Hyperspectral denoising using
unsupervised disentangled spatiospectral deep priors,” IEEE Transactions on Geoscience andRemote Sensing, vol. 60, pp. 1–16, 2022.

[114]

Y.-S. Luo, X.-L. Zhao, T.-X. Jiang, Y. Chang, M. K. Ng, and C. Li, “Self-supervised nonlinear
transform-based tensor nuclear norm for multi-dimensional image recovery,” IEEE Transactions on Image Processing, 2022.

9[115]

Y. Qiu, G. Zhou, Q. Zhao, and S. Xie, “Noisy tensor completion via low-rank tensor ring,”
IEEE Transactions on Neural Networks and Learning Systems, pp. 1–15, 2022.

[116]

T. Yokota, H. Hontani, Q. Zhao, and A. Cichocki, “Manifold modeling in embedded space:
An interpretable alternative to deep image prior,” IEEE Transactions on Neural Networks
and Learning Systems (TNNLS), vol. 33, no. 3, pp. 1022–1036, 2022.

[117]

W. He, Q. Yao, C. Li, N. Yokoya, Q. Zhao, H. Zhang, and L. Zhang, “Non-local meets global:
An iterative paradigm for hyperspectral image restoration,” IEEE Transactions on PatternAnalysis and Machine Intelligence (TPAMI), vol. 44, no. 4, pp. 2089–2107, 2022.

[118]

T. Li, G. Zhou, Y. Qiu, and Q. Zhao, “Toward understanding convolutional neural networks from volterra convolution perspective,” Journal of Machine Learning Research (JMLR),
vol. 23, no. 311, pp. 1–50, 2022.

[119]

S. Liu, J. Zhang, A. Wang, H. Wu, Q. Zhao, and J. Long, “Subject adaptation convolutional
neural network for eeg-based motor imagery classification,” Journal of Neural Engineering,
2022.

[120]

Y. Zheng, T. Huang, X. Zhao, and Q. Zhao, “Tensor completion via fully-connected tensor
network decomposition with regularized factors,” Journal of Scientific Computing, vol. 92,
no. 8, pp. 1–35, 2022.

[121]

Y. Yu, G. Zhou, H. Huang, S. Xie, and Q. Zhao, “A semi-supervised label-driven autoweighted strategy for multi-view data classification,” Knowledge-Based Systems, p. 109 694,
2022.

[122]

Y. Qiu, G. Zhou, J. Zeng, Q. Zhao, and S. Xie, “Imbalanced low-rank tensor completion via
latent matrix factorization,” Neural Networks, 2022.

[123]

W. He, Y. Chen, N. Yokoya, C. Li, and Q. Zhao, “Hyperspectral super-resolution via coupled
tensor ring factorization,” Pattern Recognition, vol. 122, p. 108 280, 2022.

[124]

A. Wang, Q. Zhao, Z. Jin, C. Li, and G. Zhou, “Robust tensor decomposition via orientation
invariant tubal nuclear norms,” Science China Technological Sciences, pp. 1–18, 2022.

[125]

D. Zhang, Y. Luo, Y. Yu, Q. Zhao, and G. Zhou, “Semi-supervised multi-view clustering withdual hypergraph regularized partially shared non-negative matrix factorization,” SCIENCECHINA: Technological Sciences, 2022.

[126]

H. Takayama, Q. Zhao, H. Hontani, and T. Yokota, “Bayesian tensor completion and decomposition with automatic cp rank determination using mgp shrinkage prior,” SN Computer
Science, vol. 3, no. 3, pp. 1–17, 2022.

2021 [127]

A. Wang, G. Zhou, Z. Jin, and Q. Zhao, “Tensor recovery via ∗l -spectral k-support norm,”
IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 522–534, 2021. doi:
10.1109/JSTSP.2021.3058763.

[128]

Z. Chen, G. Zhou, and Q. Zhao, “Hierarchical factorization strategy for high-order tensor andapplication for data completion,” IEEE Signal Processing Letters, vol. 28, pp. 1255–1259,
2021.

10

2020

2019

[129]

J. Yu, G. Zhou, C. Li, Q. Zhao, and S. Xie, “Low tensor-ring rank completion by parallel
matrix factorization,” IEEE transactions on neural networks and learning systems (TNNLS),
vol. 32, no. 7, pp. 3020–3033, 2021.

[130]

X. Jin, J. Tang, X. Kong, Y. Peng, J. Cao, Q. Zhao, and W. Kong, “CTNN: A convolutional tensor-train neural network for multi-task brainprint recognition,” IEEE Transactions
on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 103–112, 2021.

[131]

L. Sui, X. Zhao, Q. Zhao, T. Tanaka, and J. Cao, “Hybrid convolutional neural network for
localization of epileptic focus based on iEEG,” Neural Plasticity, vol. 2021, 2021.

[132]

B. Li, Z. Zhang, F. Duan, Z. Yang, Q. Zhao, Z. Sun, and J. Solé-Casals, “Component-mixing
strategy: A decomposition-based data augmentation algorithm for motor imagery signals,”
Neurocomputing, vol. 465, pp. 325–335, 2021.

[133]

A. Wang, G. Zhou, and Q. Zhao, “Guaranteed robust tensor completion via L-SVD withapplications to remote sensing data,” Remote Sensing, vol. 13, no. 18, p. 3671, 2021.

[134]

Y. Qiu, G. Zhou, X. Chen, D. Zhang, X. Zhao, and Q. Zhao, “Semi-supervised non-negative
tucker decomposition for tensor data representation,” Science China Technological Sciences,vol. 64, no. 9, pp. 1881–1892, 2021.

[135]

C. Caiafa, J. Solé-Casals, P. Marti-Puig, S. Zhe, and T. Tanaka, “Decomposition methods
for machine learning with small, incomplete or noisy datasets,” Applied Sciences, vol. 10,
no. 23, p. 8481, 2020.

[136]

R. Li, Q. Wu, J. Liu, Q. Wu, C. Li, and Q. Zhao, “Monitoring depth of anesthesia basedon hybrid features and recurrent neural network,” Frontiers in neuroscience, vol. 14, p. 26,
2020.

[137]

Z. Sun, B. Li, F. Duan, H. Jia, S. Wang, Y. Liu, A. Cichocki, C. F. Caiafa, and J. SoleCasals, “Wlnet: Towards an approach for robust workload estimation based on shallow neuralnetworks,” IEEE Access, vol. 9, pp. 3165–3173, 2020.

[138]

F. Duan, Z. Huang, Z. Sun, Y. Zhang, Q. Zhao, A. Cichocki, Z. Yang, and J. Solé-Casals,“Topological network analysis of early alzheimer’s disease based on resting-state eeg,” IEEE
Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 10, pp. 2164–
2172, 2020.

[139]

G. Cui, L. Zhu, L. Gui, Q. Zhao, J. Zhang, and J. Cao, “Multidimensional clinical datadenoising via Bayesian CP factorization,” Science China Technological Sciences, vol. 63,
no. 2, pp. 249–254, 2020.

[140]

L. A. Suad, C. F. Caiafa, S. Cichowolski, and E. M. Arnal, “Galactic hi supershells: Kinetic
energies and possible origin,” Astronomy and Astrophysics, vol. 624, no. A&A, pp. 1–11,
2019.

[141]

M. Iwata, L. Yuan, Q. Zhao, Y. Tabei, F. Berenger, R. Sawada, S. Akiyoshi, M. Hamano, andY. Yamanishi, “Predicting drug-induced transcriptome responses of a wide range of humancell lines by a novel tensor-train decomposition algorithm,” Bioinformatics, vol. 35, no. 14,
pp. i191–i199, 2019.

11

2018

[142]

W. He, N. Yokoya, L. Yuan, and Q. Zhao, “Remote sensing image reconstruction using tensor
ring completion and total variation,” IEEE Transactions on Geoscience and Remote Sensing,vol. 57, no. 11, pp. 8998–9009, 2019.

[143]

Q. Shi, Y. Cheung, Q. Zhao, and H. Lu, “Feature extraction for incomplete data via low-rank
tensor decomposition with feature regularization,” IEEE Transactions on Neural Networks andLearning Systems (TNNLS), vol. 30, no. 6, pp. 1803 –1817, 2019.

[144]

W. Kong, X. Kong, Q. Fan, Q. Zhao, and A. Cichocki, “Task-free brainprint recognition
based on low-rank and sparse decomposition model,” International Journal of Data Mining
and Bioinformatics (IJDMB), vol. 22, no. 3, pp. 280–300, 2019.

[145]

L. Sui, X. Zhao, J. Cao, and Q. Zhao, “Localization of epileptic foci from ieeg via mixed
convolutional neural network,” International Journal of Latest Trends in Engineering andTechnology, vol. 14, no. 4, pp. 8–13, 2019.

[146]

L. Yuan, J. Cao, and Q. Zhao, “Tensor ring decomposition for visual data denoising via tensor
random projection,” International Journal of Latest Trends in Engineering and Technology,
vol. 13, no. 2, pp. 102–107, 2019.

[147]

X. Zhao, L. Gui, J. Cao, and Q. Zhao, “Epileptic focus localization based on entropy andconvolutional neural network,” International Journal of Latest Trends in Engineering andTechnology, vol. 14, no. 4, pp. 14–17, 2019.

[148]

L. Yuan, C. Li, J. Cao, and Q. Zhao, “Rank minimization on tensor ring: An efficient approachfor tensor decomposition and completion,” Machine Learning, pp. 1–20, 2019.

[149]

L. Zhu, G. Cui, J. Cao, A. Cichocki, J. Zhang, and C. Zhou, “A hybrid system for distinguishing between brain death and coma using diverse eeg features,” Sensors, vol. 19, no. 6,
p. 1342, 2019.

[150]

L. Yuan, Q. Zhao, L. Gui, and J. Cao, “High-order tensor completion via gradient-based
optimization under tensor train format,” Signal Processing: Image Communication, vol. 73,
pp. 53–61, 2019.

[151]

S. Al-Baddai, P. Marti-Puig, E. Gallego-Jutglà, K. Al-Subari, A. M. Tomé, B. Ludwig, E. W.
Lang, and J. Solé-Casals, “A recognition–verification system for noisy faces based on an
empirical mode decomposition with green’s functions,” Soft Computing, pp. 1–19, 2019.

[152]

Y. Qiu, G. Zhou, Q. Zhao, and A. Cichocki, “Comparative study on the classification methods
for breast cancer diagnosis,” Bulletin of the Polish Academy of Sciences. Technical Sciences,vol. 66, no. 6, pp. 841–848, 2018.

[153]

J. Solé-Casals, C. F. Caiafa, Q. Zhao, and A. Cichocki, “Brain-computer interface withcorrupted EEG data: A tensor completion approach,” Cognitive Computation, vol. 10, no. 6,1062––1074, 2018.

[154]

Y. Kumagai, R. Matsui, and T. Tanaka, “Music familiarity affects EEG entrainment whenlittle attention is paid,” Frontiers in Human Neuroscience, vol. 12, p. 444, 2018.

[155]

Y. Zhang, D. Guo, F. Li, et al., “Correction to “correlated component analysis for enhancing
the performance of SSVEP-based brain-computer interface”,” IEEE Transactions on Neural
Systems and Rehabilitation Engineering (TNSRE), vol. 26, no. 8, pp. 1645–1646, 2018.
12

[156]

Y. Zhang, D. Guo, F. Li, et al., “Correlated component analysis for enhancing the performance of SSVEP-based brain-computer interface.,” IEEE Transactions on Neural Systems
and Rehabilitation Engineering (TNSRE), vol. 26, no. 5, pp. 948–956, 2018.

[157]

Y. Zhang, E. Yin, F. Li, et al., “Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI,” IEEE Transactions on Neural Systems andRehabilitation Engineering (TNSRE), vol. 26, no. 7, pp. 1314–1323, 2018.

[158]

L. Gui, X. Zhao, Q. Zhao, and J. Cao, “Image and video completion by using Bayesian
tensor decomposition,” International Journal of Computer Science Issues (IJCSI), vol. 15,
no. 5, pp. 1–8, 2018.

[159]

L. Gui, X. Zhao, Q. Zhao, and J. Cao, “Non-local image denoising by using Bayesian lowrank tensor factorization on high-order patches,” International Journal of Computer Science
Issues (IJCSI), vol. 15, no. 5, pp. 16–25, 2018.

[160]

J. Lin, W. Chen, C. Shen, M. Chiu, Y. Kao, F. Lai, Q. Zhao, and A. Cichocki, “Visualizationand sonification of long-term epilepsy electroencephalogram monitoring,” Journal of Medical
and Biological Engineering, vol. 38, no. 6, 943––952, 2018.

[161]

W. Kong, L. Wang, J. Zhang, Q. Zhao, and J. Sun, “The dynamic EEG microstates in mental
rotation,” Sensors, vol. 18, no. 9, p. 2920, 2018.

Book Chapter
2022

[162]

T. Yokota, C. F. Caiafa, and Q. Zhao, “Tensor methods for low-level vision,” in Tensors for
Data Processing. Elsevier, 2022, pp. 371–425.

Book
2017

[163]

A. Cichocki, A. Phan, Q. Zhao, N. Lee, I. Oseledets, M. Sugiyama, and D. Mandic, Tensor
networks for dimensionality reduction and large-scale optimization: Part 2 applications andfuture perspectives (Foundations and Trends® in Machine Learning 6). Now Publishers,Inc., 2017, vol. 9, pp. 431–673.

2016

[164]

A. Cichocki, N. Lee, I. Oseledets, A. Phan, Q. Zhao, and D. Mandic, Tensor networks for
dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions(Foundations and Trends® in Machine Learning 4-5). Now Publishers, Inc., 2016, vol. 9,
pp. 249–429.

13