Tensor Learning Unit
We study various tensor-based machine learning technologies, e.g., tensor decomposition, multilinear latent variable model, tensor regression and classification, tensor networks, deep tensor learning, and Bayesian tensor learning, with aim to facilitate the learning from high-order structured data or large-scale latent space. Our goal is to develop innovative, scalable and efficient tensor learning algorithms supported by theoretical principles. The novel applications in computer vision and brain data analysis will also be explored to provide new insights into tensor learning methods.
11. 2018 Our paper has been accepted to AAAI 2019.
10. 2018 Unit Leader Qibin Zhao talked at
International Workshop on Tensor Networks and Machine Learning,
06. 2018 Dr. Guillaume Rabusseau (McGill University, Canada) give a talk "Machine Learning with Tensors for Structured Data".
06. 2018 Lecture at Waseda University.
05. 2018 Prof. Danilo Mandic (Imperial College London, UK) give a talk "Hearables: Enabling technologies for lifelong learning in E-Health".
04. 2018 Our paper has been accepted to IJCAI 2018.
03. 2018 Prof. Justin Dauwels (Nanyang Technological University, Singapore) give a talk "Bayesian Inference of Sparse Networks (BISN)".
01. 2018 Our paper has been accepted to ICLR 2018 (Workshop).
01. 2018 Our paper has been accepted to ICASSP 2018.
12. 2017 Dr. Fabien Lotte (Inria Bordeaux Sud-Ouest, France) give a talk "Combining machine learning and psychology to design usable Brain-Computer Interfaces".
12. 2017 Dr. Tomasz M. RUTKOWSKI (Cogent Labs Inc., The University of Tokyo, Japan) give a talk "End-to-end Deep-learning Approaches for Online BCI and Offline Experiment Brainwave Analyses".
Prof. Brahim CHAIB-DRAA,
Dr. Tomasz M. RUTKOWSKI,
Dr. Fabien Lotte,
Prof. Justin Dauwels,
Prof. Danilo Mandic,
Dr. Guillaume Rabusseau
Anjie Zhang (Parttimer),
Xingwei Cao (Parttimer),
Shifeng Huang (Intern),
Xinqi Chen (Intern)
- 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), pp. 2255–2261, 2018.
- L. Yuan, Q. Zhao, and J. Cao, “High-order tensor completion for data recovery via sparse tensor-train optimization,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1258-1262, 2018.
- X. Cao, X. Zhao, and Q. Zhao, “Tensorizing Generative Adversarial Nets,” in The Third International Conference On Consumer Electronics (ICCE-Asia), pp. 72-75, 2018.
- X. Zhao, G. Cui, L. Yuan, T. Tanaka, Q. Zhao, and J. Cao, “A hybrid brain computer interface based on audiovisual stimuli P300,” in The Third International Conference On Consumer Electronics (ICCE-Asia), pp. 68-71, 2018.
- 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.
- 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), pp. 222-229, 2017.
- 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), pp. 589-595, 2017.
- Q. Shi, Y.-m. Cheung, and Q. Zhao, “Feature extraction for incomplete data via low-rank tucker decomposition,” in Europe Conference on Machine Learning and Principles Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 564-581, 2017.
- 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 and Automated Learning (IDEAL), pp. 516-523, 2017.
- L. Gui, Q. Zhao, and J. Cao, “Brain image completion by Bayesian tensor decomposition,” in 22nd International Conference on Digital Signal Processing (DSP), pp. 1-4, 2017.
- 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, 2018.
- W. Kong, L. Wang, J. Zhang, Q. Zhao and J. Sun, "The Dynamic EEG Microstates in Mental Rotation," Sensors, vol. 18, no. 9, pp. 2920, 2018.
- J.-W. Lin, W. Chen, C.-P. Shen, M.-J. Chiu, Y.-H. Kao, F. Lai, Q. Zhao, and A. Cichocki, “Visualization and sonification of long-term epilepsy electroencephalogram monitoring,” Journal of Medical and Biological Engineering, pp. 1–10, 2018.
- Y. Zhang, D. Guo, F. Li, E. Yin, Y. Zhang, P. Li, Q. Zhao, T. Tanaka, D. Yao, and P. Xu, “Correlated component analysis for enhancing the performance of ssvep-based brain-computer interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 5, pp. 948–956, 2018.
- Y. Zhang, E. Yin, F. Li, Y. Zhang, T. Tanaka, Q. Zhao, Y. Cui, P. Xu, D. Yao, and D. Guo, “Two-stage frequency recognition method based on correlated component analysis for ssvep-based BCI,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 7, pp. 1314–1323, 2018.
- J. Sole-Casals, C. F. Caiafa, Q. Zhao, and A. Cichocki, “Brain-computer interface with corrupted eeg data: A tensor completion approach,” Cognitive Computation, 2018.
- A. Cichocki, N. Lee, I. Oseledets, A.-H. Phan, Q. Zhao, D. P. Mandic et al., “Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions,” Foundations and Trends R in Machine Learning, vol. 9, no. 4-5, pp. 249-429, 2016.
- A. Cichocki, A.-H. Phan, Q. Zhao, N. Lee, I. Oseledets, M. Sugiyama, D. P. Mandic et al., “Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives,” Foundations and Trends R in Machine Learning, vol. 9, no. 6, pp. 431-673, 2017.
- TGAN : Tensorizing Generative Adversarial Nets (Python)
- Gan PU : Generative Adversarial Positive-Unlabeled Learning (Python)
- FBCP : Bayesian CP Factorization for Tensor Completion (Matlab)
- BRTF : Bayesian Robust Tensor Factorization (Matlab)
- Tensor-Train Tensor Completion (Matlab)
- IJCAI 18 Generative Adversarial Positive-unlabeled Learning
Waseda University Lectures
Lecture 1 : Multilinear Algebra and Tensor Decompositions
Lecture 2 : Tensor Networks
Lecture 3 : Tensor Methods for Signal Processing and Machine Learning
Access to Center for Advanced Intelligence Project
RIKEN Center for Advanced Intelligence Project,
Nihonbashi 1-chome Mitsui Building, 15th floor,
1-4-1 Nihonbashi, Chuo-ku, Tokyo.
Seeking Openings for Research Scientists or Postdoctoral Researchers or Technical Scientist or Internship Student.