Tensor Learning Unit
We study various tensorbased 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 highorder structured data or largescale 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.
News
 05.2019 Our paper has been accepted to ACL 2019.
 05.2019 Our paper received IEEE Signal Processing Magazine
Best Paper
Award.
[Paper]
 05.2019 Longhao Yuan recieved the
Best Student Paper
Award.
[Paper]
 02.2019 2 papers have been accepted to CVPR 2019.
 02.2019 5 papers have been accepted to ICASSP 2019.
 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,
Skoltech, Moscow.
 06.2018 Dr. Guillaume Rabusseau (McGill University, Canada) give a talk "Machine Learning with Tensors for Structured Data".
 06.2018 Lecture at Waseda University.
[Silde]
 05.2018 Prof. Danilo Mandic (Imperial College London, UK) give a talk "Hearables: Enabling technologies for lifelong learning in EHealth".
 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 SudOuest, France) give a talk "Combining machine learning and psychology to design usable BrainComputer Interfaces".
 12.2017 Dr. Tomasz M. RUTKOWSKI (Cogent Labs Inc., The University of Tokyo, Japan) give a talk "Endtoend Deeplearning Approaches for Online BCI and Offline Experiment Brainwave Analyses".
Members
Unit Leader
Postdoctoral Researcher
Parttimer
Intern
Jianqiang Li, Andong Wang, Ziyao Wang.

Visiting Scientist
Visitor
Jordi SoléCasals,
Brahim CHAIBDRAA,
Tomasz M. RUTKOWSKI,
Fabien Lotte,
Justin Dauwels,
Danilo Mandic,
Guillaume Rabusseau.

Former members
Yuyuan Yu (Intern),
Jiajia Tang (Intern),
Jinshi Yu (Intern),
Anjie Zhang (Parttimer),
Xingwei Cao (Parttimer),
Shifeng Huang (Intern),
Xinqi Chen (Intern).

Selected Publications
Full publication list
[PDF]
2019
 Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
P. Liang, Z. Liu, Y. Tsai, Q. Zhao, R. Salakhutdinov, L. Morency
[C] ACL 2019
[Paper]
 Guaranteed Matrix Completion Under Multiple Linear Transformations
C. Li, W. He, L. Yuan, Z. Sun, Q. Zhao
[C] CVPR 2019
[Paper]
[Silde]
 NonLocal Meets Global: An Integrated Paradigm for Hyperspectral Denoising
W. He, Q. Yao, C. Li, N. Yokoya, Q. Zhao
[C] CVPR 2019
[Paper]
 Learning Efficient Tensor Representations with Ringstructured Networks
Q. Zhao, M. Sugiyama, L. Yuan, A, Cichocki
[C] ICASSP 2019
[Paper]
[Poster]
 Lowrank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling
C. Li, Z. Sun, J. Yu, M. Hou, Q. Zhao
[C] ICASSP 2019
[Paper]
[Poster]
 Randomized Tensor Ring Decomposition and Its Application to Largescale Data Reconstruction
L. Yuan, C. Li, J. Cao, Q. Zhao
[C] ICASSP 2019
[Paper]
[Poster]
 Totalvariationregularized Tensor Ring Completion for Remote Sensing Image Reconstruction
W. He, L. Yuan, N. Yokoya
[C] ICASSP 2019
[Paper]
[Poster]
 Tensor ring decomposition with rank minimization on latent space: An efficient approach for
tensor completion
L. Yuan, C. Li, M. Danilo, J. Cao, Q. Zhao
[C] AAAI 2019
[Code]
2018
 Tensorizing generative adversarial nets
X. Cao, X. Zhao, Q. Zhao
[C] ICCE Asia 2018 (International Conference On Consumer Electronics)
[Paper]
[Code]
 Generative adversarial positiveunlabeled learning
M. Hou, B. Chaibdraa, C. Li, Q. Zhao
[C] IJCAI 2018
[Paper]
[Silde]
[Code]
 AI neurotechnology for aging societies–taskload and dementia EEG digital biomarker development using information geometry machine learning methods
T. M. Rutkowski, Q. Zhao, M. S. Abe, M. Otake
[C] NeurIPS Workshop 2018
 Highorder tensor completion for data recovery via sparse tensortrain optimization
L. Yuan, Q. Zhao, J. Cao
[C] ICASSP 2018
[Paper]
 Detection of epileptic foci based on interictal iEEG by using convolutional neural network
X. Zhao, Q. Zhao, T. Tanaka, J. Cao, W. Kong, H. Sugano, N. Yoshida
[C] DSP 2018 (International Conference on Digital Signal Processing)
[Paper]
 Feature extraction for incomplete data via lowrank tensor decomposition with feature regularization
Q. Shi, Y. Cheung, Q. Zhao, H. Lu
[J] IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
[Paper]
 Highorder tensor completion via gradientbased optimization under tensor train format
L. Yuan, Q. Zhao, L. Gui, J. Cao
[J] Signal Processing: Image Communication
[Paper]
2017
 Feature extraction for incomplete data via lowrank Tucker decomposition
Q. Shi, Y. Cheung, and Q. Zhao
[C] ECML PKDD 2017 (Joint European Conference on Machine Learning and Knowledge Discovery in Databases)
[Paper]
2016
 Tensor Ring Decomposition
Q. Zhao, G. Zhou, S. Xie, L. Zhang, A. Cichocki
[J] [arXiv]
Book
 Tensor networks for dimensionality reduction and largescale optimization: Part 2 applications and future perspectives
A. Cichocki, A. Phan, Q. Zhao, N. Lee, I. Oseledets, M. Sugiyama, D. P. Mandic et al
Foundations and Trends in Machine Learning
[Book]
 Tensor networks for dimensionality reduction and largescale optimization: Part 1 lowrank tensor decompositions
A. Cichocki, N. Lee, I. Oseledets, A. Phan, Q. Zhao, D. P. Mandic et al
Foundations and Trends in Machine Learning
[Book]
Codes
 TRLRF: Tensor Ring Lowrank Factors (Matlab)
[Github]
 TGAN: Tensorizing Generative Adversarial Nets (Python)
[Github]
 Gan PU: Generative Adversarial PositiveUnlabeled Learning (Python)
[Code]
 FBCP: Bayesian CP Factorization for Tensor Completion (Matlab)
[Github]
 BRTF: Bayesian Robust Tensor Factorization (Matlab)
[Github]
 T3C: TensorTrain Tensor Completion (Matlab)
[Github]
Sildes
 Tensor Network Representation for Machine Learning  Recent Advances and Perspectives
[Silde]

Waseda University Lectures
Lecture 1: Multilinear Algebra and Tensor Decompositions
[Silde]
Lecture 2: Tensor Networks
[Silde]
Lecture 3: Tensor Methods for Signal Processing and Machine Learning
[Silde]
Access
Access to Center for Advanced Intelligence Project
RIKEN Center for Advanced Intelligence Project,
Nihonbashi 1chome Mitsui Building, 15th floor,
141 Nihonbashi, Chuoku, Tokyo.
〒1030027, Japan.
Tel: +81(0)484673626
Job Opportunities
Seeking Openings for Research Scientists or Postdoctoral Researchers or Technical Scientist or Internship Student.
[Job]