Tokyo University of Agriculture and Technology

Institute of Global Innovation Research

グローバルイノベーション研究院

公開セミナー                                             
15:00-16:00

   

言語 / 英語
Language/English
東京農工大学 小金井キャンパス 11 号館 1 階 L1112
Lecture Room L1112, 1st Fl., Building 11, Koganei Campus, TUAT

Speech technologies at google: an overview
Dr. Pedro J. Moreno Google (USA)

Organized by IEEE Signal Processing Society Tokyo Joint Chapter

16:10-16:55

Short-Term Time Series Forecasting and Anomaly Detection using Tensor
Decomposition and Higher-Order Tensor Networks

Prof. Andrzej Cichocki

Skolkovo Institute of Science and Technology (Russia)

One of the fundamental scientiﬁc problem in machine learning and signal processing is time series forecasting (TSF), i.e., given thepast historical samples, how well can we predict the future samples? In many applications with highly correlated multi-variate timeseries forecasting is quite challenging since a system has complex nonlinear temporal dependencies and higher-order dynamics.
In this talk we brieﬂy review recent progress in TSF using tensor networks and basic tensor decomposition models like Tucker or
CPD models. Multiple time series arising in real applications can be reformulated as set of matrices or even a high-order tensors(multi-way data) quite naturally, for example, brain data or set of video frames/neuroimages recorded by the spatio-temporal gridof electrodes or sensors. When dealing with tensors, traditional TSF models require reshaping time-series into higher order tensors.
Multi-way Delay embedding Transform (MDT) and Block Hankel Tensors (BHT) are promising methods to Hankelize and tensorize
available time series data sets to low-rank high-order block Hankel tensors. We will discuss in more detail MDT/BHT approach formultiple time series forecasting.
Tensor decomposition and tensor networks are promising and powerful computational techniques for extracting valuable
information from multidimensional data. Taking this advantage, tensor decomposition-based approaches can handle multiple timeseries simultaneously to achieve better forecasting performance. For example, Tucker decomposition integrated with ARIMA model
can be applied to obtain the multilinear ARIMA model for TSF tasks. Moreover, we can incorporate tensor decomposition with deepneural networks for more complex tensorial TSF.
In a nutshell, of this talk, we will explain how to apply hankelization along the temporal direction to transform multiple time series
to a high-order block Hankel tensors. Next, we will discuss the various tensor network decompositions which allows us to explore
intrinsic correlations among multiple time series. Furthermore, we will present tensor ‒ARIMA approach with various constraints
imposed on core tensors like smoothness, sparsity non-negativity or orthogonality.

17:00-17:45

From machine learning to user learning in brain-computer interaction
Dr. Fabien Lotte

Institut National de Recherche en Informatique et en Automatique (France)

Brain-computer interfaces (BCIs) are communication and control systems that translate users' brain activity - often measured using
electroencephalography (EEG) - into commands for an interactive application. Such BCIs are very promising for multiple applications
including using EEG for controlling assistive technologies for motor impaired users, for controlling games for healthy users or forpost-stroke rehabilitation, among many others.
However, despite this promising potential, current BCIs are still relatively rarely used outside laboratories, mostly due to their low
reliability: they (too) often recognize users' mental commands erroneously. To address the reliability issue of BCIs, two mains research
directions - that are complementary - can be followed: improving machine learning algorithms used to process EEG signals andimproving user training to guide them to acquire robust BCI control skills.
In this talk I will present some of our work in these directions. This will include our work on classiﬁers of EEG signals based on
adaptive Riemannian classiﬁers or our work on the content and modality of the BCI feedback. We will also illustrate both aspects withour experience in training a tetraplegic user to control a multi class and self-paced BCI.

■共催 ⁄ Co-organized by
グローバルイノベーション研究院 ライフサイエンス分野 田中チーム
Institute of Global Innovation Research “Life Science” Tanaka Team
卓越大学院プログラム
Excellent Leader Development for Super Smart Society
by New Industry Creation and Diversity
IEEE Signal Processing Society Tokyo Joint Chapter

どなたでも、ご聴講いただけます。
Everyone is welcome to attend.
小金井キャンパス
Koganei Campus

講演会場 /Venue東門
East Gate■お問合せ先 / Contact
グローバルイノベーション研究院 工学研究院 田中 聡久
Institute of Global Innovation Research, Institute of Engineering
Prof. Toshihisa Tanaka
e-mail: tanakat ( ここに @ を入れてください）cc.tuat.ac.jp
詳細はホームページをご覧ください
Please refer to our website for more information

正門
Main Gate

https://www.tuat-global.jp/
https://en.tuat-global.jp/