EPFL CIS – RIKEN AIP Seminar Series by Qibin Zhao

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Event details

Date 16.02.2022
Hour 10:0011:00
Speaker Qibin Zhao
Location
By Zoom
Online
Category Conferences - Seminars
Event Language English

Get your Zoom link:https://c5dc59ed978213830355fc8978.doorkeeper.jp/events/130798

Date and Time: February 16th 6:00pm – 7:00pm(JST)
10:00am-11:00pm(CET)

Title: Efficient Machine Learning with Tensor Networks
Abstract:
Modern ML methods have achieved the remarkable performance by dramatically increasing the DNN model size and the amount of high quality data samples. However, how to learn information from data efficiently and train a parameter efficient model become important in particular applications. Tensor Networks (TNs), which were studied in quantum physics and applied mathematics, have been increasingly investigated and applied to machine learning and signal processing, due to their advantages in handling large-scale and high-dimensional problems, model compression in DNNs, and efficient computations for learning algorithms. This talk aims to present some recent progresses of TNs technology applied to machine learning from perspectives of basic principle and algorithms, particularly in unsupervised learning, data completion, multi-model learning and various applications in deep learning modeling and etc. Finally, we will also present several potential research directions and new trends in this area.
Bio:
Qibin Zhao received the Ph.D. degree in computer science from Shanghai Jiao Tong University, China in 2009. He was a research scientist at RIKEN Brain Science Institute from 2009 to 2017. Then, he joined RIKEN Center for Advanced Intelligence Project as a unit leader (2017 - 2019) and is currently a team leader for tensor learning team. He is also a visiting professor in Tokyo University of Agriculture and Technology and Saitama Institute of Technology, Japan. His research interests include machine learning, tensor factorization and tensor networks, and brain signal processing. He has published more than 150 scientific papers, and co-authored two monographs on tensor networks. He serves as an editorial board member for the journal “Science China: Technological Sciences”, Area Chair for top-tier ML conferences of NeurIPS, ICML, AISTATS, AAAI, IJCAI and ACML. He has (co)-organized several workshops on “tensor networks in machine learning” at NeurIPS 2020, 2021 and IJCAI 2020.
 
 
RIKEN Center for Advanced Intelligence Project (AIP) which houses more than 40 research teams ranging from fundamentals of machine learning to analysis of ethics and social impact of artificial intelligence collaborate with the EPFL CIS on a monthly online seminar series around the topics and applications of AI.
RIKEN is Japan’s largest comprehensive research institution renowned for high-quality research in a diverse range of scientific disciplines.
RIKEN Center for Advanced Intelligence Project (AIP) houses more than 40 research teams ranging from fundamentals of machine learning and optimization, applications in medicine, materials, and disaster, to analysis of ethics and social impact of artificial intelligence.
EPFL is located in Switzerland and is one of the most vibrant and cosmopolitan science and technology institutions. EPFL has both a Swiss and international vocation and focuses on three missions: teaching, research and innovation.
The Center for Intelligent Systems (CIS) at EPFL, a joint initiative of the schools ENAC, IC, SB, STI and SV seeks to advance research and practice in the strategic field of intelligent systems.

All participants are required to agree with the AIP Seminar Series Code of Conduct.
Please see the URL below.
https://aip.riken.jp/event-list/termsofparticipation/?lang=en
RIKEN AIP will expect adherence to this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.

Practical information

  • General public
  • Free

Organizer

  • CIS

Contact

Tags

CISSBSTISVICENACApprentissage automatique Intelligence artificielle Robotique Vision par ordinateur Artificial intelligence AI Robotics Computer vision

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