AI4Sセミナーシリーズ「Structured representation learning: tensor network principles toward scalable and reliable AI」 Talk by Qibin Zhao (RIKEN AIP)
AIP AI4S (AI for Science) Seminar Series
The AIP AI4S (AI for Science) Seminar Series is organized by the RIKEN Center for Advanced Intelligence Project (AIP).
This seminar series features invited researchers presenting recent advances, emerging methodologies, and interdisciplinary applications in AI for Science.
Through this series, we aim to promote cross-disciplinary discussion, foster collaboration, and strengthen the AI for Science research community.
Details of each seminar will be announced individually.
Date & Time : 4:00pm-5:30pm (JST), June 17, 2026
Venue : Hybrid (In-person + Online)
Please note that in-person attendance is limited to AIP researchers.
Speaker : Qibin Zhao (Team Director, RIKEN AIP)
Title : Structured representation learning: tensor network principles toward scalable and reliable AI
Abstract : Tensor Networks (TNs) are factorizations of high dimensional tensors into networks of many low-dimensional tensors, which have been studied in quantum physics, high-performance computing, and applied mathematics. TNs have been increasingly investigated and applied to machine learning and signal processing, due to its significant advances in handling large-scale and high-dimensional problems. This talk aims to present some recent progress of TNs technology developed for machine learning from perspectives of basic principle and algorithms, especially focusing on the efficiency, robustness and scalability issues in deep learning models.
