Program
| Time | Talk | Chair |
|---|---|---|
| 9:50–10:00 |
Opening Remarks by Qibin Zhao
|
|
| 10:00–10:30 |
Title: Interpretable AI: A missing link to critical decision making and eHealth
Speaker: Danilo Mandic (Imperial College London)
|
Chao Li |
| 10:30–11:00 |
Title: Towards verifiable inference based on disentangled tensor manifold representation
Speaker: Liqing Zhang (Shanghai Jiao Tong University)
|
|
| 11:00–11:20 |
Coffee Break
|
|
| 11:20–11:50 |
Title: Towards AI-based Diagnostic Support for Epileptic EEG
Speaker: Toshihisa Tanaka (Tokyo University of Agriculture and Technology / RIKEN AIP)
|
Andong Wang |
| 11:50–12:20 |
Title: Estimating LLM Uncertainty with Evidence
Speaker: Joey Tianyi Zhou (A*STAR)
|
|
| 12:20–13:30 |
Lunch Meeting (at venue)
|
|
| 13:30–14:00 |
Title: Tensor Representation for Machine Learning: Efficiency and Reliability
Speaker: Qibin Zhao (RIKEN AIP)
|
Yuning Qiu |
| 14:00–14:30 |
Title: Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective
Speaker: Guoxu Zhou (Guangdong University of Technology)
|
|
| 14:30–15:15 |
Coffee Break & Poster Session 1
|
|
| 15:15–15:45 |
Title: Plug-and-Play of Least Squares based Tensor Decomposition Algorithms for Tensor Learning
Speaker: Tatsuya Yokota (Nagoya Institute of Technology / RIKEN AIP)
|
Zerui Tao |
| 15:45–16:15 |
Title: Towards Fine-Grained Analysis in Zero-Shot Learning
Speaker: Jingcai Guo (Hong Kong Polytechnic University)
|
|
| 16:15–17:00 |
Coffee Break & Poster Session 2
|
|
| 17:00–17:30 |
Title: Some Developments in Density Ratio Estimation for Statistical Inference
Speaker: Delu Zeng (South China University of Technology)
|
Mingyuan Bai |
| 17:30–18:00 |
Title: The importance of low-dimensional data models for machine learning with incomplete or compressed data samples
Speaker: Cesar F. Caiafa (Instituto Argentino de Radioastronomía / RIKEN AIP)
|
|
| 18:00–18:30 |
Discussion: Future Directions in Tensor Research
|
Qibin Zhao |
| 18:30–18:40 |
Group Photo
|
|
| 18:40–21:00 |
Working Dinner (at venue)
|
Poster Presentation
Poster Session 1 (14:30–15:15)
-
Poster #1Presenter: Qibin Zhao (RIKEN AIP)Title: FY2024 Team Research Achievements
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Poster #2Presenter: Yuning Qiu (RIKEN AIP)Title: Harnessing Tensor Structures: From Tensor Recovery to Generative AI
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Poster #3Presenter: Chao Li (RIKEN AIP)Title: Adaptive Structural Tensor Representation & Alignment (Astra🚀) Maximizes Tensor Network Supremacy in High-Dimensional Computation for Machine Learning, and Beyond
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Poster #4Presenter: Guang Lin (Tokyo University of Agriculture and Technology / RIKEN AIP)Title: Towards Trustworthy Machine Learning Under Adversarial Attacks
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Poster #5Presenter: Mingyuan Bai (RIKEN AIP)Title: When Large Generative Models Meet Adversarial Defense
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Poster #6Presenter: Zerui Tao (RIKEN AIP)Title: Tensor Decomposition Methods for Complex Data Structures and Learning Efficiency
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Poster #7Presenter: Shanglin Li (RIKEN AIP)Title: Source-free Unsupervised and Conditional Adaptation in EEG
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Poster #8Presenter: Andong Wang (RIKEN AIP)Title: Robust Tensor Learning via Tensor SVD
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Poster #9Presenter: Haonan Huang (RIKEN AIP)Title: Adversarial Robust Multi-view Learning
Poster Session 2 (16:15–17:00)
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Poster #10Presenter: Xin Zhang (A*STAR)Title: Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator
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Poster #11Presenter: Farzaneh Heidari (Université de Montreal / Mila / RIKEN AIP)Title: Explaining graph models using tensor network local surrogates
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Poster #12Presenter: Binghua Li (Tokyo University of Agriculture and Technology / Juntendo University / RIKEN AIP)Title: Interpretable and Robust Diagnostic Models for MRI Analysis
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Poster #13Presenter: Jian Xu (South China University of Technology / RIKEN AIP)Title: Boosting GP Models Inference with Generative Models
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Poster #14Presenter: Tatsuya Yokota (Nagoya Institute of Technology / RIKEN AIP)Title: Tensor Modeling using Hankelization for Image and Video Recovery
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Poster #15Presenter: Takanobu Furuhashi (Nagoya Institute of Technology)Title: WEEP: Resolving the Conflict Between Strong Sparsity and Differentiability
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Poster #16Presenter: Masataka Yamamoto (Nagoya Institute of Technology)Title: An Optimization Algorithm for Any Tensor Networks based on Graph Representation
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Poster #17Presenter: Namgil Lee (Kangwon National University)Title: Deep neural networks for combining heterogeneous features of peptides in DIA mass spectrometry
Organizer
Tensor Learning Team, AIP, RIKEN
Homepage: https://qibinzhao.github.io/
Venue
Open space, RIKEN AIP
Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo
How to access: https://aip.riken.jp/access/