RIKEN-AIP & PRAIRIE Joint Workshop on Machine Learning and Artificial Intelligence was held on March 20 and 21, 2023, at the RIKEN-AIP-office in Tokyo. Over 35 researchers joined the workshop at the venue and about 35 researchers joined remotely and engaged in an active debate on the topic of AI and Machine learning.
We held a joint workshop with PRAIRIE as follows:
*Please check the slides used in the presentation from “Presentation Document (PDF)”!
*The event videos are partially available.
Date and Time:
March 20, 2023: 9:30 am – 16:45 pm (JST)
March 21, 2023: 9:30 am – 15:30 pm (JST)
Time Schedule
【March 20: 9:30 am – 16:45 pm (JST)】
9:30 -10:00
Speaker: Masashi Sugiyama, RIKEN AIP
Title: Introduction of RIKEN-AIP
Day1 video archive available from 00h00m06s!
10:00-10:45
Speaker: Jean Ponce, PRAIRIE
Title: Introduction of PRAIRIE/Beyond the computer vision comfort zone
Day1 video archive available from 00h07m15s!
Presentation Document PDF(13.3MB)
10:45-11:15 Break Time
11:15-11:45
Speaker: Pierre-Louis Poirion, RIKEN AIP
Title: Random subspace methods for non-convex optimization
Day1 video archive available from 01h14m04s!
Presentation Document PDF(0.5MB)
11:45-12:30
Speaker: Florian Yger, PRAIRIE
Title: Representation learning with structured data
Day1 video archive available from 01h41m35s!
Presentation Document PDF(24.1MB)
12:30-14:30 Break Time and Poster Session*
14:30-15:00
Speaker: Alexandra Wolf, RIKEN AIP
Title: Cognitive Behavioral Assistive Technology
Presentation Document PDF(6.4MB)
15:00-15:45
Speaker: Emmanuel Barillot, PRAIRIE
Title: Patient stratification in oncology: learning immunotherapy response
Presentation Document PDF(10.6MB)
15:45-16:15 Break Time
16:15-16:45
Speaker: Ichiro Takeuchi, RIKEN AIP
Title: Statistical Test for XAI
Day1 video archive available from 02h47m27s!
Presentation Document PDF(1.4MB)
【March 21: 9:30 am – 15:30 pm (JST)】
9:30 -10:00
Speaker: Masashi Sugiyama, RIKEN AIP
Title: Transfer learning
Presentation Document PDF(3.6MB)
10:00-10:45
Speaker: Stephane Caron, PRAIRIE
Title: Robots that learn world representations, why, and which ones?
Presentation Document PDF(35.2MB)
10:45-11:15 Break Time
11:15-11:45
Speaker: Taiji Suzuki, RIKEN AIP
Title: Representation power and optimization ability of neural networks
Day2 video archive available from (00h00m06sから開始)
Presentation Document PDF(4.6MB)
11:45-12:30
Speaker: Gabriel Peyre, PRAIRIE
Title: On the Training of Infinitely Deep and Wide ResNets
Day2 video archive available from (00h34m34sから開始)
Presentation Document PDF(10.5MB)
12:30-14:30 BreakTime and Poster Session*
14:30-15:00
Speaker: Qibin Zhao, RIKEN AIP
Title: Efficient machine learning with tensor networks
Day2 video archive available from (01h22m46sから開始)
Presentation Document PDF(3.4MB)
15:00-15:30
Speaker: Lin Gu, RIKEN AIP
Title: Addressing practical challenges from medical to general applications
Day2 video archive available from (01h56m09sから開始)
Presentation Document PDF(8.0MB)
*Poster Session: Only available at the RIKEN AIP center in Nihonbashi
List of posters (for both days):
| Poster presenters | Title |
|---|---|
| Jingfeng Zhang | Adversarial robustness |
| Minh Ha Quang | Information geometry and optimal transport framework for Gaussian processes |
| Yuwei Sun | Meta-Learning in Decentralized Neural Networks Towards Systematic Generalization |
| Vo Nguyen Le Duy | Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detection |
| Wei Huang | Benign Overfitting for Graph Nerual Networks |
| Thomas Moellenhoff | SAM as an Optimal Relaxation of Bayes |
| Andong Wang | Robust learning enhanced by low-dimensional strctures |
| Atsushi Nitanda | Primal and Dual Analysis of Mean-field Models |
| Ryuichiro Hataya | Gradient-based hyperparameter optimization using the Nyström method |
| Kazusato Oko | Reducing Communication in Federated Learning |
| Geoffrey Wolfer | Mixing Time Estimation in Markov Chains |
| Tomohisa Okazaki | Scientific Machine Learning for Geophysical Modeling |
| Alexandra Wolf | AI for Social Good – Dementia EEG Neurobiomarker Elucidation with Network Analysis of Time Series and Subsequent Machine Learning Model Application |
| Peter Jack Naylor & Diego Di Carlo | IMPERSONATE IMPlicit nEural RepreSentatiON chAnge deTEction |
