説明
[Day1]
Date:
Sep. 7 Switzerland 9:50 -14:00 / Japan 16:50 – 21:00
Program:
(Abstract)
1: click the below url: https://www.youtube.com/watch?v=bszwCCq5Qqc&t=8s
Jingfeng Zhang (RIKEN-AIP)
Title: Adversarial robustness: from basic science to applications
2: click the below: https://www.youtube.com/watch?v=wTs0sWWMkyI
Guillermo Ortiz-Jimenez (EPFL-CIS)
Title: Catastrophic overfitting is a bug but also a feature
3: click the below url: https://www.youtube.com/watch?v=bszwCCq5Qqc&t=30m38s
11:00 / 18:00 Vo Nguyen Le Duy (RIKEN-AIP)
Title: Exact Statistical Inference for the Wasserstein Distance by Selective Inference
4: click the below: https://www.youtube.com/watch?v=fIM3W2p-Zb8
Hugo Cui (EPFL-CIS)
Title: Error rates for kernel methods under source and capacity conditions
5: click the below url: https://www.youtube.com/watch?v=bszwCCq5Qqc&t=56m32s
Yu Zhe (RIKEN-AIP)
Title: Domain Generalization via Adversarially Learned Novel Domains
6: click the below: https://www.youtube.com/watch?v=t-NiQFRMFCQ
Berfin Simsek (EPFL-CIS)
Title: Neural Network Loss Landscapes: Symmetry-Induced Saddles and the Global Minima Manifold
7: click the below url: https://www.youtube.com/watch?v=bszwCCq5Qqc$t=1h28m36s
Yaxiong Liu (RIKEN-AIP)
Title: Expert advice problem with noisy low rank loss
8: click the below: https://www.youtube.com/watch?v=-X0uXaDS72c
Hadrien Hendrikx (EPFL-CIS)
Title: Beyond spectral gap: the role of the topology in decentralized learning
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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.
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 30 research teams ranging from fundamentals of machine learning and optimization, applications in medicine, materials, and disaster, to the analysis of ethics and social impact of artificial intelligence.
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