The document summarizes various research topics presented at the ICLR 2020 virtual conference, including virtual contrastive learning, neural architecture search, graph neural networks, and federated adversarial domain adaptation. Each entry links to corresponding research papers that explore these areas such as disentangled representations and the use of graph information. The focus is on advancements in machine learning and representation learning techniques.

1. Yamato OKAMOTO
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ICLR’2020

2. COVID-19 Virtual Conference
• MTG
• MTG
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MTG

3. Virtual Conference
• 100$
•
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• WiFi
•
•
•
memo
• Disentangle
• !?
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Virtual

5. Contrastive Learning of Structured World Models
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https://openreview.net/pdf?id=H1gax6VtDB
• State Encoder Action Transition
• State+Action State Encoder
Reconstruction State
•

6. Doing for Our Robots What Nature Did For Us
Prof. Leslie Kaelbling / MIT
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https://iclr.cc/virtual/speaker_2.html#stream

7. Evaluating The Search Phase of Neural Architecture Search
• NAS
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Policy
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(i) a constrained search space
(ii) weight sharing, which shuffles the architecture ranking during the search, thus negatively impacting it.
https://openreview.net/pdf?id=H1loF2NFwr

8. Measuring and Improving the Use of Graph Information
in Graph Neural Networks
• Graph-CNN Graph-Data
• Graph-Data
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• GNN
• Information-Gain=0 KL
https://openreview.net/pdf?id=rkeIIkHKvS

9. Bounds on Over-Parameterization for Guaranteed Existence of
Descent Paths in Shallow ReLU Networks
• Over-Parameter Over-Parameter
• ReLU
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https://openreview.net/pdf?id=BkgXHTNtvS

10. Federated Adversarial Domain Adaptation
• Federated-Learning Source-Data
Target-Data Unsupervised-Domain-Adaptation
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• aggregation
https://arxiv.org/pdf/1911.02054.pdf

11. Demystifying Inter-Class Disentanglement
• Disentangle
• Encoder Class Content
• Encoder
• VAE
https://openreview.net/pdf?id=Hyl9xxHYPr

12. Theory and Evaluation Metrics for Learning Disentangled
Representations
• Semantic Feature Disentangle Disentangle
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• Disentangle
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https://arxiv.org/pdf/1908.09961.pdf