Research talks: Learning for interpretability
Speakers:
Hanwang Zhang, Professor, Nanyang Technological University
Yuwang Wang, Senior Researcher, Microsoft Research Asia
Shujian Yu, Professor, UiT – The Arctic University of Norway
One of the critical shortcomings of big data-driven deep learning is its black-box nature. To help resolve this, it’s important to develop architectures and algorithms that can capture the fundamentals of how humans learn and infer. Join Professor Hanwang Zhang from Nanyang Technological University in Singapore, Microsoft Senior Researcher Yuwang Wang, and Professor Shujian Yu from the Arctic University of Norway as they share their work and insights on how to achieve interpretable learning by leveraging representation disentanglement and information theory. You’ll learn about these powerful concepts and discover how they help address interpretability and generalization in deep learning.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- Track:
- Towards Human-Like Visual Learning & Reasoning
- Date:
- Speakers:
- Hanwang Zhang, Yuwang Wang, Shujian Yu
- Affiliation:
- Nanyang Technological University, Microsoft Research, UiT - The Arctic University of Norway
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Yuwang Wang
Senior Researcher
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Hanwang Zhang
Professor
Nanyang Technological University
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Shujian Yu
Professor
UiT - The Arctic University of Norway
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Towards Human-Like Visual Learning & Reasoning
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Opening remarks: Towards Human-Like Visual Learning and Reasoning
Speakers:- Wenjun Zeng,
- Wenjun Zeng
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Research talks: Learning for interpretability
Speakers:- Yuwang Wang,
- Hanwang Zhang,
- Shujian Yu
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Research talks: Few-shot and zero-shot visual learning and reasoning
Speakers:- Han Hu,
- Zhe Gan,
- Kyoung Mu Lee
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Research talks: Generalization and adaptation
Speakers:- Suha Kwak,
- Chong Luo,
- Lu Yuan
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