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Submit ReviewPeiyuan Liao is the youngest Chinese Kaggle grandmaster at only 20 years old with numerous gold medals and 1st, 2nd, and 3rd place finishes. He helped research two deep learning papers while in high school and now researches adversarial attacks on graph neural networks at Carnegie Mellon.
Learn more about Peiyuan:
https://liaopeiyuan.github.io/
https://www.kaggle.com/alexanderliao
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Timestamps:
03:20 How Peiyuan was first exposed to CS and ML
06:45 Researching deep learning in high school
10:30 Researching graph neural networks at Carnegie Mellon (https://arxiv.org/abs/2009.13504)
20:30 How he keeps up with the field and gets research ideas
24:05 Research tools he uses
31:30 Advice for Kaggle beginners
34:30 How Peiyuan first approaches a new Kaggle competition
40:15 His team's 3rd-place solution to the 2020 Google Landmark Recognition Challenge (https://arxiv.org/abs/2010.05350)
50:30 How he approached the Global Wheat Detection challenge (https://www.kaggle.com/c/global-wheat-detection/discussion/175961)
56:40 How he decides to quit a Kaggle competition
59:25 The difference between him and the average Kaggler
01:03:20 Contributing to open source projects
01:06:00 Rapid Fire Questions
Links:
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
Graph Adversarial Networks: Protecting Information against Adversarial Attacks
Open Neural Network Exchange (ONNX)
Deformable Convolutional Networks
Google Landmark Recognition 2020 Competition Third Place Solution
Peiyuan Liao is the youngest Chinese Kaggle grandmaster at only 20 years old with numerous gold medals and 1st, 2nd, and 3rd place finishes. He helped research two deep learning papers while in high school and now researches adversarial attacks on graph neural networks at Carnegie Mellon.
Learn more about Peiyuan:
https://liaopeiyuan.github.io/
https://www.kaggle.com/alexanderliao
Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: http://bit.ly/mle-newsletter
Comments? Questions? Submit them here: http://bit.ly/mle-survey
Follow Charlie on Twitter: https://twitter.com/CharlieYouAI
Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/
Subscribe to ML Engineered: https://mlengineered.com/listen
Timestamps:
03:20 How Peiyuan was first exposed to CS and ML
06:45 Researching deep learning in high school
10:30 Researching graph neural networks at Carnegie Mellon (https://arxiv.org/abs/2009.13504)
20:30 How he keeps up with the field and gets research ideas
24:05 Research tools he uses
31:30 Advice for Kaggle beginners
34:30 How Peiyuan first approaches a new Kaggle competition
40:15 His team's 3rd-place solution to the 2020 Google Landmark Recognition Challenge (https://arxiv.org/abs/2010.05350)
50:30 How he approached the Global Wheat Detection challenge (https://www.kaggle.com/c/global-wheat-detection/discussion/175961)
56:40 How he decides to quit a Kaggle competition
59:25 The difference between him and the average Kaggler
01:03:20 Contributing to open source projects
01:06:00 Rapid Fire Questions
Links:
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
Graph Adversarial Networks: Protecting Information against Adversarial Attacks
Open Neural Network Exchange (ONNX)
Deformable Convolutional Networks
Google Landmark Recognition 2020 Competition Third Place Solution
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
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