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Qiyao Wei

My website is https://qiyaowei.github.io/website/ Reach me at [email protected]

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Hi there! You found my webpage! This is Qiyao (chi-yao), a University of Toronto undergrad inspired by the advances and accomplishments of Artificial Intelligence. My short-term research interest is to make AI more about theory and justification, rather than network design and hyperparameter tuning. My long-term goal is to crack the code of human intelligence, or to achieve human-level intelligence in our machines.

Publications and Presentations

  • Here is a technical report on mirror descent, Bregman Divergences, and an analysis on Krichene and Bartlett (2017). Here is a presentation I gave to Professor Yinyu Ye at Stanford on this paper.
  • Epidemic Control with Reinforcement Learning, only single-node for now. Here is a presentation I gave to our research group at Vector.

Favorite Readings

This is where I put some of the articles or papers I read, and will recommend to others. Specifically, they will either convey some inspiration that I could not have summarized better, or illustrated a theory very clearly

Favorite quotes

This is where I record some of the best (i.e. pithy but elucidating) words I came across. For a total stranger, they will require context to make sense. I always welcome discussions of stuff like this.

  • "Why does the wind blow?" "Because the leaves move" ---Yann LeCun interview with Lex Fridman
  • E.T.Jaynes page 103 "how we solve a problem when it becomes too difficult"
  • Von Neumann "You should call it entropy, for two reasons. In the first place your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, nobody knows what entropy really is, so in a debate you will always have the advantage"

Miscellaneous

There are also beautiful words that are not AI related.

  • The Great Gatsby and his "extraordinary gift of hope"
  • Top secret

What if I am just getting started?

  • Here is a nice introductory paper about MLPs and RNNs
  • Here is my favorite deep reinforcement learning course. A word of caution about videos---come to them with questions and learning will go smoothly, if watched without active learning then not much can be recalled
  • Here is a great introductory post on NTK, coming from one of my favorites blogs

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