基本信息
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职业迁徙
个人简介
Research Interests
My current research focuses on investigating the physics of language models and AI in a broader sense. This involves designing experiments to elucidate the underlying fundamental principles governing how transformers/GPTs learn to accomplish diverse AI tasks. By probing into the neurons of the pre-trained transformers, my goal is to uncover and comprehend the intricate (and sometimes surprising!)
Before that, I work on the mathematics of deep learning. That involves developing rigorous theoretical proofs towards the learnability of neural networks, in ideal and theory-friendly settings, to explain certain mysterious phenomena observed in deep learning. In this area, our paper on ensemble / knowledge distillation received some award from ICLR'23; although I am most proud of our COLT'23 result that provably shows why deep learning is actually deep –– better than shallow learners such as layer-wise training, kernel methods, etc.
In my past life, I have also worked in machine learning, optimization theory, and theoretical computer science.
My current research focuses on investigating the physics of language models and AI in a broader sense. This involves designing experiments to elucidate the underlying fundamental principles governing how transformers/GPTs learn to accomplish diverse AI tasks. By probing into the neurons of the pre-trained transformers, my goal is to uncover and comprehend the intricate (and sometimes surprising!)
Before that, I work on the mathematics of deep learning. That involves developing rigorous theoretical proofs towards the learnability of neural networks, in ideal and theory-friendly settings, to explain certain mysterious phenomena observed in deep learning. In this area, our paper on ensemble / knowledge distillation received some award from ICLR'23; although I am most proud of our COLT'23 result that provably shows why deep learning is actually deep –– better than shallow learners such as layer-wise training, kernel methods, etc.
In my past life, I have also worked in machine learning, optimization theory, and theoretical computer science.
研究兴趣
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