Liu Ziyin 刘子寅

I am a graduate student in physics at the University of Tokyo. I study and do research in, technically speaking, theoretical physics in the Ueda Group. Personally, I am interested in art, literature and philosophy. I also play Go. Even if I do my best, I speak poor English, awful Japanese, and even worse Shanghainese. If you have questions or want to collaborate, or just want to say hi, please shoot an email.

Doctor thesis: Symmetry breaking in deep learning (深層学習に於ける対称性の破れ, 2023).
Master thesis: Mean-field learning dynamics of deep neural networks (2020).

Research Interest

• phase transition and symmetry breaking in neural network
• analytically solvable models of deep learning
• design of principled and simple algorithms of learning

Past Research

• loss landscape of neural networks (asymptotics, landscape, etc.):
  
• symmetry and loss landscape of self-supervised learning
• posterior collapse in Bayesian deep learning
• landscape of deep linear nets
• prediction variance of stochastic neural networks
• interpolation and extrapolation properties of neural networks
• phase transitions in deep learning
• dynamics of stochastic gradient descent:
  
• understand different regimes of learning with the probabilistic stability
• fluctuation of stochastic gradient descent
• the nature of the minibatch noise
• power-law landscape and escaping from local minimum
• escaping from a saddle point