typeracer

I basically know of two principles for treating complicated systems in simple ways; the first is the principle of modularity and the second is the principle of abstraction. I am an apologist for computational probability in machine learning, and particularly for graphical models and variational methods, because I believe that probability theory implements these two principles in deep and intriguing ways - namely through factorization and through averaging. Exploiting these two mechanisms as fully as possible seems to me to be the way forward in machine learning.