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.
| Game | Time | WPM | Accuracy |
|---|---|---|---|
| 176 | 2022-06-16 05:43:02 | 63.21 | 96% |