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.
—from Graphical Models for Machine Learning and Digital Communication (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series), a movie by Brendan J. Frey
Active since May 5, 2017.
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| Universe | Races | Average WPM | First Race |
|---|---|---|---|
| Long Texts | 118 | 77.45 | May 5, 2017 |