Since I switched to the field of machine learning, I have been interested in its theoretical foundations—doing a lot of pure mathematics in before my Ph.D. (and a tiny bit during the Ph.D. as well) left its mark apparently. I noticed two things relatively... (more…)

This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, undirected graphical models, expressive power of graphical models, factor gra... (more…)

Posted by Patrick Riley, Principal Engineer, Accelerated Science Team, Google Research Much of the development of therapeutics for human... (more…)

A guide to the widely used Optimizer functions, and a breakdown of their benefits and limitations... (more…)

This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques. Despite the great success of modern SAT solvers to solve large industrial instan... (more…)