Machine Learning Initiative

Collection Number S029
The adoption of machine learning (ML], into theoretical physics comes on the heels of an explosion of industry progress that started in 2012. Since that time, computer scientists have demonstrated that learning algorithms - those designed to respond and adapt to new data - provide an exceptionally powerful platform for tackling many difficult tasks in image recognition, natural language comprehension, game play and more. This new breed of ML algorithm has now conquered benchmarks previously thought to be decades away due to their high mathematical complexity. In the last several years, researchers at Perimeter have begun to examine machine learning algorithms for application to a new set of problems, including condensed matter, quantum information, numerical relativity, quantum gravity and astrophysics.

Tensor Network

Collection Number S028
This series consists of occasional seminars on Tensor Networks, ranging from algorithms to their application in condensed matter, quantum gravity, or high energy physics. Each seminar starts with a gentle introduction to the subject under discussion. Everyone is strongly encouraged to participate with questions and comments.