Explores the application of statistical physics in computational problems, covering topics such as Bayesian inference, mean-field spin glass models, and compressed sensing.
Explores phase transitions in physics and computational problems, highlighting challenges faced by algorithms and the application of physics principles in understanding neural networks.
Covers transformer architecture and subquadratic attention mechanisms, focusing on efficient approximations and their applications in machine learning.
Explores the connection between phase transitions in physics and computational problems, showcasing how insights from physics can inform algorithm design.
