Understanding Phase Transitions in Computational Problems
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Explores phase transitions in physics and computational problems, highlighting challenges faced by algorithms and the application of physics principles in understanding neural networks.
Discusses metastability, phase transitions, approximate message passing algorithm limitations, and the efficiency of Langevin dynamics in high-dimensional inference.
Explores the application of statistical physics in computational problems, covering topics such as Bayesian inference, mean-field spin glass models, and compressed sensing.
Introduces Scientific Machine Learning, emphasizing its application in various scientific fields and the connection between machine learning and physics.
