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Discusses metastability, phase transitions, approximate message passing algorithm limitations, and the efficiency of Langevin dynamics in high-dimensional inference.
Explores the connection between phase transitions in physics and computational problems, showcasing how insights from physics can inform algorithm design.
Explores phase transitions in physics and computational problems, highlighting challenges faced by algorithms and the application of physics principles in understanding neural networks.
