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Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.
Explores the Eigenstate Thermalization Hypothesis in quantum systems, emphasizing the random matrix theory and the behavior of observables in thermal equilibrium.
Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
Delves into the variational method in relativistic quantum field theory without cutoff, emphasizing weakly entangled states and the transition to relativistic continuous matrix product states.
