Transformers converge pathwise to a stochastic particle system and SPDE in the scaling limit, exhibiting synchronization by noise and exponential energy dissipation when common noise is coercive relative to self-attention drift.
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DMFT for coevolving Hopfield network shows moderate plasticity stabilizes retrieval via positive delayed feedback while excessive plasticity imprints the initial cue and creates spurious attractors.
Kernel ridge regression predicts the self-energy of 1D Hubbard models from static and dynamic mean-field features, enabling Green's functions via Dyson's equation for U/t from weak to strong coupling.
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Machine Learning Green's Functions of Strongly Correlated Hubbard Models
Kernel ridge regression predicts the self-energy of 1D Hubbard models from static and dynamic mean-field features, enabling Green's functions via Dyson's equation for U/t from weak to strong coupling.