Langevin sampling on the modern Hopfield energy produces training-free stochastic attention that transitions from exact retrieval to generation as temperature rises, with an entropy inflection condition marking the shift.
Generative modeling by estimating gradients of the data distribution
2 Pith papers cite this work. Polarity classification is still indexing.
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SSTS extracts causal topological orders from score functions via Schur complements of the Score-Jacobian Information Matrix, bypassing acyclicity optimization.
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Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy
Langevin sampling on the modern Hopfield energy produces training-free stochastic attention that transitions from exact retrieval to generation as temperature rises, with an entropy inflection condition marking the shift.
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Optimization-Free Topological Sort for Causal Discovery via the Schur Complement of Score Jacobians
SSTS extracts causal topological orders from score functions via Schur complements of the Score-Jacobian Information Matrix, bypassing acyclicity optimization.