PTHR equilibrates many L=16 3D spin glass samples at T>=0.2 with better size scaling than standard parallel tempering and ~64x speedup over other cluster algorithms.
org/10.1073/pnas.2534768123
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Diffusion models suffer critical slowing down when sampling near criticality in the O(n) model but deeper local architectures reduce training-time scaling from quadratic to logarithmic in system size.
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Cluster moves with an entropic reservoir accelerate low-temperature simulations of three-dimensional spin glasses
PTHR equilibrates many L=16 3D spin glass samples at T>=0.2 with better size scaling than standard parallel tempering and ~64x speedup over other cluster algorithms.
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The critical slowing down in diffusion models
Diffusion models suffer critical slowing down when sampling near criticality in the O(n) model but deeper local architectures reduce training-time scaling from quadratic to logarithmic in system size.