For any fixed β, there exists a system-size-independent external field θ such that Glauber dynamics on the SK model mixes in polynomial time with high probability.
Cambridge University Press, Cambridge (2017)
4 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
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2026 4representative citing papers
PRIOR replaces masking-based ordering pressure with predictive residual inference using level-wise predictors to produce well-ordered representations that maintain or improve performance across budgets, especially in discrete settings.
An SDP-based framework computes optimal quantum cloning maps via Choi isomorphism, certifies optimality with duality, and extracts Kraus operators for universal, phase-covariant, asymmetric, and entanglement cloning including higher-order cases.
FlexLAM trains variable-length latent actions with nested dropout to match or exceed fixed-capacity LAMs at every token budget under scarce-label supervision without new architectures or losses.
citing papers explorer
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Mixing of Glauber Dynamics on High Overlap Gibbs Measures
For any fixed β, there exists a system-size-independent external field θ such that Glauber dynamics on the SK model mixes in polynomial time with high probability.
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Semantic Allocation in Ordered Bottlenecks: Predictive Residual Inference for Visual Representation Learning
PRIOR replaces masking-based ordering pressure with predictive residual inference using level-wise predictors to produce well-ordered representations that maintain or improve performance across budgets, especially in discrete settings.
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Semidefinite Programming for Optimal Quantum Cloning: A Computational Framework
An SDP-based framework computes optimal quantum cloning maps via Choi isomorphism, certifies optimality with duality, and extracts Kraus operators for universal, phase-covariant, asymmetric, and entanglement cloning including higher-order cases.
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FlexLAM: Resolving the Bottleneck Trade-off in Latent Action Learning
FlexLAM trains variable-length latent actions with nested dropout to match or exceed fixed-capacity LAMs at every token budget under scarce-label supervision without new architectures or losses.