Proves uniform-in-time propagation of chaos for second-order CBO at Monte Carlo rate via shifted internal variables, a position-velocity Lyapunov functional, and centered-moment decay.
Propagation of Chaos: A Review of Mod- els, Methods and Applications. II. Applications
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Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
Presents an optimal transport framework for simulating particle systems with arbitrary cell shapes and volumes that automatically handles exclusion constraints.
Training a mean-field Transformer under L2 regularization induces an escape from attention-driven token clustering in later layers after initial clustering.
citing papers explorer
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Uniform-in-time propagation of chaos for Second-Order Consensus-Based Optimization
Proves uniform-in-time propagation of chaos for second-order CBO at Monte Carlo rate via shifted internal variables, a position-velocity Lyapunov functional, and centered-moment decay.
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Mean-Field Analysis of Latent Variable Process Models on Dynamically Evolving Graphs with Feedback Effects
Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
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Multicellular simulations with shape and volume constraints using optimal transport
Presents an optimal transport framework for simulating particle systems with arbitrary cell shapes and volumes that automatically handles exclusion constraints.
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Training-Induced Escape from Token Clustering in a Mean-Field Formulation of Transformers
Training a mean-field Transformer under L2 regularization induces an escape from attention-driven token clustering in later layers after initial clustering.