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.
Propagation of chaos: A review of models, methods and applications
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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.
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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.
- Uniform-in-time propagation of chaos for Second-Order Consensus-Based Optimization