AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The dynamics of cells, organisms, and fluids are often modeled as probability distributions evolving over time. Reconstructing and extrapolating this evolution from unpaired snapshots requires assumptions about the underlying process. Wasserstein gradient flows are a common choice, but they cannot describe conservative...
Drifting provides a direct route to one-step generative models, but applying it directly to stochastic transition modeling requires multiple samples of the next state conditioned on the same current state. Standard trajectory data, however, typically provide only one realized next state for each observed current state...
This work introduces the class $\mathcal{P}_\psi(\mu)$ of measures that differ from a reference measure only through a finite-dimensional map $\psi$ while preserving the reference conditionals on its fibers, and develops approximation theory for fitting within it.
This work proposes Gradient-free Riemannian Langevin Sampler (GRiLS), a novel proposal that improves exploration without requiring gradient evaluations of the target density, and introduces a Riemannian metric which reshapes the local geometry in order to facilitate transitions across modes.
It is demonstrated that the stitching method achieves state-of-the-art performance across trajectory inference benchmarks, and unifies several existing methods and leads to a new particle-based method, stitching, that is simulation-free and robust to large gaps between observations.
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