AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work derives the optimal velocity field targeted by the empirical FM objective on sequential data and shows that the resulting sampler is an ODE whose dynamics describe a nonparametric, memory-augmented continuous-time dynamical system.
We introduce AREX, a training-free sampler for pretrained flow matching models that uses the target mean and covariance to capture an analytically tractable part of the sampling dynamics. We show that the velocity field of the moment-matched Gaussian target is the $L^2$-optimal affine approximation to the marginal velo...
Probabilistic forecasting is important for predicting complex dynamical systems because intrinsic randomness and incomplete observations can cause the same observed state to evolve into multiple plausible futures. While flow matching is a flexible approach for probabilistic forecasting, it is computationally expensive....
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