AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper introduces Kinematic Interpolants (KI), a novel framework that extends the seminal methodology of stochastic interpolants to infer nonlinear dynamics across multiple snapshots in phase space, i.e., observables with position and velocity measurements.
This work proposes a new algorithm called Acceleration Matching, which consists of lifting the original interpolation problem to phase space and then regressing onto an explicit conditional acceleration field that induces random, smooth trajectories that agree with the prescribed marginals.
This work develops a generative counterpart to the theory of benign overfitting and algorithmic regularization for overparameterized neural networks in the supervised lazy-training regime by studying denoising score matching in a vector-valued reproducing kernel Hilbert space with an inner-product kernel.
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