AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We show that continuous bitstream diffusion (CoBit), originally developed for language modeling, provides a unified framework for joint and conditional multimodal generation using the same training and sampling protocol. Across image-text, speech-text and protein sequence-structure modeling, models are trained separate...
Basis-restricted priors in Bayesian inverse problems can lose coverage when the truth has components outside the basis. We show that estimating the observation-noise variance can hide this loss. Under a linear forward model, when the in-span prior variance dominates the noise, the maximum-likelihood noise estimate abso...
Data-driven Riemannian geometry provides nonlinear interpolation and geometric representations of high-dimensional data. For these operations to be statistically meaningful, paths between observations should preferentially traverse high-likelihood regions. Existing scalable pullback constructions typically use a unimod...
Two parallel approaches to inference and uncertainty quantification over QoI are developed, one uses conditional diffusion and the other conditional autoencoders, and are compared against classical Bayesian methodology, unconditional generative models and deterministic supervised methods.
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