AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The method is founded on the principle of iteratively constructing feature conjunctions that would significantly increase conditional log-likelihood if added to the model, and can be understood as supervised structure learning.
This work plants a controllable latent variable inside natural-looking text and arranges the 8 states themselves on a ring, in the exact order of the Markov chain, which is supporting evidence that a concept's geometry can be formed by the statistical dynamics of the latent variable behind it.
This paper formalise and study a succinct version of the compatibility problem, encoding conditional distributions as arithmetic circuits, and shows that, for succinct circuit representations of conditionals, the compatibility problem is intractable.
This work derives the generalized score matching objective on a convex subset of $\mathbb{R}^{d}$ constructively starting from Minimum Probability Flow (MPF) learning, and shows how classical score matching as well as domain-adapted variants for non-negative data arise naturally within the proposed framework.
The \emph{unit} is proposed as an explicit primitive at the level of task semantics as an explicit primitive at the level of task semantics in supervised learning.
It is shown how, given a trained VGAE and an ILP query, a conditional variational auto-encoder can be constructed dynamically that approximates the conditional ELBO without retraining on data.
VAST (Veracity-Aware Semi-Supervised Training) outperforms the strongest graph-based SSL baselines at every operating point across three datasets, with statistically significant gains in 7 of 9 comparisons, while producing a deployable inductive classifier rather than requiring transductive graph inference.
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