AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Contrastive learning is a successful paradigm for learning $d$-dimensional geometric representations from a collection of ``anchor--positive--negative''triplets $(i,j^{+},k^{-})$, indicating that ``item $i$ is closer to $j$ than to $k$.''Despite its success, understanding why contrastive learning leads to representatio...
This work develops tunable latent priors for diffusion models, normalizing flows, and variational autoencoders, leveraging nested dropout and shows empirically that tunable priors consistently achieve lower reconstruction errors than fixed-complexity baselines.
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