Latent prediction SSL recovers latent trees from PCFG data with sample complexity constant in hierarchy depth L (up to logs), unlike exponential for token-level or supervised methods.
& Wyart, M
5 Pith papers cite this work. Polarity classification is still indexing.
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Higher-variance classes are learned first in diffusion models; strong class imbalance reverses the order and imposes distinct delayed learning times on minority classes.
Linear generative models memorize at small data loads but converge continuously once samples scale linearly with dimension; this convergence is insensitive to sharp recovery of principal latent factors.
diffGHOST is a conditional diffusion model that segments learned latent space to identify and mitigate memorization of critical trajectory samples, aiming to deliver privacy guarantees alongside data utility.
Diffusion models require new generalization frameworks because memorization and novel generation are incompatible, so research should focus on what models learn before memorization begins.
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diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories
diffGHOST is a conditional diffusion model that segments learned latent space to identify and mitigate memorization of critical trajectory samples, aiming to deliver privacy guarantees alongside data utility.