Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
Latent space oddity: on the curvature of deep generative models
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6verdicts
UNVERDICTED 6representative citing papers
Unsupervised manifold learning on ICSD data reveals a low-dimensional embedding that segregates superconductors and predicts critical temperatures across families.
LAST linearizes action manifolds with Lie-algebraic mapping and discretizes them into approximately isotropic charts to align with VL semantic geometry via Gromov-Wasserstein distance.
PACE recovers geometry-consistent continuous transport dynamics from destructive single-cell snapshots via anisotropic metrics and neural bridges, reducing reconstruction distances by 23.7% on average across seven datasets.
Shell-LCC models the high-quality data manifold as an isotropic shell to derive cost-free reward signals that improve realism and high-frequency details in text-to-video generation.
X-VAE uses empirical statistics from a pretrained autoencoder to set a data-adaptive Gaussian prior and introduces a latent scaling factor for controllable generation.
citing papers explorer
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Charting the emergent low-dimensional manifold of quantum materials
Unsupervised manifold learning on ICSD data reveals a low-dimensional embedding that segregates superconductors and predicts critical temperatures across families.