LeJEPA achieves linear identifiability of latent variables uniquely when the latents are Gaussian in worlds with stationary additive-noise transitions.
On measures of dependence.Acta Math
2 Pith papers cite this work. Polarity classification is still indexing.
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Spectra-Scope is a new AutoML framework that trains interpretable machine learning models on spectral data to characterize material properties while enabling users to understand which spectral features drive the predictions.
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When Does LeJEPA Learn a World Model?
LeJEPA achieves linear identifiability of latent variables uniquely when the latents are Gaussian in worlds with stationary additive-noise transitions.
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Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data
Spectra-Scope is a new AutoML framework that trains interpretable machine learning models on spectral data to characterize material properties while enabling users to understand which spectral features drive the predictions.