Wristband Gaussian Loss deterministically Gaussianizes point embeddings via sphere-interval decomposition with a Lean-verified proof that the pushforward is uniform iff the source is N(0,I_d), plus efficient repulsion-energy computation and application to deterministic Gaussian autoencoders.
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2026 2verdicts
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Extends Stein's method to symmetric matrix normal distributions with a Stein characterization, semigroup solution, and Wasserstein bound for Wishart approximation.
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The Wristband Gaussian Loss: Deterministic, Composable Latents via a Sphere-Interval Decomposition
Wristband Gaussian Loss deterministically Gaussianizes point embeddings via sphere-interval decomposition with a Lean-verified proof that the pushforward is uniform iff the source is N(0,I_d), plus efficient repulsion-energy computation and application to deterministic Gaussian autoencoders.
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Stein's method for the symmetric matrix normal distribution with an application to the approximation of the Wishart law
Extends Stein's method to symmetric matrix normal distributions with a Stein characterization, semigroup solution, and Wasserstein bound for Wishart approximation.