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Found 3 papers, 1 papers with code
From Zero to Hero: How local curvature at artless initial conditions leads away from bad minima
Through both theoretical analysis and numerical experiments, we show that in practical cases, i. e. for finite but even very large $N$, successful optimization via gradient descent in phase retrieval is achieved by falling towards the good minima before reaching the bad ones.
Dynamical Regimes of Diffusion Models
Using statistical physics methods, we study generative diffusion models in the regime where the dimension of space and the number of data are large, and the score function has been trained optimally.
Regularization of Mixture Models for Robust Principal Graph Learning
A regularized version of Mixture Models is proposed to learn a principal graph from a distribution of $D$-dimensional data points.
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