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Riemannian Langevin Monte Carlo schemes for sampling PSD matrices with fixed rank
This paper introduces two explicit schemes to sample matrices from Gibbs distributions on $\mathcal S^{n, p}_+$, the manifold of real positive semi-definite (PSD) matrices of size $n\times n$ and rank $p$.
Deep Linear Networks for Matrix Completion -- An Infinite Depth Limit
The deep linear network (DLN) is a model for implicit regularization in gradient based optimization of overparametrized learning architectures.
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